survival/0000755000176200001440000000000014111165372012126 5ustar liggesuserssurvival/NAMESPACE0000644000176200001440000001604614005770434013357 0ustar liggesusersuseDynLib(survival, .registration = TRUE) importFrom(splines, spline.des, ns) importFrom(Matrix, expm) importFrom(stats, aggregate, anova, approx, as.formula, coef, coefficients, contr.treatment, cor, density, delete.response, extractAIC, formula, family, fitted, lm, logLik, makepredictcall, median, model.extract, model.frame, model.matrix, model.offset, model.response, make.link, model.weights, na.fail, na.pass, napredict, naprint, naresid, nobs, pchisq, pnorm, printCoefmat, predict, qnorm, quantile, qgamma, reformulate, setNames, terms.formula, resid, residuals, rnorm, runif, stat.anova, terms, weights, var, vcov, .checkMFClasses, .getXlevels, update, update.formula) importFrom(graphics, arrows, axis, barplot, box, clip, matlines, matplot, hist, frame, identify, image, par, plot, points, lines, segments, strheight, strwidth, text) importFrom(Matrix, expm) importFrom(methods, as) importFrom(utils, getS3method, type.convert, methods, head, tail) export(Surv, Surv2, Surv2data, aeqSurv, aareg, agreg.fit, agexact.fit, attrassign, blogit, bprobit, bcloglog, blog, basehaz, cch, clogit, cipoisson, cluster, concordance, concordancefit, coxph, cox.zph, coxph.control, coxph.detail, coxph.fit, coxph.wtest, finegray, format.Surv, frailty, frailty.gamma, frailty.gaussian, frailty.t, is.Surv, is.na.Surv, is.ratetable, nsk, labels.survreg, match.ratetable, neardate, psurvreg, qsurvreg, dsurvreg, pseudo, pspline, pyears, ratetableDate, ratetable, ridge, royston, rsurvreg, rttright,statefig, strata, survSplit, survcheck, survdiff, survexp, survfit, survfit0, survfit.formula, coxsurv.fit, survfitKM, survfitCI, survobrien, survpenal.fit, survreg, survreg.control, survreg.fit, survreg.distributions, survregDtest, tcut, tmerge, untangle.specials, yates, yates_setup, survConcordance, survConcordance.fit, survfitcoxph.fit) S3method('[', aareg) S3method('[', Surv) S3method('[', Surv2) S3method('[', cox.zph) S3method('[', coxph.penalty) S3method('[', ratetable) S3method('[', survfit) S3method('[', survfitms) S3method('[', tcut) S3method('[', tmerge) S3method(Math, Surv) S3method(Math, Surv2) S3method(Math, ratetable) S3method(Ops, Surv) S3method(Ops, Surv2) S3method(Ops, ratetable) S3method(Summary, Surv) S3method(Summary, Surv2) S3method(anyDuplicated, Surv) S3method(anyDuplicated, Surv2) S3method(anova, coxph) S3method(anova, coxphlist) S3method(anova, survreg) S3method(anova, survreglist) S3method(aggregate, survfit) S3method(as.character, Surv) S3method(as.character, Surv2) S3method(as.data.frame, Surv) S3method(as.data.frame, Surv2) S3method(as.integer, Surv) S3method(as.integer, Surv2) S3method(as.logical, Surv) S3method(as.logical, Surv2) S3method(as.matrix, ratetable) S3method(as.matrix, Surv) S3method(as.matrix, Surv2) S3method(as.numeric, Surv) S3method(as.numeric, Surv2) S3method(attrassign, default) S3method(attrassign, lm) S3method(barplot, Surv) S3method(c, Surv) S3method(c, Surv2) S3method(coef, concordance) S3method(coef, coxphms) S3method(concordance, formula) S3method(concordance, lm) S3method(concordance, coxph) S3method(concordance, survreg) S3method(density, Surv) S3method(dim, survfit) S3method(duplicated, Surv) S3method(duplicated, Surv2) S3method(extractAIC,coxph.penal) S3method(fitted, coxph) S3method(fitted, survreg) S3method(format, Surv) S3method(format, Surv2) S3method(head, Surv) S3method(hist, Surv) S3method(hist, Surv2) S3method(identify, Surv) S3method(identify, Surv2) S3method(image, Surv) S3method(image, Surv2) S3method(is.na, Surv) S3method(is.na, Surv2) S3method(is.na, coxph.penalty) S3method(is.na, ratetable) S3method(labels, survreg) S3method(levels, Surv) S3method(levels, tcut) S3method(lines, aareg) S3method(lines, Surv) S3method(lines, Surv2) S3method(lines, survexp) S3method(lines, survfit) S3method(logLik, coxph) S3method(logLik, coxph.null) S3method(logLik, survreg) S3method(makepredictcall, pspline) S3method(makepredictcall, nsk) S3method(median, Surv) S3method(model.frame, coxph) S3method(model.frame, survreg) S3method(model.matrix, coxph) S3method(model.matrix, survreg) S3method(nobs, coxph) S3method(nobs, survreg) S3method(pairs, Surv) S3method(pairs, Surv2) S3method(plot, aareg) S3method(plot, cox.zph) S3method(plot, Surv) S3method(plot, survfit) S3method(points, Surv) S3method(points, Surv2) S3method(points, survfit) S3method(predict, coxph) S3method(predict, coxphms) S3method(predict, coxph.penal) S3method(predict, pspline) S3method(predict, survreg) S3method(predict, survreg.penal) S3method(print, Surv) S3method(print, Surv2) S3method(print, aareg) S3method(print, cch) S3method(print, clogit) S3method(print, concordance) S3method(print, cox.zph) S3method(print, coxph) S3method(print, coxph.null) S3method(print, coxph.penal) S3method(print, pyears) S3method(print, ratetable) S3method(print, summary.cch) S3method(print, summary.coxph) S3method(print, summary.coxph.penal) S3method(print, summary.survexp) S3method(print, summary.survfit) S3method(print, summary.survfitms) S3method(print, summary.survreg) S3method(print, survdiff) S3method(print, survcheck) S3method(print, survexp) S3method(print, survfit) S3method(print, survfitms) S3method(print, survreg) S3method(print, survreg.penal) S3method(print, yates) S3method(quantile, survfit) S3method(quantile, survfitms) S3method(ratetableDate, POSIXt) S3method(ratetableDate, Date) S3method(ratetableDate, date) S3method(ratetableDate, chron) S3method(ratetableDate, default) S3method(rep, Surv) S3method(rep, Surv2) S3method(rep.int, Surv) S3method(rep.int, Surv2) S3method(rep_len, Surv) S3method(rep_len, Surv2) S3method(residuals, coxph) S3method(residuals, coxphms) S3method(residuals, coxph.null) S3method(residuals, coxph.penal) S3method(residuals, survfit) S3method(residuals, survreg) S3method(residuals, survreg.penal) S3method(rev, Surv) S3method(rev, Surv2) S3method(summary, aareg) S3method(summary, cch) S3method(summary, coxph) S3method(summary, coxph.penal) S3method(summary, ratetable) S3method(summary, pyears) S3method(summary, survexp) S3method(summary, survfit) S3method(summary, survfitms) S3method(summary, survreg) S3method(summary, tmerge) S3method(survfit, coxph) S3method(survfit, coxphms) S3method(survfit, formula) S3method(survfit, matrix) S3method(survfit, Surv) S3method(t, Surv) S3method(t, Surv2) S3method(tail, Surv) S3method(tail, Surv2) S3method(text, Surv) S3method(text, Surv2) S3method(unique, Surv) S3method(vcov, cch) S3method(vcov, concordance) S3method(vcov, coxph) S3method(vcov, survreg) S3method(weights, coxph) S3method(weights, survreg) S3method(yates_setup, default) S3method(yates_setup, glm) S3method(yates_setup, coxph) S3method('[', ratetable2) S3method(is.na,ratetable2) # xtfrm.Surv was moved out of base into survival at one point if(getRversion() >= "3.6.0") S3method(xtfrm, Surv) # Make Surv objects have their logical length. S3method(length, Surv) S3method(names, Surv) S3method("names<-", Surv) survival/noweb/0000755000176200001440000000000014110732403013232 5ustar liggesuserssurvival/noweb/noweb.sty0000644000176200001440000000240513570772300015117 0ustar liggesusers% % The format files for the noweave package % It looks like a short form of Sweave.sty % \NeedsTeXFormat{LaTeX2e} \RequirePackage{graphicx,fancyvrb,hyperref} %forward, backward, and both hyperlinks % nwhypf{my label}{text to put here}{label of forward link} % nwhypb{my label}{text to put here}{label of backwards link} % nwhyp {my label}{text to put here}{label of backwards link}{forward link label} \newcommand{\nwhypf}[3]{\hypertarget{#1}{$\langle$\textit{#2}}\hyperlink{#3}{$\rangle$}} \newcommand{\nwhypb}[3]{\hyperlink{#3}{$\langle$}\hypertarget{#1}{\textit{#2}$\rangle$}} \newcommand{\nwhyp}[4]{\hyperlink{#3}{$\langle$}\hypertarget{#1}{\textit{#2}}\hyperlink{#4}{$\rangle$}} % no hyperlink code reference \newcommand{\nwhypn}[1]{$\langle$\textit{#1}$\rangle$} % dummy out noweboptions, in case someone used them (they are part % of Ramsay's standalone version \newcommand{\noweboptions}[1]{} % The standard font for ~ used in formulas is ugly, redefine it % to the math mode symbol by making use of an active charcter trick % Leave the \, {, and } characters active %\newcommand{\twiddle}{\ensuremath{\sim}} \newcommand{\twiddle}{\textasciitilde} \DefineVerbatimEnvironment{nwchunk}{Verbatim}{commandchars=\\\{\},% codes={\catcode`~=\active},defineactive=\def~{\twiddle}} survival/noweb/code.toc0000644000176200001440000000643614110720444014666 0ustar liggesusers\contentsline {section}{\numberline {1}Introduction}{2}{section.1}% \contentsline {section}{\numberline {2}Cox Models}{3}{section.2}% \contentsline {subsection}{\numberline {2.1}Coxph}{3}{subsection.2.1}% \contentsline {subsection}{\numberline {2.2}Exact partial likelihood}{29}{subsection.2.2}% \contentsline {subsection}{\numberline {2.3}Andersen-Gill fits}{39}{subsection.2.3}% \contentsline {subsection}{\numberline {2.4}Predicted survival}{58}{subsection.2.4}% \contentsline {subsubsection}{\numberline {2.4.1}Multi-state models}{81}{subsubsection.2.4.1}% \contentsline {section}{\numberline {3}The Fine-Gray model}{88}{section.3}% \contentsline {subsection}{\numberline {3.1}The predict method}{93}{subsection.3.1}% \contentsline {section}{\numberline {4}Concordance}{104}{section.4}% \contentsline {subsection}{\numberline {4.1}Main routine}{104}{subsection.4.1}% \contentsline {subsection}{\numberline {4.2}Methods}{113}{subsection.4.2}% \contentsline {section}{\numberline {5}Expected Survival}{135}{section.5}% \contentsline {subsection}{\numberline {5.1}Parsing the covariates list}{144}{subsection.5.1}% \contentsline {section}{\numberline {6}Person years}{155}{section.6}% \contentsline {subsection}{\numberline {6.1}Print and summary}{163}{subsection.6.1}% \contentsline {section}{\numberline {7}Residuals for survival curves}{173}{section.7}% \contentsline {subsection}{\numberline {7.1}R-code}{173}{subsection.7.1}% \contentsline {section}{\numberline {8}Residuals for survival curves}{173}{section.8}% \contentsline {subsection}{\numberline {8.1}R-code}{173}{subsection.8.1}% \contentsline {subsection}{\numberline {8.2}Simple survival}{177}{subsection.8.2}% \contentsline {paragraph}{Fleming-Harrington}{182}{section*.2}% \contentsline {paragraph}{Kaplan-Meier}{183}{section*.3}% \contentsline {subsection}{\numberline {8.3}Multi-state Aalen-Johansen estimate}{184}{subsection.8.3}% \contentsline {paragraph}{Nelson-Aalen}{187}{section*.4}% \contentsline {paragraph}{Aalen-Johansen}{188}{section*.5}% \contentsline {section}{\numberline {9}Accelerated Failure Time models}{195}{section.9}% \contentsline {subsection}{\numberline {9.1}Residuals}{195}{subsection.9.1}% \contentsline {section}{\numberline {10}Survival curves}{202}{section.10}% \contentsline {subsection}{\numberline {10.1}Kaplan-Meier}{207}{subsection.10.1}% \contentsline {subsection}{\numberline {10.2}Kaplan-Meier}{212}{subsection.10.2}% \contentsline {subsection}{\numberline {10.3}Competing risks}{235}{subsection.10.3}% \contentsline {subsubsection}{\numberline {10.3.1}C-code}{246}{subsubsection.10.3.1}% \contentsline {subsubsection}{\numberline {10.3.2}Printing and plotting}{254}{subsubsection.10.3.2}% \contentsline {section}{\numberline {11}Matrix exponentials and transition matrices}{270}{section.11}% \contentsline {subsection}{\numberline {11.1}Decompostion}{272}{subsection.11.1}% \contentsline {subsection}{\numberline {11.2}Derivatives}{276}{subsection.11.2}% \contentsline {section}{\numberline {12}Plotting survival curves}{278}{section.12}% \contentsline {section}{\numberline {13}State space figures}{291}{section.13}% \contentsline {section}{\numberline {14}tmerge}{297}{section.14}% \contentsline {section}{\numberline {15}Linear models and contrasts}{312}{section.15}% \contentsline {section}{\numberline {16}The cox.zph function}{334}{section.16}% survival/noweb/pyears2.Rnw0000644000176200001440000004310013537676563015337 0ustar liggesusers\subsection{Print and summary} The print function for pyear gives a very abbreviated printout: just a few lines. It works with pyears objects with or without a data component. <>= print.pyears <- function(x, ...) { if (!is.null(cl<- x$call)) { cat("Call:\n") dput(cl) cat("\n") } if (is.null(x$data)) { if (!is.null(x$event)) cat("Total number of events:", format(sum(x$event)), "\n") cat ( "Total number of person-years tabulated:", format(sum(x$pyears)), "\nTotal number of person-years off table:", format(x$offtable), "\n") } else { if (!is.null(x$data$event)) cat("Total number of events:", format(sum(x$data$event)), "\n") cat ( "Total number of person-years tabulated:", format(sum(x$data$pyears)), "\nTotal number of person-years off table:", format(x$offtable), "\n") } if (!is.null(x$summary)) { cat("Matches to the chosen rate table:\n ", x$summary) } cat("Observations in the data set:", x$observations, "\n") if (!is.null(x$na.action)) cat(" (", naprint(x$na.action), ")\n", sep='') cat("\n") invisible(x) } @ The summary function attempts to create output that looks like a pandoc table, which in turn makes it mesh nicely with Rstudio. Pandoc has 4 types of tables: with and without vertical bars and with single or multiple rows per cell. If the pyears object has only a single dimension then our output will be a simple table with a row or column for each of the output types (see the vertical argument). The result will be a simple table or a ``pipe'' table depending on the vline argument. For two or more dimensions the output follows the usual R strategy for printing an array, but with each ``cell'' containing all of the summaries for that combination of predictors, thus giving either a ``multiline'' or ``grid'' table. The default values of no vertical lines makes the tables appropriate for non-pandoc output such as a terminal session. <>= summary.pyears <- function(object, header=TRUE, call=header, n= TRUE, event=TRUE, pyears=TRUE, expected = TRUE, rate = FALSE, rr = expected, ci.r = FALSE, ci.rr = FALSE, totals=FALSE, legend=TRUE, vline = FALSE, vertical = TRUE, nastring=".", conf.level=0.95, scale= 1, ...) { # Usual checks if (!inherits(object, "pyears")) stop("input must be a pyears object") temp <- c(is.logical(header), is.logical(call), is.logical(n), is.logical(event) , is.logical(pyears), is.logical(expected), is.logical(rate), is.logical(ci.r), is.logical(rr), is.logical(ci.rr), is.logical(vline), is.logical(vertical), is.logical(legend), is.logical(totals)) tname <- c("header", "call", "n", "event", "pyears", "expected", "rate", "ci.r", "rr", "ci.rr", "vline", "vertical", "legend", "totals") if (any(!temp) || length(temp) != 14 || any(is.na(temp))) { stop("the ", paste(tname[!temp], collapse=", "), "argument(s) must be single logical values") } if (!is.numeric(conf.level) || conf.level <=0 || conf.level >=1 | length(conf.level) > 1 || is.na(conf.level) > 1) stop("conf.level must be a single numeric between 0 and 1") if (is.na(scale) || !is.numeric(scale) || length(scale) !=1 || scale <=0) stop("scale must be a value > 0") vname <- attr(terms(object), "term.labels") #variable names if (!is.null(object$data)) { # Extra work: restore the tables which had been unpacked into a df # All of the categories are factors in this case tdata <- object$data[vname] # the conditioning variables dname <- lapply(tdata, function(x) { if (is.factor(x)) levels(x) else sort(unique(x))}) # dimnames dd <- sapply(dname, length) # dim of arrays index <- tapply(tdata[,1], tdata) restore <- c('n', 'event', 'pyears', 'expected') #do these, if present restore <- restore[restore %in% names(object$data)] new <- lapply(object$data[restore], function(x) { temp <- array(0L, dim=dd, dimnames=dname) temp[index] <- x temp} ) object <- c(object, new) } if (is.null(object$expected)) { expected <- FALSE rr <- FALSE ci.rr <- FALSE } if (is.null(object$event)) { event <- FALSE rate <- FALSE ci.r <- FALSE rr <- FALSE ci.rr <- FALSE } # print out the front matter if (call && !is.null(object$call)) { cat("Call: ") dput(object$call) cat("\n") } if (header) { cat("number of observations =", object$observations) if (length(object$omit)) cat(" (", naprint(object$omit), ")\n", sep="") else cat("\n") if (object$offtable > 0) cat(" Total time lost (off table)", format(object$offtable), "\n") cat("\n") } # Add in totals if requested if (totals) { # if the pyear object was based on any time dependent cuts, then # the "n" component cannot be totaled up. tcut <- if (is.null(object$tcut)) TRUE else object$tcut object$n <- pytot(object$n, na=tcut) object$pyears <- pytot(object$pyears) if (event) object$event <- pytot(object$event) if (expected) object$expected <- pytot(object$expected) } dd <- dim(object$n) vname <- attr(terms(object), "term.labels") #variable names <> if (length(dd) ==1) { # 1 dimensional table <> } else { # more than 1 dimension <> } invisible(object) } <> @ <>= # Put the elements to be printed onto a list pname <- (tname[3:6])[c(n, event, pyears, expected)] plist <- object[pname] if (rate) { pname <- c(pname, "rate") plist$r <- scale* object$event/object$pyears } if (ci.r) { pname <- c(pname, "ci.r") plist$ci.r <- cipoisson(object$event, object$pyears, p=conf.level) *scale } if (rr) { pname <- c(pname, "rr") plist$rr <- object$event/object$expected } if (ci.rr) { pname <- c(pname, "ci.rr") plist$ci.rr <- cipoisson(object$event, object$expected, p=conf.level) } rname <- c(n = "N", event="Events", pyears= "Time", expected= "Expected events", rate = "Event rate", ci.r = "CI (rate)", rr= "Obs/Exp", ci.rr= "CI (O/E)") rname <- rname[pname] @ If there is only one dimension to the table we can forgo the top legend and use the object names as one of the margins. If \code{vertical=TRUE} the output types are vertical, otherwise they are horizontal. Format each element of the output separately. <>= cname <- names(object$n) #category names if (vertical) { # The person-years objects list across the top, categories up and down # This makes columns line up in a standard "R" way # The first column label is the variable name, content is the categories plist <- lapply(plist, pformat, nastring, ...) # make it character pcol <- sapply(plist, function(x) nchar(x[1])) #width of each one colwidth <- pmax(pcol, nchar(rname)) +2 for (i in 1:length(plist)) plist[[i]] <- strpad(plist[[i]], colwidth[i]) colwidth <- c(max(nchar(vname), nchar(cname)) +2, colwidth) leftcol <- list(strpad(cname, colwidth[1])) header <- strpad(c(vname, rname), colwidth) } else { # in this case each column will have different types of objects in it # alignment is the nuisance newmat <- pybox(plist, length(plist[[1]]), nastring, ...) colwidth <- pmax(nchar(cname), apply(nchar(newmat), 1, max)) +2 # turn the list sideways plist <- split(newmat, row(newmat)) for (i in 1:length(plist)) plist[[i]] <- strpad(plist[[i]], colwidth[i]) colwidth <- c(max(nchar(vname), nchar(rname)) +2, colwidth) leftcol <- list(strpad(rname, colwidth[1])) header <- strpad(c(vname, cname), colwidth) } # Now print it if (vline) { # use a pipe table cat(paste(header, collapse = "|"), "\n") cat(paste(strpad("-", colwidth, "-"), collapse="|"), "\n") temp <- do.call("paste", c(leftcol, plist, list(sep ="|"))) cat(temp, sep= '\n') } else { cat(paste(header, collapse = " "), "\n") cat(paste(strpad("-", colwidth, "-"), collapse=" "), "\n") temp <- do.call("paste", c(leftcol, plist, list(sep =" "))) cat(temp, sep='\n') } @ When there are more than one category in the pyears object then we use a special layout. Each 'cell' of the printed table has all of the values in it. <>= if (header) { # the header is itself a table width <- max(nchar(rname)) if (vline) { cat('+', strpad('-', width, '-'), "+\n", sep="") cat(paste0('|',strpad(rname, width), '|'), sep='\n') cat('+', strpad('-', width, '-'), "+\n\n", sep="") } else { cat(strpad('-', width, '-'), "\n") cat(strpad(rname, width), sep='\n') cat(strpad('-', width, '-'), "\n\n") } } tname <- vname[1:2] #names for the row and col rowname <- dimnames(object$n)[[1]] colname <- dimnames(object$n)[[2]] if (length(dd) > 2) newmat <- pybox(plist, c(dd[1],dd[2], prod(dd[-(1:2)])), nastring, ...) else newmat <- pybox(plist, dd, nastring, ...) if (length(dd) > 2) { newmat <- pybox(plist, c(dd[1],dd[2], prod(dd[-(1:2)])), nastring, ...) outer.label <- do.call("expand.grid", dimnames(object$n)[-(1:2)]) temp <- names(outer.label) for (i in 1:nrow(outer.label)) { # first the caption, then data cat(paste(":", paste(temp, outer.label[i,], sep="=")), '\n') pyshow(newmat[,,i,], tname, rowname, colname, vline) } } else { newmat <- pybox(plist, dd, nastring, ...) pyshow(newmat, tname, rowname, colname, vline) } @ Here are some character manipulation functions. The stringi package has more elegant versions of the pad function, but we don't need the speed. No one is going to print out thousands of lines. <>= strpad <- function(x, width, pad=' ') { # x = the string(s) to be padded out # width = width of desired string. nc <- nchar(x) added <- width - nc left <- pmax(0, floor(added/2)) # can't add negative space right <- pmax(0, width - (nc + left)) # right will be >= left if (all(right <=0)) { if (length(x) >= length(width)) x # nothing needs to be done else rep(x, length=length(width)) } else { # Each pad could be a different length. # Make a long string from which we can take a portion longpad <- paste(rep(pad, max(right)), collapse='') paste0(substring(longpad, 1, left), x, substring(longpad,1, right)) } } pformat <- function(x, nastring, ...) { # This is only called for single index tables, in vertical mode # Any matrix will be a confidence interval if (is.matrix(x)) ret <- paste(ifelse(is.na(x[,1]), nastring, format(x[,1], ...)), "-", ifelse(is.na(x[,2]), nastring, format(x[,2], ...))) else ret <- ifelse(is.na(x), nastring, format(x, ...)) } @ Create formatted boxes. We want all the decimal points to line up, so the format calls are in 3 parts: integer, real, and confidence interval. If there are confidence intervals, format their values and then paste together the left-right ends. The intermediag form \code{final} is a matrix with one column per statistic. At the end, reformat it as an array whose last dimension is the components. <>= pybox <- function(plist, dd, nastring, ...) { ci <- (substring(names(plist), 1,3) == "ci.") # the CI components int <- sapply(plist, function(x) all(x == floor(x) | is.na(x))) int <- (!ci & int) real<- (!ci & !int) nc <- prod(dd) final <- matrix("", nrow=nc, ncol=length(ci)) if (any(int)) { # integers if (any(sapply(plist[int], length) != nc)) stop("programming length error, notify package author") temp <- unlist(plist[int]) final[,int] <- ifelse(is.na(temp), nastring, format(temp)) } if (any(real)) { # floating point if (any(sapply(plist[real], length) != nc)) stop("programming length error, notify package author") temp <- unlist(plist[real]) final[,real] <- ifelse(is.na(temp), nastring, format(temp, ...)) } if (any(ci)) { if (any(sapply(plist[ci], length) != nc*2)) stop("programming length error, notify package author") temp <- unlist(plist[ci]) temp <- array(ifelse(is.na(temp), nastring, format(temp, ...)), dim=c(nc, 2, sum(ci))) final[,ci] <- paste(temp[,1,], temp[,2,], sep='-') } array(final, dim=c(dd, length(ci))) } @ This function prints out a box table. Each cell contains the full set of statistics that were requested. Most of the work is the creation of the appropriate spacing and special characters to create a valid pandoc table. <>= pyshow <- function(dmat, labels, rowname, colname, vline) { # Every column is the same width, except the first colwidth <- c(max(nchar(rowname), nchar(labels[1])), rep(max(nchar(dmat[1,1,]), nchar(colname)), length(colname))) colwidth[2] <- max(colwidth[2], nchar(labels[2])) ncol <- length(colwidth) dd <- dim(dmat) # vector of length 3, third dim is the statistics rline <- ceiling(dd[3]/2) #which line to put the row label on. if (vline) { # use a grid table cat("+", paste(strpad('-', colwidth, pad='-'), collapse='+'), "+\n", sep='') temp <- rep(' ', ncol); temp[2] <- labels[2] cat("|", paste(strpad(temp, colwidth), collapse="|"), "|\n", sep='') cat("|", paste(strpad(c(labels[1], colname), colwidth), collapse="|"), "|\n", sep='') cat("+", paste(strpad('=', colwidth, pad='='), collapse="+"), "+\n", sep='') for (i in 1:dd[1]) { for (j in 1:dd[3]) { #one printout line per stat if (j==rline) temp <- c(rowname[i], dmat[i,,j]) else temp <- c("", dmat[i,,j]) cat("|", paste(strpad(temp, colwidth), collapse='|'), "|\n", sep='') } cat("+", paste(strpad('-', colwidth, '-'), collapse='+'), "+\n", sep='') } } else { # use a multiline table cat(paste(strpad('-', colwidth, '-'), collapse='-'), "\n") temp <- rep(' ', ncol); temp[2] <- labels[2] cat(paste(strpad(temp, colwidth), collapse=" "), "\n") cat(paste(strpad(c(labels[1], colname), colwidth), collapse=" "), "\n") cat(paste(strpad('-', colwidth, pad='-'), collapse=" "), "\n") for (i in 1:dd[1]) { for (j in 1:dd[3]) { #one printout line per stat if (j==rline) temp <- c(rowname[i], dmat[i,,j]) else temp <- c("", dmat[i,,j]) cat(paste(strpad(temp, colwidth), collapse=' '), "\n") } if (i< dd[1]) cat(" \n") #blank line } cat(paste(strpad('-', colwidth, '-'), collapse='-'), "\n") } } @ This function adds a totals row to the data, for either the first or first and second dimensions. The ``n'' component can't be totaled, so we turn that into NA. <>= pytot <- function(x, na=FALSE) { dd <- dim(x) if (length(dd) ==1) { if (na) array(c(x, NA), dim= length(x) +1, dimnames=list(c(dimnames(x)[[1]], "Total"))) else array(c(x, sum(x)), dim= length(x) +1, dimnames=list(c(dimnames(x)[[1]], "Total"))) } else if (length(dd) ==2) { if (na) new <- rbind(cbind(x, NA), NA) else { new <- rbind(x, colSums(x)) new <- cbind(new, rowSums(new)) } array(new, dim=dim(x) + c(1,1), dimnames=list(c(dimnames(x)[[1]], "Total"), c(dimnames(x)[[2]], "Total"))) } else { # The general case index <- 1:length(dd) if (na) sum1 <- sum2 <- sum3 <- NA else { sum1 <- apply(x, index[-1], sum) # row sums sum2 <- apply(x, index[-2], sum) # col sums sum3 <- apply(x, index[-(1:2)], sum) # total sums } # create a new matrix and then fill it in d2 <- dd d2[1:2] <- dd[1:2] +1 dname <- dimnames(x) dname[[1]] <- c(dname[[1]], "Total") dname[[2]] <- c(dname[[2]], "Total") new <- array(x[1], dim=d2, dimnames=dname) # say dim(x) =(5,8,4); we want new[6,-9,] <- sum1; new[-6,9,] <- sum2 # and new[6,9,] <- sum3 # if dim is longer, we need to add more commas commas <- rep(',', length(dd) -2) eval(parse(text=paste("new[1:dd[1], 1:dd[2]", commas, "] <- x"))) eval(parse(text=paste("new[ d2[1],-d2[2]", commas, "] <- sum1"))) eval(parse(text=paste("new[-d2[1], d2[2]", commas, "] <- sum2"))) eval(parse(text=paste("new[ d2[1], d2[2]", commas, "] <- sum3"))) new } } @ survival/noweb/yates2.Rnw0000644000176200001440000007600014073031214015134 0ustar liggesusersNow for the primary function. The user may have a list of tests, or a single term. The first part of the function does the usual of grabbing arguments and then checking them. The fit object has to have the standard stuff: terms, assign, xlevels and contrasts. Attributes of the terms are used often enough that we copy them to \code{Tatt} to save typing. We will almost certainly need the model frame and/or model matrix as well. In the discussion below I use x1 to refer to the covariates/terms that are the target, e.g. \code{test='Mask'} to get the mean population values for each level of the Mask variable in the solder data set, and x2 to refer to all the other terms in the model, the ones that we average over. These are also referred to as U and V in the vignette. <>= yates <- function(fit, term, population=c("data", "factorial", "sas"), levels, test =c("global", "trend", "pairwise"), predict="linear", options, nsim=200, method=c("direct", "sgtt")) { Call <- match.call() if (missing(fit)) stop("a fit argument is required") Terms <- try(terms(fit), silent=TRUE) if (inherits(Terms, "try-error")) stop("the fit does not have a terms structure") else Terms <- delete.response(Terms) # y is not needed Tatt <- attributes(Terms) # a flaw in delete.response: it doesn't subset dataClasses Tatt$dataClasses <- Tatt$dataClasses[row.names(Tatt$factors)] if (inherits(fit, "coxphms")) stop("multi-state coxph not yet supported") if (is.list(predict) || is.function(predict)) { # someone supplied their own stop("user written prediction functions are not yet supported") } else { # call the method indx <- match(c("fit", "predict", "options"), names(Call), nomatch=0) temp <- Call[c(1, indx)] temp[[1]] <- quote(yates_setup) mfun <- eval(temp, parent.frame()) } if (is.null(mfun)) predict <- "linear" # we will need the original model frame and X matrix mframe <- fit$model if (is.null(mframe)) mframe <- model.frame(fit) Xold <- model.matrix(fit) if (is.null(fit$assign)) { # glm models don't save assign xassign <- attr(Xold, "assign") } else xassign <- fit$assign nvar <- length(xassign) nterm <- length(Tatt$term.names) termname <- rownames(Tatt$factors) iscat <- sapply(Tatt$dataClasses, function(x) x %in% c("character", "factor")) method <- match.arg(casefold(method), c("direct", "sgtt")) #allow SGTT if (method=="sgtt" && missing(population)) population <- "sas" if (inherits(population, "data.frame")) popframe <- TRUE else if (is.character(population)) { popframe <- FALSE population <- match.arg(tolower(population[1]), c("data", "factorial", "sas", "empirical", "yates")) if (population=="empirical") population <- "data" if (population=="yates") population <- "factorial" } else stop("the population argument must be a data frame or character") test <- match.arg(test) if (popframe || population != "data") weight <- NULL else { weight <- model.extract(mframe, "weights") if (is.null(weight)) { id <- model.extract(mframe, "id") if (!is.null(id)) { # each id gets the same weight count <- c(table(id)) weight <- 1/count[match(id, names(count))] } } } if (method=="sgtt" && (population !="sas" || predict != "linear")) stop("sgtt method only applies if population = sas and predict = linear") beta <- coef(fit, complete=TRUE) nabeta <- is.na(beta) # undetermined coefficients vmat <- vcov(fit, complete=FALSE) if (nrow(vmat) > sum(!nabeta)) { # a vcov method that does not obey the complete argument vmat <- vmat[!nabeta, !nabeta] } # grab the dispersion, needed for the writing an SS in linear models if (class(fit)[1] =="lm") sigma <- summary(fit)$sigma else sigma <- NULL # don't compute an SS column # process the term argument and check its legality if (missing(levels)) contr <- cmatrix(fit, term, test, assign= xassign) else contr <- cmatrix(fit, term, test, assign= xassign, levels = levels) x1data <- as.data.frame(contr$levels) # labels for the PMM values # Make the list of X matrices that drive everything: xmatlist # (Over 1/2 the work of the whole routine) xmatlist <- yates_xmat(Terms, Tatt, contr, population, mframe, fit, iscat) # check rows of xmat for estimability <> # Drop missing coefficients, and use xmatlist to compute the results beta <- beta[!nabeta] if (predict == "linear" || is.null(mfun)) { # population averages of the simple linear predictor <> } else { <> } result$call <- Call class(result) <- "yates" result } @ Models with factor variables may often lead to population predictions that involve non-estimable functions, particularly if there are interactions and the user specifies a factorial population. If there are any missing coefficients we have to do formal checking for this: any given row of the new $X$ matrix, for prediction, must be in the row space of the original $X$ matrix. If this is true then a regression of a new row on the old $X$ will have residuals of zero. It is not possible to derive this from the pattern of NA coefficients alone. Set up a function that returns a true/false vector of whether each row of a matrix is estimable. This test isn't relevant if population=none. <>= if (any(is.na(beta)) && (popframe || population != "none")) { Xu <- unique(Xold) # we only need unique rows, saves time to do so if (inherits(fit, "coxph")) X.qr <- qr(t(cbind(1.0,Xu))) else X.qr <- qr(t(Xu)) # QR decomposition of the row space estimcheck <- function(x, eps= sqrt(.Machine$double.eps)) { temp <- abs(qr.resid(X.qr, t(x))) # apply(abs(temp), 1, function(x) all(x < eps)) # each row estimable all(temp < eps) } estimable <- sapply(xmatlist, estimcheck) } else estimable <- rep(TRUE, length(xmatlist)) @ When the prediction target is $X\beta$ there is a four step process: build the reference population, create the list of X matrices (one prediction matrix for each for x1 value), column means of each X form each row of the contrast matrix Cmat, and then use Cmat to get the pmm values and tests of the pmm values. <>= #temp <- match(contr$termname, colnames(Tatt$factors)) #if (any(is.na(temp))) # stop("term '", contr$termname[is.na(temp)], "' not found in the model") meanfun <- if (is.null(weight)) colMeans else function(x) { colSums(x*weight)/ sum(weight)} Cmat <- t(sapply(xmatlist, meanfun))[,!nabeta] # coxph model: the X matrix is built as though an intercept were there (the # baseline hazard plays that role), but then drop it from the coefficients # before computing estimates and tests. If there was a strata * covariate # interaction there will be many more colums to drop. if (inherits(fit, "coxph")) { nkeep <- length(fit$means) # number of non-intercept columns col.to.keep <- seq(to=ncol(Cmat), length= nkeep) Cmat <- Cmat[,col.to.keep, drop=FALSE] offset <- -sum(fit$means[!nabeta] * beta) # recenter the predictions too } else offset <- 0 # Get the PMM estimates, but only for estimable ones estimate <- cbind(x1data, pmm=NA, std=NA) if (any(estimable)) { etemp <- estfun(Cmat[estimable,,drop=FALSE], beta, vmat) estimate$pmm[estimable] <- etemp$estimate + offset estimate$std[estimable] <- sqrt(diag(etemp$var)) } # Now do tests on the PMM estimates, one by one if (method=="sgtt") { <> } else { if (is.list(contr$cmat)) { test <- t(sapply(contr$cmat, function(x) testfun(x %*% Cmat, beta, vmat, sigma^2))) natest <- sapply(contr$cmat, nafun, estimate$pmm) } else { test <- testfun(contr$cmat %*% Cmat, beta, vmat, sigma^2) test <- matrix(test, nrow=1, dimnames=list("global", names(test))) natest <- nafun(contr$cmat, estimate$pmm) } if (any(natest)) test[natest,] <- NA } if (any(estimable)){ # Cmat[!estimable,] <- NA result <- list(estimate=estimate, test=test, mvar=etemp$var, cmat=Cmat) } else result <- list(estimate=estimate, test=test, mvar=NA) if (method=="sgtt") result$SAS <- Smat @ In the non-linear case the mfun object is either a single function or a list containing two functions \code{predict} and \code{summary}. The predict function is handed a vector $\eta = X\beta$ along with the $X$ matrix, though most methods don't use $X$. The result of predict can be a vector or a matrix. For coxph models we add on an ``intercept coef'' that will center the predictions. <>= xall <- do.call(rbind, xmatlist)[,!nabeta, drop=FALSE] if (inherits(fit, "coxph")) { xall <- xall[,-1, drop=FALSE] # remove the intercept eta <- xall %*% beta -sum(fit$means[!nabeta]* beta) } else eta <- xall %*% beta n1 <- nrow(xmatlist[[1]]) # all of them are the same size index <- rep(1:length(xmatlist), each = n1) if (is.function(mfun)) predfun <- mfun else { # double check the object if (!is.list(mfun) || any(is.na(match(c("predict", "summary"), names(mfun)))) || !is.function(mfun$predic) || !is.function(mfun$summary)) stop("the prediction should be a function, or a list with two functions") predfun <- mfun$predict sumfun <- mfun$summary } pmm <- predfun(eta, xall) n2 <- length(eta) if (!(is.numeric(pmm)) || !(length(pmm)==n2 || nrow(pmm)==n2)) stop("prediction function should return a vector or matrix") pmm <- rowsum(pmm, index, reorder=FALSE)/n1 pmm[!estimable,] <- NA # get a sample of coefficients, in order to create a variance # this is lifted from the mvtnorm code (can't include a non-recommended # package in the dependencies) tol <- sqrt(.Machine$double.eps) if (!isSymmetric(vmat, tol=tol, check.attributes=FALSE)) stop("variance matrix of the coefficients is not symmetric") ev <- eigen(vmat, symmetric=TRUE) if (!all(ev$values >= -tol* abs(ev$values[1]))) warning("variance matrix is numerically not positive definite") Rmat <- t(ev$vectors %*% (t(ev$vectors) * sqrt(ev$values))) bmat <- matrix(rnorm(nsim*ncol(vmat)), nrow=nsim) %*% Rmat bmat <- bmat + rep(beta, each=nsim) # add the mean # Now use this matrix of noisy coefficients to get a set of predictions # and use those to create a variance matrix # Since if Cox we need to recenter each run sims <- array(0., dim=c(nsim, nrow(pmm), ncol(pmm))) if (inherits(fit, 'coxph')) offset <- bmat %*% fit$means[!nabeta] else offset <- rep(0., nsim) for (i in 1:nsim) sims[i,,] <- rowsum(predfun(xall %*% bmat[i,] - offset[i]), index, reorder=FALSE)/n1 mvar <- var(sims[,,1]) # this will be used for the tests estimate <- cbind(x1data, pmm=unname(pmm[,1]), std= sqrt(diag(mvar))) # Now do the tests, on the first column of pmm only if (is.list(contr$cmat)) { test <- t(sapply(contr$cmat, function(x) testfun(x, pmm[,1], mvar[estimable, estimable], NULL))) natest <- sapply(contr$cmat, nafun, pmm[,1]) } else { test <- testfun(contr$cmat, pmm[,1], mvar[estimable, estimable], NULL) test <- matrix(test, nrow=1, dimnames=list(contr$termname, names(test))) natest <- nafun(contr$cmat, pmm[,1]) } if (any(natest)) test[natest,] <- NA if (any(estimable)) result <- list(estimate=estimate,test=test, mvar=mvar) else result <- list(estimate=estimate, test=test, mvar=NA) # If there were multiple columns from predfun, compute the matrix of # results and variances if (ncol(pmm) > 1 && any(estimable)){ pmm <- apply(sims, 2:3, mean) mvar2 <- apply(sims, 2:3, var) # Call the summary function, if present if (is.list(mfun)) result$summary <- sumfun(pmm, mvar2) else { result$pmm <- pmm result$mvar2 <- mvar2 } } @ Build the population data set. If the user provided a data set as the population then the task is fairly straightforward: we manipulate the data set and then call model.frame followed by model.matrix in the usual way. The primary task in that case is to verify that the data has all the needed variables. Otherwise we have to be subtle. \begin{enumerate} \item We have ready access to a model frame, but not to the data. Consider a spline term for instance --- it's not always possible to go backwards and get the data. \item We need to manipulate this model frame, e.g., make everyone treatment=A, then repeat with everyone treatment B. \item We need to do it in a way that makes the frame still look like a correct model frame to R. This requires care. \end{enumerate} For population= factorial we create a population data set that has all the combinations. If there are three adjusters z1, z2 and z3 with 2, 3, and 5 levels, respectively, the new data set will have 30 rows. If the primary model didn't have any z1*z2*z3 terms in it we likely could get by with less, but it's not worth the programming effort to figure that out: predicted values are normally fairly cheap. For population=sas we need a mixture: categoricals are factorial and others are data. Say there were categoricals with 3 and 5 levels, so the factorial data set has 15 obs, while the overall n is 50. We need a data set of 15*50 observations to ensure all combinations of the two categoricals with each continuous line. An issue with data vs model is names. Suppose the original model was \code{lm(y \textasciitilde ns(age,4) + factor(ph.ecog))}. In the data set the variable name is ph.ecog, in the model frame, the xlevels list, and terms structure it is factor(ph.ecog). The data frame has individual columns for the four variables, the model frame is a list with 3 elements, one of which is named ``ns(age, 4)'': notice the extra space before the 4 compared to what was typed. <>= yates_xmat <- function(Terms, Tatt, contr, population, mframe, fit, iscat, weight) { # which variables(s) are in x1 (variables of interest) # First a special case of strata(grp):x, which causes strata(grp) not to # appear as a column if (is.na(match(contr$termname, colnames(Tatt$factors)))) { x1indx <- (contr$termname== rownames(Tatt$factors)) names(x1indx) <- rownames(Tatt$factors) if (!any(x1indx)) stop(paste("variable", contr$termname, "not found")) } else x1indx <- apply(Tatt$factors[,contr$termname,drop=FALSE] >0, 1, any) x2indx <- !x1indx # adjusters if (inherits(population, "data.frame")) pdata <- population #user data else if (population=="data") pdata <- mframe #easy case else if (population=="factorial") pdata <- yates_factorial_pop(mframe, Terms, x2indx, fit$xlevels) else if (population=="sas") { if (all(iscat[x2indx])) pdata <- yates_factorial_pop(mframe, Terms, x2indx, fit$xlevels) else if (!any(iscat[x2indx])) pdata <- mframe # no categoricals else { # mixed population pdata <- yates_factorial_pop(mframe, Terms, x2indx & iscat, fit$xlevels) n2 <- nrow(pdata) pdata <- pdata[rep(1:nrow(pdata), each=nrow(mframe)), ] row.names(pdata) <- 1:nrow(pdata) # fill in the continuous k <- rep(1:nrow(mframe), n2) for (i in which(x2indx & !iscat)) { j <- names(x1indx)[i] if (is.matrix(mframe[[j]])) pdata[[j]] <- mframe[[j]][k,, drop=FALSE] else pdata[[j]] <- (mframe[[j]])[k] attributes(pdata[[j]]) <- attributes(mframe[[j]]) } } } else stop("unknown population") # this should have been caught earlier # Now create the x1 data set, the unique rows we want to test <> xmatlist } @ Build a factorial data set from a model frame. <>= yates_factorial_pop <- function(mframe, terms, x2indx, xlevels) { x2name <- names(x2indx)[x2indx] dclass <- attr(terms, "dataClasses")[x2name] if (!all(dclass %in% c("character", "factor"))) stop("population=factorial only applies if all the adjusting terms are categorical") nvar <- length(x2name) n2 <- sapply(xlevels[x2name], length) # number of levels for each n <- prod(n2) # total number of rows needed pdata <- mframe[rep(1, n), -1] # toss the response row.names(pdata) <- NULL # throw away funny names n1 <- 1 for (i in 1:nvar) { j <- rep(rep(1:n2[i], each=n1), length=n) xx <- xlevels[[x2name[i]]] if (dclass[i] == "factor") pdata[[x2name[i]]] <- factor(j, 1:n2[i], labels= xx) else pdata[[x2name[i]]] <- xx[j] n1 <- n1 * n2[i] } attr(pdata, "terms") <- terms pdata } @ The next section builds a set of X matrices, one for each level of the x1 combination. The following was learned by reading the source code for model.matrix: \begin{itemize} \item If pdata has no terms attribute then model.matrix will call model.frame first, otherwise not. The xlev argument is passed forward to model.frame but is otherwise unused. \item If necessary, it will reorder the columns of pdata to match the terms, though I try to avoid that. \item Toss out the response variable, if present. \item Any character variables are turned into factors. The dataClass attribute of the terms object is not consulted. \item For each column that is a factor \begin{itemize} \item if it alreay has a contrasts attribute, it is left alone. \item otherwise a contrasts attribute is added using a matching element from contrasts.arg, if present, otherwise the global default \item contrasts.arg must be a list, but it does not have to contain all factors \end{itemize} \item Then call the internal C code \end{itemize} If pdata already is a model frame we want to leave it as one, so as to avoid recreating the raw data. If x1data comes from the user though, so we need to do that portion of model.frame processing ourselves, in order to get it into the right form. Always turn characters into factors, since individual elements of \code{xmatlist} will have only a subset of the x1 variables. One nuisance is name matching. Say the model had \code{factor(ph.ecog)} as a term; then \code{fit\$xlevels} will have `factor(ph.ecog)' as a name but the user will likely have created a data set using `ph.ecog' as the name. <>= if (is.null(contr$levels)) stop("levels are missing for this contrast") x1data <- as.data.frame(contr$levels) # in case it is a list x1name <- names(x1indx)[x1indx] for (i in 1:ncol(x1data)) { if (is.character(x1data[[i]])) { if (is.null(fit$xlevels[[x1name[i]]])) x1data[[i]] <- factor(x1data[[i]]) else x1data[[i]] <- factor(x1data[[i]], fit$xlevels[[x1name[i]]]) } } xmatlist <- vector("list", nrow(x1data)) if (is.null(attr(pdata, "terms"))) { np <- nrow(pdata) k <- match(x1name, names(pdata), nomatch=0) if (any(k>0)) pdata <- pdata[, -k, drop=FALSE] # toss out yates var for (i in 1:nrow(x1data)) { j <- rep(i, np) tdata <- cbind(pdata, x1data[j,,drop=FALSE]) # new data set xmatlist[[i]] <- model.matrix(Terms, tdata, xlev=fit$xlevels, contrast.arg= fit$contrasts) } } else { # pdata is a model frame, convert x1data # if the name and the class agree we go forward simply index <- match(names(x1data), names(pdata), nomatch=0) if (all(index >0) && identical(lapply(x1data, class), lapply(pdata, class)[index]) & identical(sapply(x1data, ncol) , sapply(pdata, ncol)[index])) { # everything agrees for (i in 1:nrow(x1data)) { j <- rep(i, nrow(pdata)) tdata <- pdata tdata[,names(x1data)] <- x1data[j,] xmatlist[[i]] <- model.matrix(Terms, tdata, contrasts.arg= fit$contrasts) } } else { # create a subset of the terms structure, for x1 only # for instance the user had age=c(75, 75, 85) and the term was ns(age) # then call model.frame to fix it up x1term <- Terms[which(x1indx)] x1name <- names(x1indx)[x1indx] attr(x1term, "dataClasses") <- Tatt$dataClasses[x1name] # R bug x1frame <- model.frame(x1term, x1data, xlev=fit$xlevels[x1name]) for (i in 1:nrow(x1data)) { j <- rep(i, nrow(pdata)) tdata <- pdata tdata[,names(x1frame)] <- x1frame[j,] xmatlist[[i]] <- model.matrix(Terms, tdata, xlev=fit$xlevels, contrast.arg= fit$contrasts) } } } @ The decompostion based algorithm for SAS type 3 tests. Ignore the set of contrasts cmat since the algorithm can only do a global test. We mostly mimic the SAS GLM algorithm. For the generalized Cholesky decomposition $LDL' = X'X$, where $L$ is lower triangular with $L_{ii}=1$ and $D$ is diagonal, the set of contrasts $L'\beta$ gives the type I sequential sums of squares, partitioning the rows of $L$ into those for term 1, term 2, etc. If $X$ is the design matrix for a balanced factorial design then it is also true that $L_{ij}=0$ unless term $j$ includes term $i$, e.g., x1:x2 includes x1. These blocks of zeros mean that changing the order of the terms in the model simply rearranges $L$, and individual tests are unchanged. This is precisely the definition of a type III contrast in SAS. With a bit of reading between the lines the ``four types of estimable functions'' document suggests the following algorithm: \begin{enumerate} \item Start with an $X$ matrix in standard order of intercept, main effects, first order interactions, etc. Code any categorical variable with $k$ levels as $k$ 0/1 columns. An interaction of two categoricals with $k$ and $l$ levels will have $kl$ columns, etc. \item Create the dependency matrix $D = (X'X)^-(X'X)$. If column $i$ of $X$ can be written as a linear combination of prior columns, then column $i$ of $D$ contains that combination. Other columns of $D$ match the identity matrix. \item Intitialize $L = D$. \item For any row $i$ and $j$ such that $i$ is contained in $j$, make $L_i$ orthagonal to $L_j$. \end{enumerate} The algorithm appears to work in almost all cases, an exception is when the type 3 test has fewer degrees of freedom that we would expect. Continuous variables are not orthagonalized in the SAS type III approach, nor any interaction that contains a continuous variable as one of its parts. To find the nested terms first note which rows of \code{factors} refer to categorical variables (the \code{iscat} variable); columns of \code{factors} that are non-zero only in categorical rows are the ``categorical'' columns. A term represented by one column in \code{factors} ``contains'' the term represented in some other column iff it's non-zero elements are a superset. We have to build a new X matrix that is the expanded SAS coding, and are only able to do that for models that have an intercept, and use contr.treatement or contr.SAS coding. <>= # It would be simplest to have the contrasts.arg to be a list of function names. # However, model.matrix plays games with the calling sequence, and any function # defined at this level will not be seen. Instead create a list of contrast # matrices. temp <- sapply(fit$contrasts, function(x) (is.character(x) && x %in% c("contr.SAS", "contr.treatment"))) if (!all(temp)) stop("yates sgtt method can only handle contr.SAS or contr.treatment") temp <- vector("list", length(fit$xlevels)) names(temp) <- names(fit$xlevels) for (i in 1:length(fit$xlevels)) { cmat <- diag(length(fit$xlevels[[i]])) dimnames(cmat) <- list(fit$xlevels[[i]], fit$xlevels[[i]]) if (i>1 || Tatt$intercept==1) { if (fit$contrasts[[i]] == "contr.treatment") cmat <- cmat[, c(2:ncol(cmat), 1)] } temp[[i]] <- cmat } sasX <- model.matrix(formula(fit), data=mframe, xlev=fit$xlevels, contrasts.arg=temp) sas.assign <- attr(sasX, "assign") # create the dependency matrix D. The lm routine is unhappy if it thinks # the right hand and left hand sides are the same, fool it with I(). # We do this using the entire X matrix even though only categoricals will # eventually be used; if a continuous variable made it NA we need to know. D <- coef(lm(sasX ~ I(sasX) -1)) dimnames(D)[[1]] <- dimnames(D)[[2]] #get rid if the I() names zero <- is.na(D[,1]) # zero rows, we'll get rid of these later D <- ifelse(is.na(D), 0, D) # make each row orthagonal to rows for other terms that contain it # Containing blocks, if any, will always be below # this is easiest to do with the transposed matrix # Only do this if both row i and j are for a categorical variable if (!all(iscat)) { # iscat marks variables in the model frame as categorical # tcat marks terms as categorical. For x1 + x2 + x1:x2 iscat has # 2 entries and tcat has 3. tcat <- (colSums(Tatt$factors[!iscat,,drop=FALSE]) == 0) } else tcat <- rep(TRUE, max(sas.assign)) # all vars are categorical B <- t(D) dimnames(B)[[2]] <- paste0("L", 1:ncol(B)) # for the user if (ncol(Tatt$factors) > 1) { share <- t(Tatt$factors) %*% Tatt$factors nc <- ncol(share) for (i in which(tcat[-nc])) { j <- which(share[i,] > 0 & tcat) k <- j[j>i] # terms that I need to regress out if (length(k)) { indx1 <- which(sas.assign ==i) indx2 <- which(sas.assign %in% k) B[,indx1] <- resid(lm(B[,indx1] ~ B[,indx2])) } } } # Cut B back down to the non-missing coefs of the original fit Smat <- t(B)[!zero, !zero] Sassign <- xassign[!nabeta] @ Although the SGTT does test for all terms, we only want to print out the ones that were asked for. <>= keep <- match(contr$termname, colnames(Tatt$factors)) if (length(keep) > 1) { # more than 1 term in the model test <- t(sapply(keep, function(i) testfun(Smat[Sassign==i,,drop=FALSE], beta, vmat, sigma^2))) rownames(test) <- contr$termname } else { test <- testfun(Smat[Sassign==keep,, drop=FALSE], beta, vmat, sigma^2) test <- matrix(test, nrow=1, dimnames=list(contr$termname, names(test))) } @ The print routine places the population predicted values (PPV) alongside the tests on those values. Defaults are copied from printCoefmat. <>= print.yates <- function(x, digits = max(3, getOption("digits") -2), dig.tst = max(1, min(5, digits-1)), eps=1e-8, ...) { temp1 <- x$estimate temp1$pmm <- format(temp1$pmm, digits=digits) temp1$std <- format(temp1$std, digits=digits) # the spaces help separate the two parts of the printout temp2 <- cbind(test= paste(" ", rownames(x$test)), data.frame(x$test), stringsAsFactors=FALSE) row.names(temp2) <- NULL temp2$Pr <- format.pval(pchisq(temp2$chisq, temp2$df, lower.tail=FALSE), eps=eps, digits=dig.tst) temp2$chisq <- format(temp2$chisq, digits= dig.tst) temp2$df <- format(temp2$df) if (!is.null(temp2$ss)) temp2$ss <- format(temp2$ss, digits=digits) if (nrow(temp1) > nrow(temp2)) { dummy <- temp2[1,] dummy[1,] <- "" temp2 <- rbind(temp2, dummy[rep(1, nrow(temp1)-nrow(temp2)),]) } if (nrow(temp2) > nrow(temp1)) { # get rid of any factors before padding for (i in which(sapply(temp1, is.factor))) temp1[[i]] <- as.character(temp1[[i]]) dummy <- temp1[1,] dummy[1,] <- "" temp1 <- rbind(temp1, dummy[rep(1, nrow(temp2)- nrow(temp1)),]) } print(cbind(temp1, temp2), row.names=FALSE) invisible(x) } @ Routines to allow yates to interact with other models. Each is called with the fitted model and the type of prediction. It should return NULL when the type is a linear predictor, since the parent routine has a very efficient approach in that case. Otherwise it returns a function that will be applied to each value $\eta$, from each row of a prediction matrix. <>= yates_setup <- function(fit, ...) UseMethod("yates_setup", fit) yates_setup.default <- function(fit, type, ...) { if (!missing(type) && !(type %in% c("linear", "link"))) warning("no yates_setup method exists for a model of class ", class(fit)[1], " and estimate type ", type, ", linear predictor estimate used by default") NULL } yates_setup.glm <- function(fit, predict = c("link", "response", "terms", "linear"), ...) { type <- match.arg(predict) if (type == "link" || type== "linear") NULL # same as linear else if (type == "response") { finv <- family(fit)$linkinv function(eta, X) finv(eta) } else if (type == "terms") stop("type terms not yet supported") } @ For the coxph routine, we are making use of the R environment by first defining the baseline hazard and then defining the predict and summary functions. This means that those functions have access to the baseline. <>= yates_setup.coxph <- function(fit, predict = c("lp", "risk", "expected", "terms", "survival", "linear"), options, ...) { type <- match.arg(predict) if (type=="lp" || type == "linear") NULL else if (type=="risk") function(eta, X) exp(eta) else if (type == "survival") { # If there are strata we need to do extra work # if there is an interaction we want to suppress a spurious warning suppressWarnings(baseline <- survfit(fit, censor=FALSE)) if (missing(options) || is.null(options$rmean)) rmean <- max(baseline$time) # max death time else rmean <- options$rmean if (!is.null(baseline$strata)) stop("stratified models not yet supported") cumhaz <- c(0, baseline$cumhaz) tt <- c(diff(c(0, pmin(rmean, baseline$time))), 0) predict <- function(eta, ...) { c2 <- outer(exp(drop(eta)), cumhaz) # matrix of values surv <- exp(-c2) meansurv <- apply(rep(tt, each=nrow(c2)) * surv, 1, sum) cbind(meansurv, surv) } summary <- function(surv, var) { bsurv <- t(surv[,-1]) std <- t(sqrt(var[,-1])) chaz <- -log(bsurv) zstat <- -qnorm((1-baseline$conf.int)/2) baseline$lower <- exp(-(chaz + zstat*std)) baseline$upper <- exp(-(chaz - zstat*std)) baseline$surv <- bsurv baseline$std.err <- std/bsurv baselinecumhaz <- chaz baseline } list(predict=predict, summary=summary) } else stop("type expected is not supported") } @ survival/noweb/coxsurv.Rnw0000644000176200001440000006457314012332762015457 0ustar liggesusers \subsection{Predicted survival} The \code{survfit} method for a Cox model produces individual survival curves. As might be expected these have much in common with ordinary survival curves, and share many of the same methods. The primary differences are first that a predicted curve always refers to a particular set of covariate values. It is often the case that a user wants multiple values at once, in which case the result will be a matrix of survival curves with a row for each time and a column for each covariate set. The second is that the computations are somewhat more difficult. The input arguments are \begin{description} \item[formula] a fitted object of class `coxph'. The argument name of `formula' is historic, from when the survfit function was not a generic and only did Kaplan-Meier type curves. \item[newdata] contains the data values for which curves should be produced, one per row \item[se.fit] TRUE/FALSE, should standard errors be computed. \item[individual] a particular option for time-dependent covariates \item[stype] survival type for the formula 1=direct 2= exp \item[ctype] cumulative hazard, 1=Nelson-Aalen, 2= corrected for ties \item[censor] if FALSE, remove any times that have no events from the output. This is for backwards compatability with older versions of the code. \item[id] replacement and extension for the individual argument \item[start.time] Start a curve at a later timepoint than zero. \item[influence] whether to return the influence matrix \end{description} All the other arguments are common to all the methods, refer to the help pages. Other survival routines have id and cluster options; this routine inherits those variables from coxph. If coxph did a robust variance, this routine will do one also. <>= survfit.coxph <- function(formula, newdata, se.fit=TRUE, conf.int=.95, individual=FALSE, stype=2, ctype, conf.type=c("log", "log-log", "plain", "none", "logit", "arcsin"), censor=TRUE, start.time, id, influence=FALSE, na.action=na.pass, type, ...) { Call <- match.call() Call[[1]] <- as.name("survfit") #nicer output for the user object <- formula #'formula' because it has to match survfit <> <> <> <> <> if (missing(newdata)) { if (inherits(formula, "coxphms")) stop ("newdata is required for multi-state models") risk2 <- 1 } else { if (length(object$means)) risk2 <- exp(c(x2 %*% beta) + offset2 - xcenter) else risk2 <- exp(offset2 - xcenter) } <> <> } @ The third line \code{as.name('survfit')} causes the printout to say `survfit' instead of `survfit.coxph'. %' The setup for the has three main phases, first of course to sort out the options the user has given us, second to rebuild the data frame, X matrix, etc from the original Cox model, and third to create variables from the new data set. In the code below x2, y2, strata2, id2, etc. are variables from the new data, X, Y, strata etc from the old. One exception to the pattern is id= argument, oldid = id from original data, id2 = id from new. If the newdata argument is missing we use \code{object\$means} as the default value. This choice has lots of statistical shortcomings, particularly in a stratified model, but is common in other packages and a historic option here. If stype is missing we use the standard approach of exp(cumulative hazard), and ctype is pulled from the Cox model. That is, the \code{coxph} computation used for \code{ties='breslow'} is the same as the Nelson-Aalen hazard estimate, and the Efron approximation the tie-corrected hazard. One particular special case (that gave me fits for a while) is when there are non-heirarchical models, for example \code{~ age + age:sex}. The fit of such a model will \emph{not} be the same using the variable \code{age2 <- age-50}; I originally thought it was a flaw induced by my subtraction. The routine simply cannot give a sensible curve for a model like this. The issue continued to surprise me each time I rediscovered it, leading to an error message for my own protection. I'm not convinced at this time that there is a sensible survival curve that \emph{could} be calculated for such a model. A model with \code{age + age:strata(sex)} will be ok, because the coxph routine treats this last term as though it had a * in it, i.e., fits a stratified model. <>= Terms <- terms(object) robust <- !is.null(object$naive.var) # did the coxph model use robust var? if (!is.null(attr(object$terms, "specials")$tt)) stop("The survfit function can not process coxph models with a tt term") if (!missing(type)) { # old style argument if (!missing(stype) || !missing(ctype)) warning("type argument ignored") else { temp1 <- c("kalbfleisch-prentice", "aalen", "efron", "kaplan-meier", "breslow", "fleming-harrington", "greenwood", "tsiatis", "exact") survtype <- match(match.arg(type, temp1), temp1) stype <- c(1,2,2,1,2,2,2,2,2)[survtype] if (stype!=1) ctype <-c(1,1,2,1,1,2,1,1,1)[survtype] } } if (missing(ctype)) { # Use the appropriate one from the model temp1 <- match(object$method, c("exact", "breslow", "efron")) ctype <- c(1,1,2)[temp1] } else if (!(ctype %in% 1:2)) stop ("ctype must be 1 or 2") if (!(stype %in% 1:2)) stop("stype must be 1 or 2") if (!se.fit) conf.type <- "none" else conf.type <- match.arg(conf.type) tfac <- attr(Terms, 'factors') temp <- attr(Terms, 'specials')$strata has.strata <- !is.null(temp) if (has.strata) { stangle = untangle.specials(Terms, "strata") #used multiple times, later # Toss out strata terms in tfac before doing the test 1 line below, as # strata end up in the model with age:strat(grp) terms or *strata() terms # (There might be more than one strata term) for (i in temp) tfac <- tfac[,tfac[i,] ==0] # toss out strata terms } if (any(tfac >1)) stop("not able to create a curve for models that contain an interaction without the lower order effect") Terms <- object$terms n <- object$n[1] if (!has.strata) strata <- NULL else strata <- object$strata if (!missing(individual)) warning("the `id' option supersedes `individual'") missid <- missing(id) # I need this later, and setting id below makes # "missing(id)" always false if (!missid) individual <- TRUE else if (missid && individual) id <- rep(0L,n) #dummy value else id <- NULL if (individual & missing(newdata)) { stop("the id option only makes sense with new data") } @ In two places below we need to know if there are strata by covariate interactions, which requires looking at attributes of the terms object. The factors attribute will have a row for the strata variable, or maybe more than one (multiple strata terms are legal). If it has a 1 in a column that corresponds to something of order 2 or greater, that is a strata by covariate interaction. <>= if (has.strata) { temp <- attr(Terms, "specials")$strata factors <- attr(Terms, "factors")[temp,] strata.interaction <- any(t(factors)*attr(Terms, "order") >1) } @ I need to retrieve a copy of the original data. We always need the $X$ matrix and $y$, both of which might be found in the data object. If the fit was a multistate model, the original call included either strata, offset, weights, or id, or if either $x$ or $y$ are missing from the \code{coxph} object, then the model frame will need to be reconstructed. We have to use \code{object['x'}] instead of \texttt{object\$x} since the latter will pick off the \code{xlevels} component if the \code{x} component is missing (which is the default). <>= coxms <- inherits(object, "coxphms") if (coxms || is.null(object$y) || is.null(object[['x']]) || !is.null(object$call$weights) || !is.null(object$call$id) || (has.strata && is.null(object$strata)) || !is.null(attr(object$terms, 'offset'))) { mf <- stats::model.frame(object) } else mf <- NULL #useful for if statements later @ For a single state model we can grab the X matrix off the model frame, for multistate some more work needs to be done. We have to repeat some lines from coxph, but to do that we need some further material. We prefer \code{object\$y} to model.response, since the former will have been passed through aeqSurv with the options the user specified. For a multi-state model, however, we do have to recreate since the saved y has been expanded. In that case observe the saved status of timefix. Old saved objects might not have that element, if missing assume TRUE. <>= position <- NULL Y <- object[['y']] if (is.null(mf)) { weights <- object$weights # let offsets/weights be NULL until needed offset <- NULL X <- object[['x']] } else { weights <- model.weights(mf) offset <- model.offset(mf) X <- model.matrix.coxph(object, data=mf) if (is.null(Y) || coxms) { Y <- model.response(mf) if (is.null(object$timefix) || object$timefix) Y <- aeqSurv(Y) } oldid <- model.extract(mf, "id") if (length(oldid) && ncol(Y)==3) position <- survflag(Y, oldid) else position <- NULL if (!coxms && (nrow(Y) != object$n[1])) stop("Failed to reconstruct the original data set") if (has.strata) { if (length(strata)==0) { if (length(stangle$vars) ==1) strata <- mf[[stangle$vars]] else strata <- strata(mf[, stangle$vars], shortlabel=TRUE) } } } @ If a model frame was created, then it is trivial to grab \code{y} from the new frame and compare it to \code{object\$y} from the original one. This is to avoid nonsense results that arise when someone changes the data set under our feet. We can only check the size: with the addition of aeqSurv other packages were being flagged for tiny discrepancies. Later note: this check does not work for multi-state models, and we don't \emph{have} to have it. Removed by using if (FALSE) so as to preserve the code for future consideration. <>= if (FALSE) { if (!is.null(mf)){ y2 <- object[['y']] if (!is.null(y2)) { if (ncol(y2) != ncol(Y) || length(y2) != length(Y)) stop("Could not reconstruct the y vector") } } } type <- attr(Y, 'type') if (!type %in% c("right", "counting", "mright", "mcounting")) stop("Cannot handle \"", type, "\" type survival data") if (!missing(start.time)) { if (!is.numeric(start.time) || length(start.time) > 1) stop("start.time must be a single numeric value") # Start the curves after start.time # To do so, remove any rows of the data with an endpoint before that # time. if (ncol(Y)==3) { keep <- Y[,2] > start.time Y[keep,1] <- pmax(Y[keep,1], start.time) } else keep <- Y[,1] > start.time if (!any(Y[keep, ncol(Y)]==1)) stop("start.time argument has removed all endpoints") Y <- Y[keep,,drop=FALSE] X <- X[keep,,drop=FALSE] if (!is.null(offset)) offset <- offset[keep] if (!is.null(weights)) weights <- weights[keep] if (!is.null(strata)) strata <- strata[keep] if (length(id) >0 ) id <- id[keep] if (length(position) >0) position <- position[keep] n <- nrow(Y) } @ In the above code we see id twice. The first, kept as \code{oldid} is the identifier variable for subjects in the original data set, and is needed whenever it contained subjects with more than one row. The second is the user variable of this call, and is used to define multiple rows for a new subject. The latter usage should be rare but we need to allow for it. If a variable is deemed redundant the \code{coxph} routine will have set its coefficient to NA as a marker. We want to ignore that coefficient: treating it as a zero has the desired effect. Another special case is a null model, having either ~1 or only an offset on the right hand side. In that case we create a dummy covariate to allow the rest of the code to work without special if/else. The last special case is a model with a sparse frailty term. We treat the frailty coefficients as 0 variance (in essence as an offset). The frailty is removed from the model variables but kept in the risk score. This isn't statistically very defensible, but it is backwards compatatble. %' A non-sparse frailty does not need special code and works out like any other variable. Center the risk scores by subtracting $ \overline x \hat\beta$ from each. The reason for this is to avoid huge values when calculating $\exp(X\beta)$; this would happen if someone had a variable with a mean of 1000 and a variance of 1. Any constant can be subtracted, mathematically the results are identical as long as the same values are subtracted from the old and new $X$ data. The mean is used because it is handy, we just need to get $X\beta$ in the neighborhood of zero. <>= if (length(object$means) ==0) { # a model with only an offset term # Give it a dummy X so the rest of the code goes through # (This case is really rare) # se.fit <- FALSE X <- matrix(0., nrow=n, ncol=1) if (is.null(offset)) offset <- rep(0, n) xcenter <- mean(offset) coef <- 0.0 varmat <- matrix(0.0, 1, 1) risk <- rep(exp(offset- mean(offset)), length=n) } else { varmat <- object$var beta <- ifelse(is.na(object$coefficients), 0, object$coefficients) if (is.null(offset)) xcenter <- sum(object$means * beta) else xcenter <- sum(object$means * beta)+ mean(offset) if (!is.null(object$frail)) { keep <- !grepl("frailty(", dimnames(X)[[2]], fixed=TRUE) X <- X[,keep, drop=F] } if (is.null(offset)) risk <- c(exp(X%*% beta - xcenter)) else risk <- c(exp(X%*% beta + offset - xcenter)) } @ The \code{risk} vector and \code{x} matrix come from the original data, and are the raw data for the survival curve and its variance. We also need the risk score $\exp(X\beta)$ for the target subject(s). \begin{itemize} \item For predictions with time-dependent covariates the user will have either included an \code{id} statement (newer style) or specified the \code{individual=TRUE} option. If the latter, then \code{newdata} is presumed to contain only a single indivual represented by multiple rows. If the former then the \code{id} variable marks separate individuals. In either case we need to retrieve the covariates, strata, and repsonse from the new data set. \item For ordinary predictions only the covariates are needed. \item If newdata is not present we assume that this is the ordinary case, and use the value of \code{object\$means} as the default covariate set. This is not ideal statistically since many users view this as an ``average'' survival curve, which it is not. \end{itemize} When grabbing [newdata] we want to use model.frame processing, both to handle missing values correctly and, perhaps more importantly, to correctly map any factor variables between the original fit and the new data. (The new data will often have only one of the original levels represented.) Also, we want to correctly handle data-dependent nonlinear terms such as ns and pspline. However, the simple call found in predict.lm, say, \code{model.frame(Terms, data=newdata, ..} isn't used here for a few reasons. The first is a decision on our part that the user should not have to include unused terms in the newdata: sometimes we don't need the response and sometimes we do. Second, if there are strata, the user may or may not have included strata variables in their data set and we need to act accordingly. The third is that we might have an \code{id} statement in this call, which is another variable to be fetched. At one time we dealt with cluster() terms in the formula, but the coxph routine has already removed those for us. Finally, note that there is no ability to use sparse frailties and newdata together; it is a hard case and so rare as to not be worth it. First, remove unnecessary terms from the orginal model formula. If \code{individual} is false then the repsonse variable can go. The dataClasses and predvars attributes, if present, have elements in the same order as the first dimension of the ``factors'' attribute of the terms. Subscripting the terms argument does not preserve dataClasses or predvars, however. Use the pre and post subscripting factors attribute to determine what elements of them to keep. The predvars component is a call objects with one element for each term in the formula, so y ~ age + ns(height) would lead to a predvars of length 4, element 1 is the call itself, 2 would be y, etc. The dataClasses object is a simple list. <>= if (missing(newdata)) { # If the model has interactions, print out a long warning message. # People may hate it, but I don't see another way to stamp out these # bad curves without backwards-incompatability. # I probably should complain about factors too (but never in a strata # or cluster term). if (any(attr(Terms, "order") > 1) ) warning("the model contains interactions; the default curve based on columm means of the X matrix is almost certainly not useful. Consider adding a newdata argument.") if (length(object$means)) { mf2 <- as.list(object$means) #create a dummy newdata names(mf2) <- names(object$coefficients) mf2 <- as.data.frame(mf2) x2 <- matrix(object$means, 1) } else { # nothing but an offset mf2 <- data.frame(X=0) x2 <- 0 } offset2 <- 0 found.strata <- FALSE } else { if (!is.null(object$frail)) stop("Newdata cannot be used when a model has frailty terms") Terms2 <- Terms if (!individual) Terms2 <- delete.response(Terms) <> } @ For backwards compatability, I allow someone to give an ordinary vector instead of a data frame (when only one curve is required). In this case I also need to verify that the elements have a name. Then turn it into a data frame, like it should have been from the beginning. (Documentation of this ability has been suppressed, however. I'm hoping people forget it ever existed.) <>= if (is.vector(newdata, "numeric")) { if (individual) stop("newdata must be a data frame") if (is.null(names(newdata))) { stop("Newdata argument must be a data frame") } newdata <- data.frame(as.list(newdata), stringsAsFactors=FALSE) } @ Finally get my new model frame mf2. We allow the user to leave out any strata() variables if they so desire, \emph{if} there are no strata by covariate interactions. How does one check if the strata variables are or are not available in the call? My first attempt at this was to wrap the call in a try() construct and see if it failed. This doesn't work. \begin{itemize} \item What if there is no strata variable in newdata, but they do have, by bad luck, a variable of the same name in their main directory? \item It would seem like changing the environment to NULL would be wise, so that we don't find variables anywhere but in the data argument, a sort of sandboxing. Not wise: you then won't find functions like ``log''. \item We don't dare modify the environment of the formula at all. It is needed for the sneaky caller who uses his own function inside the formula, 'mycosine' say, and that function can only be found if we retain the environment. \end{itemize} One way out of this is to evaluate each of the strata terms (there can be more than one) one at a time, in an environment that knows nothing except "list" and a fake definition of "strata", and newdata. Variables that are part of the global environment won't be found. I even watch out for the case of either "strata" or "list" is the name of the stratification variable, which causes my fake strata function to return a function when said variable is not in newdata. The variable found.strata is true if ALL the strata are found, set it to false if any are missing. <>= if (has.strata) { found.strata <- TRUE tempenv <- new.env(, parent=emptyenv()) assign("strata", function(..., na.group, shortlabel, sep) list(...), envir=tempenv) assign("list", list, envir=tempenv) for (svar in stangle$vars) { temp <- try(eval(parse(text=svar), newdata, tempenv), silent=TRUE) if (!is.list(temp) || any(unlist(lapply(temp, class))== "function")) found.strata <- FALSE } if (!found.strata) { ss <- untangle.specials(Terms2, "strata") Terms2 <- Terms2[-ss$terms] } } tcall <- Call[c(1, match(c('id', "na.action"), names(Call), nomatch=0))] tcall$data <- newdata tcall$formula <- Terms2 tcall$xlev <- object$xlevels[match(attr(Terms2,'term.labels'), names(object$xlevels), nomatch=0)] tcall[[1L]] <- quote(stats::model.frame) mf2 <- eval(tcall) @ Now, finally, extract the \code{x2} matrix from the just-created frame. <>= if (has.strata && found.strata) { #pull them off temp <- untangle.specials(Terms2, 'strata') strata2 <- strata(mf2[temp$vars], shortlabel=TRUE) strata2 <- factor(strata2, levels=levels(strata)) if (any(is.na(strata2))) stop("New data set has strata levels not found in the original") # An expression like age:strata(sex) will have temp$vars= "strata(sex)" # and temp$terms = integer(0). This does not work as a subscript if (length(temp$terms) >0) Terms2 <- Terms2[-temp$terms] } else strata2 <- factor(rep(0, nrow(mf2))) if (!robust) cluster <- NULL if (individual) { if (missing(newdata)) stop("The newdata argument must be present when individual=TRUE") if (!missid) { #grab the id variable id2 <- model.extract(mf2, "id") if (is.null(id2)) stop("id=NULL is an invalid argument") } else id2 <- rep(1, nrow(mf2)) x2 <- model.matrix(Terms2, mf2)[,-1, drop=FALSE] #no intercept if (length(x2)==0) stop("Individual survival but no variables") offset2 <- model.offset(mf2) if (length(offset2) ==0) offset2 <- 0 y2 <- model.extract(mf2, 'response') if (attr(y2,'type') != type) stop("Survival type of newdata does not match the fitted model") if (attr(y2, "type") != "counting") stop("Individual=TRUE is only valid for counting process data") y2 <- y2[,1:2, drop=F] #throw away status, it's never used } else if (missing(newdata)) { if (has.strata && strata.interaction) stop ("Models with strata by covariate interaction terms require newdata") offset2 <- 0 if (length(object$means)) { x2 <- matrix(object$means, nrow=1, ncol=ncol(X)) } else { # model with only an offset and no new data: very rare case x2 <- matrix(0.0, nrow=1, ncol=1) # make a dummy x2 } } else { offset2 <- model.offset(mf2) if (length(offset2) >0) offset2 <- offset2 else offset2 <- 0 x2 <- model.matrix(Terms2, mf2)[,-1, drop=FALSE] #no intercept } @ <>= if (individual) { result <- coxsurv.fit(ctype, stype, se.fit, varmat, cluster, Y, X, weights, risk, position, strata, oldid, y2, x2, risk2, strata2, id2) } else { result <- coxsurv.fit(ctype, stype, se.fit, varmat, cluster, Y, X, weights, risk, position, strata, oldid, y2, x2, risk2) if (has.strata && found.strata) { if (is.matrix(result$surv)) { <> } } } @ The final bit of work. If the newdata arg contained strata then the user should not get a matrix of survival curves containing every newdata obs * strata combination, but rather a vector of curves, each one with the appropriate strata. It was faster to compute them all, however, than to use the individual=T logic. So now pick off the bits we want. The names of the curves will be the rownames of the newdata arg, if they exist. <>= nr <- nrow(result$surv) #a vector if newdata had only 1 row indx1 <- split(1:nr, rep(1:length(result$strata), result$strata)) rows <- indx1[as.numeric(strata2)] #the rows for each curve indx2 <- unlist(rows) #index for time, n.risk, n.event, n.censor indx3 <- as.integer(strata2) #index for n and strata for(i in 2:length(rows)) rows[[i]] <- rows[[i]]+ (i-1)*nr #linear subscript indx4 <- unlist(rows) #index for surv and std.err temp <- result$strata[indx3] names(temp) <- row.names(mf2) new <- list(n = result$n[indx3], time= result$time[indx2], n.risk= result$n.risk[indx2], n.event=result$n.event[indx2], n.censor=result$n.censor[indx2], strata = temp, surv= result$surv[indx4], cumhaz = result$cumhaz[indx4]) if (se.fit) new$std.err <- result$std.err[indx4] result <- new @ Finally, the last (somewhat boring) part of the code. First, if given the argument \code{censor=FALSE} we need to remove all the time points from the output at which there was only censoring activity. This action is mostly for backwards compatability with older releases that never returned censoring times. Second, add in the variance and the confidence intervals to the result. The code is nearly identical to that in survfitKM. <>= if (!censor) { kfun <- function(x, keep){ if (is.matrix(x)) x[keep,,drop=F] else if (length(x)==length(keep)) x[keep] else x} keep <- (result$n.event > 0) if (!is.null(result$strata)) { temp <- factor(rep(names(result$strata), result$strata), levels=names(result$strata)) result$strata <- c(table(temp[keep])) } result <- lapply(result, kfun, keep) } result$logse = TRUE # this will migrate further in if (se.fit && conf.type != "none") { ci <- survfit_confint(result$surv, result$std.err, logse=result$logse, conf.type, conf.int) result <- c(result, list(lower=ci$lower, upper=ci$upper, conf.type=conf.type, conf.int=conf.int)) } if (!missing(start.time)) result$start.time <- start.time result$call <- Call class(result) <- c('survfitcox', 'survfit') result @ survival/noweb/exact.nw0000644000176200001440000004244214100265736014723 0ustar liggesusers\subsection{Exact partial likelihood} Let $r_i = \exp(X_i\beta)$ be the risk score for observation $i$. For one of the time points assume that there that there are $d$ tied deaths among $n$ subjects at risk. For convenience we will index them as $i= 1,\ldots,d$ in the $n$ at risk. Then for the exact parial likelihood, the contribution at this time point is \begin{align*} L &= \sum_{i=1}^d \log(r_i) - \log(D) \\ \frac{\partial L}{\partial \beta_j} &= x_{ij} - (1/D) \frac{\partial D}{\partial \beta_j} \\ \frac{\partial^2 L}{\partial \beta_j \partial \beta_k} &= (1/D^2)\left[D\frac{\partial^2D}{\partial \beta_j \partial \beta_k} - \frac{\partial D}{\partial \beta_j}\frac{\partial D}{\partial \beta_k} \right] \end{align*} The hard part of this computation is $D$, which is a sum \begin{equation*} D = \sum_{S(d,n)} r_{s_1}r_{s_2} \ldots r_{s_d} \end{equation*} where $S(d,n)$ is the set of all possible subsets of size $d$ from $n$ objects, and $s_1, s_2, \ldots$ indexes the current selection. So if $n=6$ and $d=2$ we would have the 15 pairs 12, 13, .... 56; for $n=5$ and $d=3$ there would be 10 triples 123, 124, 125, \ldots, 345. The brute force computation of all subsets can take a very long time. Gail et al \cite{Gail81} show simple recursion formulas that speed this up considerably. Let $D(d,n)$ be the denominator with $d$ deaths and $n$ subjects. Then \begin{align} D(d,n) &= r_nD(d-1, n-1) + D(d, n-1) \label{d0}\\ \frac{\partial D(d,n)}{\partial \beta_j} &= \frac{\partial D(d, n-1)}{\partial \beta_j} + r_n \frac{\partial D(d-1, n-1)}{\partial \beta_j} + x_{nj}r_n D(d-1, n-1) \label{d1}\\ \frac{\partial^2D(d,n}{\partial \beta_j \partial \beta_k} &= \frac{\partial^2D(d,n-1)}{\partial \beta_j \partial \beta_k} + r_n\frac{\partial^2D(d-1,n-1)}{\partial \beta_j \partial \beta_k} + x_{nj}r_n\frac{\partial D(d-1, n-1)}{\partial \beta_k} + \nonumber \\ & x_{nk}r_n\frac{\partial D(d-1, n-1)}{\partial \beta_j} + x_{nj}x_{nk}r_n D(d-1, n-1) \label{d2} \end{align} The above recursion is captured in the three routines below. The first calculates $D$. It is called with $d$, $n$, an array that will contain all the values of $D(d,n)$ computed so far, and the the first dimension of the array. The intial condition $D(0,n)=1$ is important to all three routines. <>= #define NOTDONE -1.1 double coxd0(int d, int n, double *score, double *dmat, int dmax) { double *dn; if (d==0) return(1.0); dn = dmat + (n-1)*dmax + d -1; /* pointer to dmat[d,n] */ if (*dn == NOTDONE) { /* still to be computed */ *dn = score[n-1]* coxd0(d-1, n-1, score, dmat, dmax); if (d>= double coxd1(int d, int n, double *score, double *dmat, double *d1, double *covar, int dmax) { int indx; indx = (n-1)*dmax + d -1; /*index to the current array member d1[d.n]*/ if (d1[indx] == NOTDONE) { /* still to be computed */ d1[indx] = score[n-1]* covar[n-1]* coxd0(d-1, n-1, score, dmat, dmax); if (d1) d1[indx] += score[n-1]* coxd1(d-1, n-1, score, dmat, d1, covar, dmax); } return(d1[indx]); } double coxd2(int d, int n, double *score, double *dmat, double *d1j, double *d1k, double *d2, double *covarj, double *covark, int dmax) { int indx; indx = (n-1)*dmax + d -1; /*index to the current array member d1[d,n]*/ if (d2[indx] == NOTDONE) { /*still to be computed */ d2[indx] = coxd0(d-1, n-1, score, dmat, dmax)*score[n-1] * covarj[n-1]* covark[n-1]; if (d1) d2[indx] += score[n-1] * ( coxd2(d-1, n-1, score, dmat, d1j, d1k, d2, covarj, covark, dmax) + covarj[n-1] * coxd1(d-1, n-1, score, dmat, d1k, covark, dmax) + covark[n-1] * coxd1(d-1, n-1, score, dmat, d1j, covarj, dmax)); } return(d2[indx]); } @ Now for the main body. Start with the dull part of the code: declarations. I use [[maxiter2]] for the S structure and [[maxiter]] for the variable within it, and etc for the other input arguments. All the input arguments except strata are read-only. The output beta vector starts as a copy of ibeta. <>= #include #include "survS.h" #include "survproto.h" #include <> SEXP coxexact(SEXP maxiter2, SEXP y2, SEXP covar2, SEXP offset2, SEXP strata2, SEXP ibeta, SEXP eps2, SEXP toler2) { int i,j,k; int iter; double **covar, **imat; /*ragged arrays */ double *time, *status; /* input data */ double *offset; int *strata; int sstart; /* starting obs of current strata */ double *score; double *oldbeta; double zbeta; double newlk=0; double temp; int halving; /*are we doing step halving at the moment? */ int nrisk =0; /* number of subjects in the current risk set */ int dsize, /* memory needed for one coxc0, coxc1, or coxd2 array */ dmemtot, /* amount needed for all arrays */ ndeath; /* number of deaths at the current time point */ double maxdeath; /* max tied deaths within a strata */ double dtime; /* time value under current examiniation */ double *dmem0, **dmem1, *dmem2; /* pointers to memory */ double *dtemp; /* used for zeroing the memory */ double *d1; /* current first derivatives from coxd1 */ double d0; /* global sum from coxc0 */ /* copies of scalar input arguments */ int nused, nvar, maxiter; double eps, toler; /* returned objects */ SEXP imat2, beta2, u2, loglik2; double *beta, *u, *loglik; SEXP rlist, rlistnames; int nprotect; /* number of protect calls I have issued */ <> <> <> <> } @ Setup is ordinary. Grab S objects and assign others. I use \verb!R_alloc! for temporary ones since it is released automatically on return. <>= nused = LENGTH(offset2); nvar = ncols(covar2); maxiter = asInteger(maxiter2); eps = asReal(eps2); /* convergence criteria */ toler = asReal(toler2); /* tolerance for cholesky */ /* ** Set up the ragged array pointer to the X matrix, ** and pointers to time and status */ covar= dmatrix(REAL(covar2), nused, nvar); time = REAL(y2); status = time +nused; strata = INTEGER(PROTECT(duplicate(strata2))); offset = REAL(offset2); /* temporary vectors */ score = (double *) R_alloc(nused+nvar, sizeof(double)); oldbeta = score + nused; /* ** create output variables */ PROTECT(beta2 = duplicate(ibeta)); beta = REAL(beta2); PROTECT(u2 = allocVector(REALSXP, nvar)); u = REAL(u2); PROTECT(imat2 = allocVector(REALSXP, nvar*nvar)); imat = dmatrix(REAL(imat2), nvar, nvar); PROTECT(loglik2 = allocVector(REALSXP, 5)); /* loglik, sctest, flag,maxiter*/ loglik = REAL(loglik2); nprotect = 5; @ The data passed to us has been sorted by strata, and reverse time within strata (longest subject first). The variable [[strata]] will be 1 at the start of each new strata. Separate strata are completely separate computations: time 10 in one strata and time 10 in another are not comingled. Compute the largest product (size of strata)* (max tied deaths in strata) for allocating scratch space. When computing $D$ it is advantageous to create all the intermediate values of $D(d,n)$ in an array since they will be used in the derivative calculation. Likewise, the first derivatives are used in calculating the second. Even more importantly, say we have a large data set. It will be sorted with the shortest times first. If there is a death with 30 at risk and another with 40 at risk, the intermediate sums we computed for the n=30 case are part of the computation for n=40. To make this work we need to index our matrices, within any strata, by the maximum number of tied deaths in the strata. We save this in the strata variable: first obs of a new strata has the number of events. And what if a strata had 0 events? We mark it with a 1. Note that the maxdeath variable is floating point. I had someone call this routine with a data set that gives an integer overflow in that situation. We now keep track of this further below and fail with a message. Such a run would take longer than forever to complete even if integer subscripts did not overflow. <>= strata[0] =1; /* in case the parent forgot (e.g., no strata case)*/ temp = 0; /* temp variable for dsize */ maxdeath =0; j=0; /* first obs of current stratum */ ndeath=0; nrisk=0; for (i=0; i0) { /* assign data for the prior stratum, just finished */ /* If maxdeath <2 leave the strata alone at it's current value of 1 */ if (maxdeath >1) strata[j] = maxdeath; j = i; if (maxdeath*nrisk > temp) temp = maxdeath*nrisk; } maxdeath =0; /* max tied deaths at any time in this strata */ nrisk=0; ndeath =0; } dtime = time[i]; ndeath =0; /*number tied here */ while (time[i] ==dtime) { nrisk++; ndeath += status[i]; i++; if (i>=nused || strata[i] >0) break; /* don't cross strata */ } if (ndeath > maxdeath) maxdeath = ndeath; } /* data for the final stratum */ if (maxdeath*nrisk > temp) temp = maxdeath*nrisk; if (maxdeath >1) strata[j] = maxdeath; /* Now allocate memory for the scratch arrays Each per-variable slice is of size dsize */ dsize = temp; temp = temp * ((nvar*(nvar+1))/2 + nvar + 1); dmemtot = dsize * ((nvar*(nvar+1))/2 + nvar + 1); if (temp != dmemtot) { /* the subscripts will overflow */ error("(number at risk) * (number tied deaths) is too large"); } dmem0 = (double *) R_alloc(dmemtot, sizeof(double)); /*pointer to memory */ dmem1 = (double **) R_alloc(nvar, sizeof(double*)); dmem1[0] = dmem0 + dsize; /*points to the first derivative memory */ for (i=1; i>= sstart =0; /* a line to make gcc stop complaining */ for (i=0; i0) { /* first obs of a new strata */ maxdeath= strata[i]; dtemp = dmem0; for (j=0; j=nused || strata[i] >0) break; } /* We have added up over the death time, now process it */ if (ndeath >0) { /* Add to the loglik */ d0 = coxd0(ndeath, nrisk, score+sstart, dmem0, maxdeath); R_CheckUserInterrupt(); newlk -= log(d0); dmem2 = dmem0 + (nvar+1)*dsize; /*start for the second deriv memory */ for (j=0; j 3) R_CheckUserInterrupt(); u[j] -= d1[j]; for (k=0; k<= j; k++) { /* second derivative*/ temp = coxd2(ndeath, nrisk, score+sstart, dmem0, dmem1[j], dmem1[k], dmem2, covar[j] + sstart, covar[k] + sstart, maxdeath); if (ndeath > 5) R_CheckUserInterrupt(); imat[k][j] += temp/d0 - d1[j]*d1[k]; dmem2 += dsize; } } } } @ Do the first iteration of the solution. The first iteration is different in 3 ways: it is used to set the initial log-likelihood, to compute the score test, and we pay no attention to convergence criteria or diagnositics. (I expect it not to converge in one iteration). <>= /* ** do the initial iteration step */ newlk =0; for (i=0; i> loglik[0] = newlk; /* save the loglik for iteration zero */ loglik[1] = newlk; /* and it is our current best guess */ /* ** update the betas and compute the score test */ for (i=0; i> } /* ** Never, never complain about convergence on the first step. That way, ** if someone has to they can force one iter at a time. */ for (i=0; i>= halving =0 ; /* =1 when in the midst of "step halving" */ for (iter=1; iter<=maxiter; iter++) { newlk =0; for (i=0; i> /* am I done? ** update the betas and test for convergence */ loglik[3] = cholesky2(imat, nvar, toler); if (fabs(1-(loglik[1]/newlk))<= eps && halving==0) { /* all done */ loglik[1] = newlk; <> } if (iter==maxiter) break; /*skip the step halving and etc */ if (newlk < loglik[1]) { /*it is not converging ! */ halving =1; for (i=0; i> @ The common code for finishing. Invert the information matrix, copy it to be symmetric, and put together the output structure. <>= loglik[4] = iter; chinv2(imat, nvar); for (i=1; i>= survexpmsetup <- function(rmat) { # check the validity of the transition matrix, and determine if it # is acyclic, i.e., can be reordered into an upper triangular matrix. if (!is.matrix(rmat) || nrow(rmat) != ncol(rmat) || any(diag(rmat) > 0) || any(rmat[row(rmat) != col(rmat)] < 0)) stop ("input is not a transition matrix") if (!is.logical(all.equal(rowSums(rmat), rep(0, ncol(rmat))))) stop("input is not a transition matrix") nc <- ncol(rmat) lower <- row(rmat) > col(rmat) if (all(rmat[lower] ==0)) return(0) # already in order # score each state by (number of states it follows) - (number it precedes) temp <- 1*(rmat >0) # 0/1 matrix indx <- order(colSums(temp) - rowSums(temp)) temp <- rmat[indx, indx] # try that ordering if (all(temp[lower]== 0)) indx # it worked! else -1 # there is a loop in the states } @ \subsection{Decompostion} Based on Kalbfleisch and Lawless, ``The analysis of panel data under a Markov assumption'' (J Am Stat Assoc, 1985:863-871), the rate matrix $R$ can be written as $ADA^{-1}$ for some matrix $A$, where $D$ is a diagonal matrix of eigenvalues, provided all of the eigenvalues are distinct. Then $R^k = A D^k A^{-1}$, and using the definition of a matrix exponential we see that $\exp(R) = A \exp(D) A^{-1}$. The exponential of a diagonal matrix is simply a diagonal matrix of the exponentials. The matrix $Rt$ for a scalar $t$ has decomposition $A\exp(Dt)A^{-1}$; a single decompostion suffices for all values of $t$. A particular example is \begin{equation} R = \begin{pmatrix} r_{11} & r_{12} & r_{13} & 0 & 0 & r_{15}\\ 0 & r_{22} & 0 & r_{24} & 0 & r_{25}\\ 0 & 0 & r_{33} & r_{34} & r_{35} & r_{35}\\ 0 & 0 & 0 & r_{44} & r_{45} & r_{45} \\ 0 & 0 & 0 & 0 & r_{55} & r_{55} \\ 0 & 0 & 0 & 0 & 0 & 0 \end{pmatrix}. \end{equation} Since this is a transition matrix the diagonal elements are constrained so that row sums are zero: $r_{ii} = -\sum_{j\ne i} r_{ij}$. Since R is an upper triangular matrix it's eigenvalues lie on the diagonal. If none of the the eigenvalues are repeated, then the Prentice result applies. The decompostion is quite simple since $R$ is triangular. We want the eigenvectors, i.e. solutions to \begin{align*} R v_i &= r_{ii} v_i \\ % R v_2 &= r_{22} v_2 \\ % R v_3 &= r_{33} v_3 \\ % R v_4 &= r_{44} v_4 \\ % R v_5 &= r_{55} v_5 \\ % R v_6 &= r_{66} v_6 \end{align*} for $i= 1, \dots, 6$, where $v_i$ are the colums of $V$. It turns out that the set of eigenvectors is also upper triangular; we can solve for them one by one using back substitution. For the first eigenvector we have $v_1 = (1, 0,0,0,0,0)$. For the second we have the equations \begin{align*} r_{11} x + r_{12}y &= r_{22} x \\ r_{22}y &= r_{22} y \end{align*} which has the solution $(r_{12}/(r_{22}- r_{11}), 1, 0,0,0,0)$, and the process recurs for other rows. Since $V$ is triangular the inverse of $V$ is upper triangular and also easy to compute. This approach fails if there are tied eigenvalues. Kalbfleice and Lawless comment that this case is rare, but one can then use a decomposition to Jordan canonical form re Cox and Miller, the Theory of Stochastic Processes, 1965. Although this leads to some nice theorems it does not give a simple comutational form, however, and it is easier to fall back on the pade routine. At this time, the pade routine is as fast as the triangluar code, at least for small matrices without deriviatives. <>= survexpm <- function(rmat, time=1.0, setup, eps=1e-6) { # rmat is a transition matrix, so the diagonal elements are 0 or negative if (length(rmat)==1) exp(rmat[1]*time) #failsafe -- should never be called else { nonzero <- (diag(rmat) != 0) if (sum(nonzero ==0)) diag(nrow(rmat)) # expm(0 matrix) = identity if (sum(nonzero) ==1) { j <- which(nonzero) emat <- diag(nrow(rmat)) temp <- exp(rmat[j,j] * time) emat[j,j] <- temp emat[j, -j] <- (1-temp)* rmat[j, -j]/sum(rmat[j,-j]) emat } else if (missing(setup) || setup[1] < 0 || any(diff(sort(diag(rmat)))< eps)) pade(rmat*time) else { if (setup[1]==0) .Call(Ccdecomp, rmat, time)$P else { temp <- rmat temp[setup, setup] <- .Call(Ccdecomp, rmat[setup, setup], time) temp$P } } } } @ The routine below is modeled after the cholesky routines in the survival library. To help with notation, the return values are labeled as in the Kalbfleisch and Lawless paper, except that their Q = our rmat. Q = A diag(d) Ainv and P= exp(Qt) <>= /* ** Compute the eigenvectors for the upper triangular matrix R */ #include #include "R.h" #include "Rinternals.h" SEXP cdecomp(SEXP R2, SEXP time2) { int i,j,k; int nc, ii; static const char *outnames[]= {"d", "A", "Ainv", "P", ""}; SEXP rval, stemp; double *R, *A, *Ainv, *P; double *dd, temp, *ediag; double time; nc = ncols(R2); /* number of columns */ R = REAL(R2); time = asReal(time2); /* Make the output matrices as copies of R, so as to inherit ** the dimnames and etc */ PROTECT(rval = mkNamed(VECSXP, outnames)); stemp= SET_VECTOR_ELT(rval, 0, allocVector(REALSXP, nc)); dd = REAL(stemp); stemp = SET_VECTOR_ELT(rval, 1, allocMatrix(REALSXP, nc, nc)); A = REAL(stemp); for (i =0; i< nc*nc; i++) A[i] =0; /* R does not zero memory */ stemp = SET_VECTOR_ELT(rval, 2, duplicate(stemp)); Ainv = REAL(stemp); stemp = SET_VECTOR_ELT(rval, 3, duplicate(stemp)); P = REAL(stemp); ediag = (double *) R_alloc(nc, sizeof(double)); /* ** Compute the eigenvectors ** For each column of R, find x such that Rx = kx ** The eigenvalue k is R[i,i], x is a column of A ** Remember that R is in column order, so the i,j element is in ** location i + j*nc */ ii =0; /* contains i * nc */ for (i=0; i=0; j--) { /* fill in the rest */ temp =0; for (k=j; k<=i; k++) temp += R[j + k*nc]* A[k +ii]; A[j +ii] = temp/(dd[i]- R[j + j*nc]); } ii += nc; } /* ** Solve for A-inverse, which is also upper triangular. The diagonal ** of A and the diagonal of A-inverse are both 1. At the same time ** solve for P = A D Ainverse, where D is a diagonal matrix ** with exp(eigenvalues) on the diagonal. ** P will also be upper triangular, and we can solve for it using ** nearly the same code as above. The prior block had RA = x with A the ** unknown and x successive colums of the identity matrix. ** We have PA = AD, so x is successively columns of AD. ** Imagine P and A are 4x4 and we are solving for the second row ** of P. Remember that P[2,1]= A[2,3] = A[2,4] =0; the equations for ** this row of P are: ** ** 0*A[1,2] + P[2,2]A[2,2] + P[2,3] 0 + P[2,4] 0 = A[2,2] D[2] ** 0*A[1,3] + P[2,2]A[2,3] + P[2,3]A[3,3] + P[2,4] 0 = A[2,3] D[3] ** 0*A[1,4] + P[2,2]A[2,4] + P[2,3]A[3,4] + P[2,4]A[4,4] = A[2,4] D[4] ** ** For A-inverse the equations are (use U= A-inverse for a moment) ** 0*A[1,2] + U[2,2]A[2,2] + U[2,3] 0 + U[2,4] 0 = 1 ** 0*A[1,3] + U[2,2]A[2,3] + U[2,3]A[3,3] + U[2,4] 0 = 0 ** 0*A[1,4] + U[2,2]A[2,4] + U[2,3]A[3,4] + U[2,4]A[4,4] = 0 */ ii =0; /* contains i * nc */ for (i=0; i=0; j--) { /* fill in the rest of the column*/ temp =0; for (k=j+1; k<=i; k++) temp += A[j + k*nc]* Ainv[k +ii]; Ainv[j +ii] = -temp; } /* column i of P */ P[i + ii] = ediag[i]; for (j=0; j>= derivative <- function(rmat, time, dR, setup, eps=1e-8) { if (missing(setup) || setup[1] <0 || any(diff(sort(diag(rmat)))< eps)) return (pade(rmat*time, dR*time)) if (setup==0) dlist <- .Call(Ccdecomp, rmat, time) else dlist <- .Call(Ccdecomp, rmat[setup, setup], time) ncoef <- dim(dR)[3] nstate <- nrow(rmat) dmat <- array(0.0, dim=c(nstate, nstate, ncoef)) vtemp <- outer(dlist$d, dlist$d, function(a, b) { ifelse(abs(a-b)< eps, time* exp(time* (a+b)/2), (exp(a*time) - exp(b*time))/(a-b))}) # two transitions can share a coef, but only for the same X variable for (i in 1:ncoef) { G <- dlist$Ainv %*% dR[,,i] %*% dlist$A V <- G*vtemp dmat[,,i] <- dlist$A %*% V %*% dlist$Ainv } dlist$dmat <- dmat # undo the reordering, if needed if (setup[1] >0) { indx <- order(setup) dlist <- list(P = dlist$P[indx, indx], dmat = apply(dmat,1:2, function(x) x[indx, indx])) } dlist } @ The Pade approximation is found in the file pade.R. There is a good discussion of the problem at www.maths.manchester.ac.uk/~higham/talks/exp09.pdf. The pade function copied code from the matexp package, which in turn is based on Higham 2005. Let B be a matrix and define \begin{eqnarray*} r_m(B) &= p(B)/q(B) \\ p(B) &= \sum_{j=0^m} \frac{((2m-j)! m!}{(2m)!(m-j)! j!} B^j \\ q(B) &= p(-B) \end{eqnarray*} The algorithm for calculating $\exp(A)$ is based on the following table \begin{center} \begin{tabular}{c|ccccc} $||A||_1$ & 0.15 & .25 & .95 & 2.1 & 3.4 \\ m & 3 & 5 & 7 & 9 & 13 \end{tabular} \end{center} The 1 norm of a matrix is \code{max(colSums(A))}. If the norm is $\le 3.4$ the $\exp(A) = r_m(A)$ using the table. Otherwise, find $s$ such that $B = A/2^s$ has norm $<=3.4$ and use the table method to find $\exp(B)$, then $\exp(A) \approx B^(2^s)$, the latter involves repeated squaring of the matrix. The expm code has a lot of extra steps whose job is to make sure that elements of $A$ are not too disparate in size. Transition matrices are nice and we can skip all of that. This makes the pade function conserably faster than the expm function from the Matrix library. In fact, if there aren't any tied event times, most elements of the rate matrix will be zero, and others are on the order of 1/(number at risk), so that $m=3$ is the most common outcome. survival/noweb/cch.Rnw0000644000176200001440000002327413537676563014521 0ustar liggesusers\section{Case-cohort function} This function was originally written by Norman Breslow, then adapted to the survival library by Thomas Lumley. Poor interaction with the \code{aeqSurv} function prompted a refactoring of the code. The method is an ordinary Cox model coupled with modification of the input data along with additional computation for the variance. A case-cohort study begins with a cohort of interest for which complete sampling is infeasable. A random subcohort is chosen for follow up, and then that subcohort is augmented by any subjects for whom an event occurs. Let $n$ be the number in the study and $m$ the number who were sampled. Consider an event at day 100 who was not part of the subcohort. Who is the risk set for this event? \begin{itemize} \item The Prentice estimate uses as a risk set all the subcohort members at risk on day 100 + this failure. To accomplish this we use (start,stop] data, and make the non-subcohort event a very short interval of ($100 - \epsilon$, 100]. The value of epsilon should be small enough that no other events fall in the interval, but large enough to preclude round off error. \item The Self-Prentice estimate was developed in a later paper on the asymptotic variance. For this the risk set does not include the non-subcohrt failure. This can be effectively accomplished by giving non-sucohort failures an offset of -100. They are still in the risk set, but with an effective weight of $\exp(-100) < 10^{-40}$ so have no effect. \item The Yin-Ling method leaves everyone in the sample, but reweights the non-events. Let $n$ be the number of non-events in the cohort and $m$ the non-events in the subcohort, and reweight all non-events by $n/m$. The events have weight 1, since they will always be included. This is a simple survey sampling correction. \item Borgan et. al. considered estimates for the case where stratified sampling has been done, with one or more of the strata oversampled. Their estimator I is the Self-Prentice, but with a case weight of $n_k/m_k$ for subcohort members in stratum $k$. Each event is compared to a population averge covariate rather than a sample average covariate. Estimator II is the same, but using the Prentice estimate; non-subcohort events have a weight of 1. \item Borgan et. al. consider several other estimators which are not included here. Method III uses subsampling, and there are variants of I and II that use time dependent stratum weights. \end{itemize} The historical input arguments are a bit of a mess: the three that could be part of a data frame were represented as one-sided formulas if they were part of the data frame, and simple expressions otherwise. This precludes the use of an na.action or subset argument. To rectify that make these part of the standard model.frame processing, We need to find out if the user handed a formula to us, but without evaluating them. If any of them are, then make sure it is a legal formula of the form \code{\textasciitilde x} for some single variable, and then replace the formula with the variable name. <>= cch <- function(formula, data, weights, subset, na.action, subcoh, id, stratum, cohort.size, method=c("Prentice", "SelfPrentice", "LinYing", "I.Borgan", "II.Borgan"), robust = FALSE, control, ...) { method <- match.arg(method) Call <- match.call() if (missing(control)) control <- coxph.control(...) for (i in c("subcoh", "id", "stratum")) { if (inherits(Call[[i]], 'formula')) { if (length(Call[[i]]) != 2 || !is.name(Call[[i]][[2]]) stop("a formula used for ", i, "must have a single variable and no response") Call[[i]] <- Call[[i]][[2]] } } # Grab the data. This is identical to coxph, but with 3 # more matching arguments indx <- match(c("formula", "data", "weights", "subset", "na.action", "subcoh", "id", "stratum"), names(Call), nomatch=0) if (indx[1] ==0) stop("a formula argument is required") temp <- Call[c(1,indx)] # only keep the arguments we wanted temp[[1L]] <- quote(stats::model.frame) # change the function called special <- c("strata", "cluster") temp$formula <- if(missing(data)) terms(formula, special) else terms(formula, special, data=data) mf <- eval(temp, parent.frame()) if (nrow(mf) ==0) stop("No (non-missing) observations") Terms <- terms(mf) Y <- model.extract(mf, "response") if (!inherits(Y, "Surv")) stop("Response must be a survival object") type <- attr(Y, "type") if (type!='right' && type!='counting') stop(paste("Cox model doesn't support \"", type, "\" survival data", sep='')) if (control$timefix) Y <- aeqSurv(Y) <> strats <- attr(Terms, "specials")$strata if (length(strats)) { stemp <- untangle.specials(Terms, 'strata', 1) if (length(stemp$vars)==1) strata.keep <- mf[[stemp$vars]] else strata.keep <- strata(mf[,stemp$vars], shortlabel=TRUE) strats <- as.numeric(strata.keep) } cluster<- attr(Terms, "specials")$cluster if (length(cluster)) { robust <- TRUE #flag to later compute a robust variance tempc <- untangle.specials(Terms, 'cluster', 1:10) ord <- attr(Terms, 'order')[tempc$terms] if (any(ord>1)) stop ("Cluster can not be used in an interaction") cluster <- strata(mf[,tempc$vars], shortlabel=TRUE) #allow multiples dropterms <- tempc$terms #we won't want this in the X matrix # Save away xlevels after removing cluster (we don't want to save upteen # levels of that variable, which we will never need). xlevels <- .getXlevels(Terms[-tempc$terms], mf) } else { dropterms <- NULL if (missing(robust)) robust <- FALSE xlevels <- .getXlevels(Terms, mf) } <> <> <> <> <> } @ Now we have the main ingredients. The first task is to see if any of the \code{id, subcoh, or stratum} arguments were present. The \code{stratum} argument is not the same as the strata term in the model. The asymptotic variance formula requires knowlege of $n$, which is found in the \code{cohort.size} argument, the robust variance does not require this. <>= n <- nrow(Y) id <- model.extract(mf, "") subcoh <- model.extract(mf, "") stratum <- model.extract(mf, "") weight <- model.weights(mf) if (length(weight)==0)) weight <- rep(1.0, n) offset <- model.offset(mf) if (length(offset) ==0) { has.offset <- FALSE offset <- rep(0., n) } else has.offset <- TRUE status <- Y[,ncol(Y)] if (is.null(subcoh)) stop("a subcoh argument is required") else { if (is.logical(subcoh)) subcoh <- as.numeric(subcoh) else if (!is.numeric(subcoh)) stop("subcoh must be numeric or logical") else if (any(subcoh!=0 & subcoh !=1)) stop("numeric subcoh values must be 0 or 1") if (any(status==0 & subcoh==0)) stop("all observations outside the subcohort must be events") } if (length(stratum) > 0) { if (missing(cohort.size)) stop("the estimates for stratified sampling require cohort.size") scount <- table(stratum) indx <- match(names(cohort.size), names(scount)) phat <- scount/cohort.size[indx] if (any(is.na(phat))) stop("no cohort.size element found for strata", (names(scount))[is.na(phat)]) if (any(phat <=0 | phat >1)) stop("strata sampling fraction that is <=0 or > 1") windex <- match(stratum, names(scount)) weight[status==0] <- (weight /phat[windex])[status==0] } @ The computation has 3 branches, Prentice, Self-Prentice, and Lin-Ying. <>= if (ncol(Y) ==2) { etime <- Y[,1] status <- Y[,2] } else { etime <- Y[,2] status <- Y[,3] } if (method=="Prentice" || method=="II.Borgan") { # construct fake entry times for the non-cohort delta <- min(diff(sort(unique(etime[status==1])))) # min time between times fake <- etime[subco==0] - delta/2 if (ncol(Y) ==2) { temp <- rep(0., n) temp[subco==0] <- fake Y <- cbind(temp, Y) } else Y[subco==0, 1] <- fake fit <- agreg.fit(X, Y, strata, offset, init, control, weights, method, rownames) } else if (method== "Self-Prentice" || method= "I.Borgan") { # Use an offset offset[subco==1] <- offset[subco==0] - 100 if (ncol(Y) ==2) fit <- coxph.fit(X, Y, strata, offset, init, control, weights, method, rownames) else fit <- coxph.fit(X, Y, strata, offset, init, control, weights, method, rownames) } else { # Lin-Ying method if (missing(cohort.size)) stop("Lin-Ying method requires the cohort size") else if (!numeric(cohort.size)) stop("cohort size must be numeric") else if (length(cohort.size) > 1) stop("cohort size must be numeric, with one value per stratum") nd <- sum(status) # number of events nc <- sum(subcoh) # number in subcohort ncd <- sum(status*subcoh) # number of events in subcohort lyweight <- (cohort.size - nd)/(nc - ncd) weight[status==0] <- weight[status==0]* lyweight if (ncol(Y) ==2) fit <- coxph.fit(X, Y, strata, offset, init, control, weights, method, rownames) else fit <- coxph.fit(X, Y, strata, offset, init, control, weights, method, rownames) } @ There are two possible variances for the estimate. The asymptotic variance survival/noweb/finegray.Rnw0000644000176200001440000002630013773501663015547 0ustar liggesusers\section{The Fine-Gray model} For competing risks with ending states 1, 2, \ldots $k$, the Fine-Gray approach turns these into a set of simple 2-state Cox models: \begin{itemize} \item (not yet in state 1) $\longrightarrow$ state 1 \item (not yet in state 2) $\longrightarrow$ state 2 \item \ldots \end{itemize} Each of these is now a simple Cox model, assuming that we are willing to make a proportional hazards assumption. There is one added complication: when estimating the first model, one wants to use the data set that would have occured if the subjects being followed for state 1 had not had an artificial censoring, that is, had continued to be followed for event 1 even after event 2 occured. Sometimes this can be filled in directly, e.g., if we knew the enrollment dates for each subject along with the date that follow-up for the study was terminated, and there was no lost to follow-up (only administrative censoring.) An example is the mgus2 data set, where follow-up for death continued after the occurence of plasma cell malignancy. In practice what is done is to estimate the overall censoring distribution and give subjects artificial follow-up. The function below creates a data set that can then be used with coxph. <>= finegray <- function(formula, data, weights, subset, na.action= na.pass, etype, prefix="fg", count="", id, timefix=TRUE) { Call <- match.call() indx <- match(c("formula", "data", "weights", "subset", "id"), names(Call), nomatch=0) if (indx[1] ==0) stop("A formula argument is required") temp <- Call[c(1,indx)] # only keep the arguments we wanted temp$na.action <- na.action temp[[1L]] <- quote(stats::model.frame) # change the function called special <- c("strata", "cluster") temp$formula <- if(missing(data)) terms(formula, special) else terms(formula, special, data=data) mf <- eval(temp, parent.frame()) if (nrow(mf) ==0) stop("No (non-missing) observations") Terms <- terms(mf) Y <- model.extract(mf, "response") if (!inherits(Y, "Surv")) stop("Response must be a survival object") type <- attr(Y, "type") if (type!='mright' && type!='mcounting') stop("Fine-Gray model requires a multi-state survival") nY <- ncol(Y) states <- attr(Y, "states") if (timefix) Y <- aeqSurv(Y) strats <- attr(Terms, "specials")$strata if (length(strats)) { stemp <- untangle.specials(Terms, 'strata', 1) if (length(stemp$vars)==1) strata <- mf[[stemp$vars]] else strata <- survival::strata(mf[,stemp$vars], shortlabel=TRUE) istrat <- as.numeric(strata) mf[stemp$vars] <- NULL } else istrat <- rep(1, nrow(mf)) id <- model.extract(mf, "id") if (!is.null(id)) mf["(id)"] <- NULL # don't leave it in result user.weights <- model.weights(mf) if (is.null(user.weights)) user.weights <- rep(1.0, nrow(mf)) cluster<- attr(Terms, "specials")$cluster if (length(cluster)) { stop("a cluster() term is not valid") } # If there is start-stop data, then there needs to be an id # also check that this is indeed a competing risks form of data. # Mark the first and last obs of each subject, as we need it later. # Observations may not be in time order within a subject delay <- FALSE # is there delayed entry? if (type=="mcounting") { if (is.null(id)) stop("(start, stop] data requires a subject id") else { index <- order(id, Y[,2]) # by time within id sorty <- Y[index,] first <- which(!duplicated(id[index])) last <- c(first[-1] -1, length(id)) if (any(sorty[-last, 3] != 0)) stop("a subject has a transition before their last time point") delta <- c(sorty[-1,1], 0) - sorty[,2] if (any(delta[-last] !=0)) stop("a subject has gaps in time") if (any(Y[first,1] > min(Y[,2]))) delay <- TRUE temp1 <- temp2 <- rep(FALSE, nrow(mf)) temp1[index[first]] <- TRUE temp2[index[last]] <- TRUE first <- temp1 #used later last <- temp2 } } else last <- rep(TRUE, nrow(mf)) if (missing(etype)) enum <- 1 #generate a data set for which endpoint? else { index <- match(etype, states) if (any(is.na(index))) stop ("etype argument has a state that is not in the data") enum <- index[1] if (length(index) > 1) warning("only the first endpoint was used") } # make sure count, if present is syntactically valid if (!missing(count)) count <- make.names(count) else count <- NULL oname <- paste0(prefix, c("start", "stop", "status", "wt")) <> <> } @ The censoring and truncation distributions are \begin{align*} G(t) &= \prod_{s \le t} \left(1 - \frac{c(s)}{r_c(s)} \right ) \\ H(t) &= \prod_{s > t} \left(1 - \frac{e(s)}{r_e(s)} \right ) \end{align*} where $c(t)$ is the number of subjects censored at time $t$, $e(t)$ is the number who enter at time $t$, and $r$ is the size of the relevant risk set. These are equations 5 and 6 of Geskus (Biometrics 2011). Note that both $G$ and $H$ are right continuous functions. For tied times the assumption is that event $<$ censor $<$ entry. For $G$ we use a modified Kapan-Meier where any events at censoring time $t$ are removed from the risk set just before time $t$. To avoid issues with times that are nearly identical (but not quite) we first convert to an integer time scale, and then move events backwards by .2. Since this is a competing risks data set any non-censored observation for a subject is their last, so this time shift does not goof up the alignment of start, stop data. For the truncation distribution it is the subjects with times at or before time $t$ that are in the risk set $r_e(t)$ for truncation at (or before) $t$. $H$ can be calculated using an ordinary KM on the reverse time scale. When there is (start,stop) data and hence multiple observations per subject, calculation of $G$ needs use a status that is 1 only for the \emph{last} row row of a censored subject. <>= if (ncol(Y) ==2) { temp <- min(Y[,1], na.rm=TRUE) if (temp >0) zero <- 0 else zero <- 2*temp -1 # a value less than any observed y Y <- cbind(zero, Y) # add a start column } utime <- sort(unique(c(Y[,1:2]))) # all the unique times newtime <- matrix(findInterval(Y[,1:2], utime), ncol=2) status <- Y[,3] newtime[status !=0, 2] <- newtime[status !=0,2] - .2 Gsurv <- survfit(Surv(newtime[,1], newtime[,2], last & status==0) ~ istrat, se.fit=FALSE) @ The calculation for $H$ is also done on the integer scale. Otherwise we will someday be clobbered by times that differ only in round off error. The only nuisance is the status variable, which is 1 for the first row of each subject, since the data set may not be in sorted order. The offset of .2 used above is not needed, but due to the underlying integer scale it doesn't harm anything either. Reversal of the time scale leads to a left continuous function which we fix up later. <>= if (delay) Hsurv <- survfit(Surv(-newtime[,2], -newtime[,1], first) ~ istrat, se.fit =FALSE) @ Consider the following data set: \begin{itemize} \item Events of type 1 at times 1, 4, 5, 10 \item Events of type 2 at times 2, 5, 8 \item Censors at times 3, 4, 4, 6, 8, 9, 12 \end{itemize} The censoring distribution will have the following shape: \begin{center} \begin{tabular}{rcccccc} interval& (0,3]& (3,4] & (4,6] & (6,8] & (8,12] & 12+\\ C(t) & 1 &11/12 & (11/12)(8/10) & (11/15)(5/6)& (11/15)(5/6)(3/4)& 0 \\ & 1.0000 & .9167 & .7333 & .6111 & .4583 \end{tabular} \end{center} Notice that the event at time 4 is not counted in the risk set at time 4, so the jump is 8/10 rather than 8/11. Likewise at time 8 the risk set has 4 instead of 5: censors occur after deaths. When creating the data set for event type 1, subjects who have an event of type 2 get extended out using this censoring distribution. The event at time 2, for instance, appears as a censored observation with time dependent weights of $G(t)$. The type 2 event at time 5 has weight 1 up through time 5, then weights of $G(t)/C(5)$ for the remainder. This means a weight of 1 over (5,6], 5/6 over (6,8], (5/6)(3/4) over (9,12] and etc. Though there are 6 unique censoring intervals, in the created data set for event type 1 we only need to know case weights at times 1, 4, 5, and 10; the information from the (4,6] and (6,8] intervals will never be used. To create a minimal sized data set we can leave those intervals out. $G(t)$ only drops to zero if the largest time(s) are censored observations, so by definition no events lie in an interval with $G(t)=0$. If there is delayed entry, then the set of intervals is larger due to a merge with the jumps in Hsurv. The truncation distribution Hsurv ($H$) will become 0 at the first entry time; it is a left continuous function whereas Gsurv ($G$) is right continuous. We can slide $H$ one point to the left and merge them at the jump points. <>= status <- Y[, 3] # Do computations separately for each stratum stratfun <- function(i) { keep <- (istrat ==i) times <- sort(unique(Y[keep & status == enum, 2])) #unique event times if (length(times)==0) return(NULL) #no events in this stratum tdata <- mf[keep, -1, drop=FALSE] maxtime <- max(Y[keep, 2]) Gtemp <- Gsurv[i] if (delay) { Htemp <- Hsurv[i] dtime <- rev(-Htemp$time[Htemp$n.event > 0]) dprob <- c(rev(Htemp$surv[Htemp$n.event > 0])[-1], 1) ctime <- Gtemp$time[Gtemp$n.event > 0] cprob <- c(1, Gtemp$surv[Gtemp$n.event > 0]) temp <- sort(unique(c(dtime, ctime))) # these will all be integers index1 <- findInterval(temp, dtime) index2 <- findInterval(temp, ctime) ctime <- utime[temp] cprob <- dprob[index1] * cprob[index2+1] # G(t)H(t), eq 11 Geskus } else { ctime <- utime[Gtemp$time[Gtemp$n.event > 0]] cprob <- Gtemp$surv[Gtemp$n.event > 0] } ct2 <- c(ctime, maxtime) cp2 <- c(1.0, cprob) index <- findInterval(times, ct2, left.open=TRUE) index <- sort(unique(index)) # the intervals that were actually seen # times before the first ctime get index 0, those between 1 and 2 get 1 ckeep <- rep(FALSE, length(ct2)) ckeep[index] <- TRUE expand <- (Y[keep, 3] !=0 & Y[keep,3] != enum & last[keep]) #which rows to expand split <- .Call(Cfinegray, Y[keep,1], Y[keep,2], ct2, cp2, expand, c(TRUE, ckeep)) tdata <- tdata[split$row,,drop=FALSE] tstat <- ifelse((status[keep])[split$row]== enum, 1, 0) tdata[[oname[1]]] <- split$start tdata[[oname[2]]] <- split$end tdata[[oname[3]]] <- tstat tdata[[oname[4]]] <- split$wt * user.weights[split$row] if (!is.null(count)) tdata[[count]] <- split$add tdata } if (max(istrat) ==1) result <- stratfun(1) else { tlist <- lapply(1:max(istrat), stratfun) result <- do.call("rbind", tlist) } rownames(result) <- NULL #remove all the odd labels that R adds attr(result, "event") <- states[enum] result @ survival/noweb/survfitms.Rnw0000644000176200001440000007276114073035617016014 0ustar liggesusers\subsubsection{Printing and plotting} The \code{survfitms} class differs from a \code{survfit}, but many of the same methods nearly apply. <>= # Methods for survfitms objects <> <> @ The subscript method is a near copy of that for survfit objects, but with a slightly different set of components. The object could have strata and will almost always have multiple columns. Following convention, if there is only one subscript we treat the object as though it were a vector. The \code{nmatch} function allow the user to use either names or integer indices. <>= "[.survfitms" <- function(x, ..., drop=FALSE) { nmatch <- function(i, target) { # This function lets R worry about character, negative, # or logical subscripts # It always returns a set of positive integer indices temp <- seq(along.with=target) names(temp) <- target temp[i] } if (!is.null(x$influence.pstate) || !is.null(x$influence.cumhaz)) x <- survfit0(x, x$start.time) # make influence and pstate align ndots <- ...length() # the simplest, but not avail in R 3.4 # ndots <- length(list(...))# fails if any are missing, e.g. fit[,2] # ndots <- if (missing(drop)) nargs()-1 else nargs()-2 # a workaround dd <- dim(x) dmatch <- match(c("strata", "data", "states"), names(dd), nomatch=0) if (is.null(x$states)) stop("survfitms object has no states component") if (dmatch[3]==0) stop ("survfitms object has no states dimension") dtype <- match(names(dd), c("strata", "data", "states")) if (ndots==0) return(x) # no subscript given if (ndots >0 && !missing(..1)) i <- ..1 else i <- NULL if (ndots> 1 && !missing(..2)) j <- ..2 else j <- NULL if (ndots> 2 && !missing(..3)) k <- ..3 else k <- NULL if (is.null(i) & is.null(j) & is.null(k)) return(x) # only one curve # Make a new object newx <- vector("list", length(x)) names(newx) <- names(x) for (kk in c("logse", "version", "conf.int", "conf.type", "type", "start.time", "call")) if (!is.null(x[[kk]])) newx[[kk]] <- x[[kk]] newx$transitions <- NULL # may no longer be accurate, and not needed class(newx) <- class(x) # Like a matrix, let the user use a single subscript if they desire if (ndots==1 && length(dd) > 1) { # the 'treat it as a vector' case if (!is.numeric(i)) stop("single subscript must be numeric") if (any(dmatch==2)) stop("single index subscripts are not supported for a survfit objet with both data and state dimesions") # when subscripting a mix, these don't endure newx$cumhaz <- newx$std.chaz <- newx$influence.chaz <- NULL newx$transitions <- newx$states <- newx$newdata <- NULL # what strata and columns do I need? itemp <- matrix(1:prod(dd), nrow=dd[1]) jj <- (col(itemp))[i] # columns ii <- (row(itemp))[i] # this is now the strata id if (dtype[1]!=1 || dd[1]==1) # no strata or only 1 irow <- rep(seq(along.with= x$time), length(ii)) else { itemp2 <- split(1:sum(x$strata), rep(1:length(x$strata), x$strata)) irow <- unlist(itemp2[ii]) # rows of the pstate object } inum <- x$strata[ii] # number of rows in each ii indx <- cbind(irow, rep(jj,ii)) # matrix index for pstate # The n.risk, n.event, .. matrices dont have a newdata dimension. if (all(dtype!=2) || dd["data"]==1) kk <- jj else { # both data and states itemp <- matrix(1:(dd["data"]*dd["states"]), nrow=dd[2]) kk <- (col(itemp))[jj] # the state of each selected one indx2 <- cbind(irow, rep(k, irow)) } newx$n <- x$n[ii] newx$time <- x$time[irow] for (z in c("n.risk", "n.event", "n.censor", "n.enter")) if (!is.null(x[[z]])) newx[[z]] <- (x[[z]])[indx2] for (z in c("pstate", "std.err", "upper", "lower")) if (!is.null(x[[z]])) newx[[z]] <- (x[[z]])[indx] newx$strata <- x$strata[ii] names(newx$strata) <- seq(along.with=ii) return(newx) } # not a single subscript, i.e., the usual case # Backwards compatability: If x$strata=NULL, it is a semantic argument # of whether there is still "1 stratum". I have used the second # form at times, e.g. x[1,,2] for an object with only data and state # dimensions. # If there are no strata, 1 too many subscripts, and the first is 1, # assume this case and toss the first if (ndots == (length(dd)+1)) { if (is.null(x$strata) && (is.null(i) || (length(i)==1 && i==1))) { i <-j; j <-k; k <- NULL } else stop("incorrect number of dimensions") } else if (ndots != length(dd)) stop("incorrect number of dimensions") # create irow, which selects for the time dimension of x if (dtype[1]!=1 || is.null(i)) { irow <- seq(along.with= x$time) } else { i <- nmatch(i, names(x$strata)) itemp <- split(1:sum(x$strata), rep(1:length(x$strata), x$strata)) irow <- unlist(itemp[i]) # rows of the pstate object } # Select the n, strata, and time components of the output. Make j,k # point to the subscripts other than strata (makes later code a touch # simpler.) newx$time <- x$time[irow] if (dtype[1] !=1) { # there are no strata newx$n <- x$n k <- j; j <- i; dd <- c(0, dd) dtype <- c(1, dtype) } else { # there are strata if (is.null(i)) i <-seq(along.with=x$strata) if ((drop && length(i)>1) || !drop) newx$strata <- x$strata[i] newx$n <- x$n[i] } # The n.censor and n.enter values do not repeat with multiple X values for (z in c("n.censor", "n.enter")) if (!is.null(x[[z]])) newx[[z]] <- (x[[z]])[irow, drop=FALSE] # two cases: with newx or without newx (pstate is always present) nstate <- length(x$states) if (dtype[2] !=2) { # j indexes the states, there is no data dimension if (is.null(j)) j <- seq.int(nstate) else j <- nmatch(j, x$states) # keep these as start points for plotting, even though they won't make # true sense if states are subset, since rows won't sum to 1 if (!is.null(x$p0)) { if (is.matrix(x$p0)) newx$p0 <- x$p0[i,j, drop=FALSE] else newx$p0 <- x$p0[j] } if (!is.null(x$sp0)) { if (is.matrix(x$sp0)) newx$sp0 <- x$sp0[i,j, drop=FALSE] else newx$sp0 <- x$sp0[j] } # in the rare case of a single strata with 1 obs, don't drop dims if (length(irow)==1 && length(j) > 1) drop2 <- FALSE else drop2 <- drop for (z in c("n.risk", "n.event")) if (!is.null(x[[z]])) newx[[z]] <- (x[[z]])[irow,j, drop=drop2] for (z in c("pstate", "std.err", "upper", "lower")) if (!is.null(x[[z]])) newx[[z]] <- (x[[z]])[irow,j, drop=drop2] if (!is.null(x$influence.pstate)) { if (is.list(x$influence.pstate)) { if (length(i)==1) newx$influence.pstate <- x$influence.pstate[[i]] else newx$influence.pstate <- lapply(x$influence.pstate[i], function(x) x[,,j, drop= drop]) } else newx$influence.pstate <- x$influence.pstate[,,j, drop=drop] } if (length(j)== nstate && all(j == seq.int(nstate))) { # user kept all the states, in original order newx$states <- x$states for (z in c("cumhaz", "std.chaz")) if (!is.null(x[[z]])) newx[[z]] <- (x[[z]])[irow,, drop=drop2] if (!is.null(x$influence.chaz)) { if (is.list(x$influence.chaz)) { newx$influence.chaz <- x$influence.chaz[i] if (length(i)==1 && drop) newx$influence.chaz <- x$influence.chaz[[i]] } else newx$influence.chaz <- x$influence.chaz } } else { # Some states were dropped, leaving no consistent way to # subscript cumhaz, or not one I have yet seen clearly # So remove it from the object newx$cumhaz <- newx$std.chaz <- newx$influence.chaz <- NULL if (length(j)==1 & drop) { newx$states <- NULL temp <- class(newx) class(newx) <- temp[temp!="survfitms"] } else newx$states <- x$states[j] } } else { # j points at newdata, k points at states if (is.null(j)) j <- seq.int(dd[2]) else j <- nmatch(j, seq.int(dd[2])) if (is.null(k)) k <- seq.int(nstate) else k <- nmatch(k, x$states) # keep these as start points for plotting, even though they won't make # true sense is states are subset, since rows won't sum to 1 # (all data= sets have the same p0) if (!is.null(x$p0)) { if (is.matrix(x$p0)) newx$p0 <- x$p0[i,k] else newx$p0 <- x$p0[k] } if (!is.null(x$sp0)) { if (is.matrix(x$sp0)) newx$sp0 <- x$p0[i,k] else newx$sp0 <- x$sp0[k] } if (length(irow)==1) { if (length(j) > 1) drop2 <- FALSE else drop2<- drop if (length(k) > 1) drop3 <- FALSE else drop3 <- drop } else drop2 <- drop3 <- drop for (z in c("n.risk", "n.event")) if (!is.null(x[[z]])) newx[[z]] <- (x[[z]])[irow, k, drop=drop3] for (z in c("pstate", "std.err", "upper", "lower")) if (!is.null(x[[z]])) newx[[z]] <- (x[[z]])[irow,j,k, drop=drop2] if (!is.null(x$influence.pstate)) { if (is.list(x$influence.pstate)) { if (length(i)==1) newx$influence.pstate <- (x$influence.pstate[[i]])[,,j,k, drop=drop] else newx$influence.pstate <- lapply(x$influence.pstate[i], function(x) x[,,j,k, drop= drop]) } else newx$influence.pstate <- x$influence.pstate[,,j,k, drop=drop] } if (length(k)== nstate && all(k == seq.int(nstate))) { # user kept all the states newx$states <- x$states for (z in c("cumhaz", "std.chaz")) if (!is.null(x[[z]])) newx[[z]] <- (x[[z]])[irow,j,, drop=drop2] if (!is.null(x$influence.chaz)) { if (is.list(x$influence.chaz)) { newx$influence.chaz <- (x$influence.chaz[i])[,j,] if (length(i)==1 && drop) newx$influence.chaz <- x$influence.chaz[[i]] } else newx$influence.chaz <- x$influence.chaz[,j,] } } else { # never drop the states component. Otherwise downstream code # will start looking for x$surv instead of x$pstate newx$states <- x$states[k] newx$cumhaz <- newx$std.chaz <- newx$influence.chaz <- NULL x$transitions <- NULL } if (length(j)==1 && drop) newx$newdata <- NULL else newx$newdata <- x$newdata[j,,drop=FALSE] #newdata is a data frame } newx } @ The summary.survfit and summary.survfitms functions share a significant amount of code. One part of the code that once was subtle is dealing with intermediate time points; the findInterval function in base R has made that much easier. Since the result does not involve interpolation, one should be able to create a special index vector i and return \code{time[i]}, \code{surv[i,]}, etc, to subscript all the curves in a survfit object at once. But that approach, though efficient in theory, runs into two problems. First is the extrapolated value for the curves at time points before the first event, which is allowed to be different for different curves in survfitms objects. The second is that there is interpolation of a sort: the n.event and n.censor components are summed over intervals when the selected time points are sparse, and that process is very tricky for multiple curves at once. At one point the code took that approach, but it became too complex to maintain. The current approach is slower but more transparent: do the individual curves one by one, then paste together the results. <>= summary.survfit <- function(object, times, censored=FALSE, scale=1, extend=FALSE, rmean=getOption('survfit.rmean'), ...) { fit <- object #save typing if (!inherits(fit, 'survfit')) stop("summary.survfit can only be used for survfit objects") if (is.null(fit$logse)) fit$logse <- TRUE #older style # The print.rmean option is depreciated, it is still listened # to in print.survfit, but ignored here if (is.null(rmean)) rmean <- "common" if (is.numeric(rmean)) { if (is.null(fit$start.time)) { if (rmean < min(fit$time)) stop("Truncation point for the mean time in state is < smallest survival") } else if (rmean < fit$start.time) stop("Truncation point for the mean time in state is < smallest survival") } else { rmean <- match.arg(rmean, c('none', 'common', 'individual')) if (length(rmean)==0) stop("Invalid value for rmean option") } # adding time 0 makes the mean and median easier fit0 <- survfit0(fit, fit$start.time) #add time 0 temp <- survmean(fit0, scale=scale, rmean) table <- temp$matrix #for inclusion in the output list rmean.endtime <- temp$end.time if (!is.null(fit$strata)) { nstrat <- length(fit$strata) } delta <- function(x, indx) { # sums between chosen times if (is.logical(indx)) indx <- which(indx) if (!is.null(x) && length(indx) >0) { fx <- function(x, indx) diff(c(0, c(0, cumsum(x))[indx+1])) if (is.matrix(x)) { temp <- apply(x, 2, fx, indx=indx) # don't return a vector when only 1 time point is given if (is.matrix(temp)) temp else matrix(temp, nrow=1) } else fx(x, indx) } else NULL } if (missing(times)) { <> } else { fit <- fit0 <> times <- sort(times) #in case the user forgot if (is.null(fit$strata)) fit <- findrow(fit, times, extend) else { ltemp <- vector("list", nstrat) for (i in 1:nstrat) ltemp[[i]] <- findrow(fit[i], times, extend) fit <- unpacksurv(fit, ltemp) } } # finish off the output structure fit$table <- table if (length(rmean.endtime)>0 && !any(is.na(rmean.endtime[1]))) fit$rmean.endtime <- rmean.endtime # A survfit object may contain std(log S) or std(S), summary always std(S) if (!is.null(fit$std.err) && fit$logse) fit$std.err <- fit$std.err * fit$surv # Expand the strata if (!is.null(fit$strata)) fit$strata <- factor(rep(1:nstrat, fit$strata), 1:nstrat, labels= names(fit$strata)) if (scale != 1) { # fix scale in the output fit$time <- fit$time/scale } class(fit) <- "summary.survfit" fit } @ The simple case of no times argument. <>= if (!censored) { index <- (rowSums(as.matrix(fit$n.event)) >0) for (i in c("time","n.risk", "n.event", "surv", "pstate", "std.err", "upper", "lower", "cumhaz", "std.chaz")) { if (!is.null(fit[[i]])) { # not all components in all objects temp <- fit[[i]] if (is.matrix(temp)) temp <- temp[index,,drop=FALSE] else if (!is.array(temp)) temp <- temp[index] #simple vector else temp <- temp[index,,, drop=FALSE] # 3 way fit[[i]] <- temp } } # The n.enter and n.censor values are accumualated # both of these are simple vectors if (is.null(fit$strata)) { for (i in c("n.enter", "n.censor")) if (!is.null(fit[[i]])) fit[[i]] <- delta(fit[[i]], index) } else { sindx <- rep(1:nstrat, fit$strata) for (i in c("n.enter", "n.censor")) { if (!is.null(fit[[i]])) fit[[i]] <- unlist(sapply(1:nstrat, function(j) delta(fit[[i]][sindx==j], index[sindx==j]))) } # the "factor" is needed for the case that a strata has no # events at all, and hence 0 lines of output fit$strata[] <- as.vector(table(factor(sindx[index], 1:nstrat))) } } #if missing(times) and censored=TRUE, the fit object is ok as it is @ To deal with selected times we first define a subscripting function. For indices of 0, which are requested times that are before the first event, it fills in the initial value. <>= ssub<- function(x, indx) { #select an object and index if (!is.null(x) && length(indx)>0) { if (is.matrix(x)) x[pmax(1,indx),,drop=FALSE] else if (is.array(x)) x[pmax(1,indx),,,drop=FALSE] else x[pmax(1, indx)] } else NULL } @ This function does the real work, for any single curve. The default value for init is correct for survival curves. Say that the data has values at time 5, 10, 15, 20 \ldots, and a user asks for \code{times=c(7, 15, 20, 30)}. In the input object \code{n.risk} refers to the number at risk just before time 5, 10, \ldots; it is a left-continuous function. The survival is a right-continuous function. So at time 7 we want to take the survival from time 5 and number at risk from time 10; \code{indx1} will be the right-continuous index and \code{indx2} the left continuous one. The value of n.risk at time 30 has to be computed. For counts of events, censoring, and entry we want to know the total number that happened during the intervals of 0-7, 7-15, 15-20 and 20-30. Technically censorings at time 15 happen just after time 15 so would go into the third line of the report. However, this would lead to terrible confusion for the user since using \code{times=c(5, 10, 15, 20)} would lead to different counts than a call that did not contain the times argument, so all 3 of the intermediates are computed using indx1. A report at time 30 is made only if extend=TRUE, in which case we need to compute a tail value for n.risk. <>= findrow <- function(fit, times, extend) { if (FALSE) { if (is.null(fit$start.time)) mintime <- min(fit$time, 0) else mintime <- fit$start.time ptimes <- times[times >= mintime] } else ptimes <- times[is.finite(times)] if (!extend) { maxtime <- max(fit$time) ptimes <- ptimes[ptimes <= maxtime] } ntime <- length(fit$time) index1 <- findInterval(ptimes, fit$time) index2 <- 1 + findInterval(ptimes, fit$time, left.open=TRUE) if (length(index1) ==0) stop("no points selected for one or more curves, consider using the extend argument") # The pmax() above encodes the assumption that n.risk for any # times before the first observation = n.risk at the first obs fit$time <- ptimes for (i in c("surv", "pstate", "upper", "lower", "std.err", "cumhaz", "std.chaz")) { if (!is.null(fit[[i]])) fit[[i]] <- ssub(fit[[i]], index1) } if (is.matrix(fit$n.risk)) { # Every observation in the data has to end with a censor or event. # So by definition the number at risk after the last observed time # value must be 0. fit$n.risk <- rbind(fit$n.risk,0)[index2,,drop=FALSE] } else fit$n.risk <- c(fit$n.risk, 0)[index2] for (i in c("n.event", "n.censor", "n.enter")) fit[[i]] <- delta(fit[[i]], index1) fit } # For a single component, turn it from a list into a single vector, matrix # or array unlistsurv <- function(x, name) { temp <- lapply(x, function(x) x[[name]]) if (is.vector(temp[[1]])) unlist(temp) else if (is.matrix(temp[[1]])) do.call("rbind", temp) else { # the cumulative hazard is the only component that is an array # it's third dimension is n xx <- unlist(temp) dd <- dim(temp[[1]]) dd[3] <- length(xx)/prod(dd[1:2]) array(xx, dim=dd) } } # unlist all the components built by a set of calls to findrow # and remake the strata unpacksurv <- function(fit, ltemp) { keep <- c("time", "surv", "pstate", "upper", "lower", "std.err", "cumhaz", "n.risk", "n.event", "n.censor", "n.enter", "std.chaz") for (i in keep) if (!is.null(fit[[i]])) fit[[i]] <- unlistsurv(ltemp, i) fit$strata[] <- sapply(ltemp, function(x) length(x$time)) fit } @ Repeat the code for survfitms objects. The only real difference is the preservation of \code{pstate} and \code{cumhaz} instead of \code{surv}, and the use of survmean2. <>= summary.survfitms <- function(object, times, censored=FALSE, scale=1, extend=FALSE, rmean= getOption("survfit.rmean"), ...) { fit <- object # save typing if (!inherits(fit, 'survfitms')) stop("summary.survfitms can only be used for survfitms objects") if (is.null(fit$logse)) fit$logse <- FALSE # older style # The print.rmean option is depreciated, it is still listened # to in print.survfit, but ignored here if (is.null(rmean)) rmean <- "common" if (is.numeric(rmean)) { if (is.null(fit$start.time)) { if (rmean < min(fit$time)) stop("Truncation point for the mean is < smallest survival") } else if (rmean < fit$start.time) stop("Truncation point for the mean is < smallest survival") } else { rmean <- match.arg(rmean, c('none', 'common', 'individual')) if (length(rmean)==0) stop("Invalid value for rmean option") } fit0 <- survfit0(fit, fit$start.time) # add time 0 temp <- survmean2(fit0, scale=scale, rmean) table <- temp$matrix #for inclusion in the output list rmean.endtime <- temp$end.time if (!missing(times)) { if (!is.numeric(times)) stop ("times must be numeric") times <- sort(times) } if (!is.null(fit$strata)) { nstrat <- length(fit$strata) sindx <- rep(1:nstrat, fit$strata) } delta <- function(x, indx) { # sums between chosen times if (is.logical(indx)) indx <- which(indx) if (!is.null(x) && length(indx) >0) { fx <- function(x, indx) diff(c(0, c(0, cumsum(x))[indx+1])) if (is.matrix(x)) { temp <- apply(x, 2, fx, indx=indx) if (is.matrix(temp)) temp else matrix(temp, nrow=1) } else fx(x, indx) } else NULL } if (missing(times)) { <> } else { fit <-fit0 # easier to work with <> times <- sort(times) if (is.null(fit$strata)) fit <- findrow(fit, times, extend) else { ltemp <- vector("list", nstrat) for (i in 1:nstrat) ltemp[[i]] <- findrow(fit[i,], times, extend) fit <- unpacksurv(fit, ltemp) } } # finish off the output structure fit$table <- table if (length(rmean.endtime)>0 && !any(is.na(rmean.endtime))) fit$rmean.endtime <- rmean.endtime if (!is.null(fit$strata)) fit$strata <- factor(rep(names(fit$strata), fit$strata)) # A survfit object may contain std(log S) or std(S), summary always std(S) if (!is.null(fit$std.err) && fit$logse) fit$std.err <- fit$std.err * fit$surv if (scale != 1) { # fix scale in the output fit$time <- fit$time/scale } class(fit) <- "summary.survfitms" fit } <> <> @ Printing for a survfitms object is different than for a survfit one. The big difference is that I don't have an estimate of the median, or any other quantile for that matter. Mean time in state makes sense, but I don't have a standard error for it at the moment. The other is that there is usually a mismatch between the n.event matrix and the n.risk matrix. The latter has all the states that were possible whereas the former only has states with an arrow pointing in. We need to manufacture the 0 events for the other states. <>= print.survfitms <- function(x, scale=1, rmean = getOption("survfit.rmean"), ...) { if (!is.null(cl<- x$call)) { cat("Call: ") dput(cl) cat("\n") } omit <- x$na.action if (length(omit)) cat(" ", naprint(omit), "\n") x <- survfit0(x, x$start.time) if (is.null(rmean)) rmean <- "common" if (is.numeric(rmean)) { if (is.null(x$start.time)) { if (rmean < min(x$time)) stop("Truncation point for the mean is < smallest survival") } else if (rmean < x$start.time) stop("Truncation point for the mean is < smallest survival") } else { rmean <- match.arg(rmean, c('none', 'common', 'individual')) if (length(rmean)==0) stop("Invalid value for rmean option") } temp <- survmean2(x, scale=scale, rmean) if (is.null(temp$end.time)) print(temp$matrix, ...) else { etime <- temp$end.time dd <- dimnames(temp$matrix) cname <- dd[[2]] cname[length(cname)] <- paste0(cname[length(cname)], '*') dd[[2]] <- cname dimnames(temp$matrix) <- dd print(temp$matrix, ...) if (length(etime) ==1) cat(" *restricted mean time in state (max time =", format(etime, ...), ")\n") else cat(" *restricted mean time in state (per curve cutoff)\n") } invisible(x) } @ This part of the computation is set out separately since it is called by both print and summary. <>= survmean2 <- function(x, scale=1, rmean) { nstate <- length(x$states) #there will always be at least 1 state ngrp <- max(1, length(x$strata)) if (is.null(x$newdata)) ndata <- 0 else ndata <- nrow(x$newdata) if (ngrp >1) { igrp <- rep(1:ngrp, x$strata) rname <- names(x$strata) } else { igrp <- rep(1, length(x$time)) rname <- NULL } # The n.event matrix may not have nstate columms. Its # colnames are the first elements of states, however if (is.matrix(x$n.event)) { nc <- ncol(x$n.event) nevent <- tapply(x$n.event, list(rep(igrp, nc), col(x$n.event)), sum) dimnames(nevent) <- list(rname, x$states[1:nc]) } else { nevent <- tapply(x$n.event, igrp, sum) names(nevent) <- rname } if (ndata< 2) { outmat <- matrix(0., nrow=nstate*ngrp , ncol=2) outmat[,1] <- rep(x$n, nstate) outmat[1:length(nevent), 2] <- c(nevent) if (ngrp >1) rowname <- c(outer(rname, x$states, paste, sep=", ")) else rowname <- x$states } else { outmat <- matrix(0., nrow=nstate*ndata*ngrp, ncol=2) outmat[,1] <- rep(x$n, nstate*ndata) outmat[, 2] <- rep(c(nevent), each=ndata) temp <- outer(1:ndata, x$states, paste, sep=", ") if (ngrp >1) rowname <- c(outer(rname, temp, paste, sep=", ")) else rowname <- temp nstate <- nstate * ndata } # Caculate the mean time in each state if (rmean != "none") { if (is.numeric(rmean)) maxtime <- rep(rmean, ngrp) else if (rmean=="common") maxtime <- rep(max(x$time), ngrp) else maxtime <- tapply(x$time, igrp, max) meantime <- matrix(0., ngrp, nstate) if (!is.null(x$influence)) stdtime <- meantime for (i in 1:ngrp) { # a 2 dimensional matrix is an "array", but a 3-dim array is # not a "matrix", so check for matrix first. if (is.matrix(x$pstate)) temp <- x$pstate[igrp==i,, drop=FALSE] else if (is.array(x$pstate)) temp <- matrix(x$pstate[igrp==i,,,drop=FALSE], ncol= nstate) else temp <- matrix(x$pstate[igrp==i], ncol=1) tt <- x$time[igrp==i] # Now cut it off at maxtime delta <- diff(c(tt[tt nrow(temp)) delta <- delta[1:nrow(temp)] if (length(delta) < nrow(temp)) delta <- c(delta, rep(0, nrow(temp) - length(delta))) meantime[i,] <- colSums(delta*temp) if (!is.null(x$influence)) { # calculate the variance if (is.list(x$influence)) itemp <- apply(x$influence[[i]], 1, function(x) colSums(x*delta)) else itemp <- apply(x$influence, 1, function(x) colSums(x*delta)) stdtime[i,] <- sqrt(rowSums(itemp^2)) } } outmat <- cbind(outmat, c(meantime)/scale) cname <- c("n", "nevent", "rmean") if (!is.null(x$influence)) { outmat <- cbind(outmat, c(stdtime)/scale) cname <- c(cname, "std(rmean)") } # report back a single time, if there is only one if (all(maxtime == maxtime[1])) maxtime <- maxtime[1] } else cname <- c("n", "nevent") dimnames(outmat) <- list(rowname, cname) if (rmean=='none') list(matrix=outmat) else list(matrix=outmat, end.time=maxtime/scale) } @ survival/noweb/agreg.Rnw0000644000176200001440000011051514060746037015026 0ustar liggesusers\subsection{Andersen-Gill fits} When the survival data set has (start, stop] data a couple of computational issues are added. A primary one is how to do this compuation efficiently. At each event time we need to compute 3 quantities, each of them added up over the current risk set. \begin{itemize} \item The weighted sum of the risk scores $\sum w_i r_i$ where $r_i = \exp(\eta_i)$ and $\eta_i = x_{i1}\beta_1 + x_{i2}\beta_2 +\ldots$ is the current linear predictor. \item The weighted mean of the covariates $x$, with weight $w_i r_i$. \item The weighted variance-covariance matrix of $x$. \end{itemize} The current risk set at some event time $t$ is the set of all (start, stop] intervals that overlap $t$, and are part of the same strata. The round/square brackets in the prior sentence are important: for an event time $t=20$ the interval $(5,20]$ is considered to overlap $t$ and the interval $(20,55]$ does not overlap $t$. Our routine for the simple right censored Cox model computes these efficiently by keeping a cumulative sum. Starting with the longest survival move backwards through time, adding and subtracting subject from the sum as we go. The code below creates two sort indices, one orders the data by reverse stop time and the other by reverse start time, each within strata. The fit routine is called by the coxph function with arguments \begin{description} \item[x] matrix of covariates \item[y] three column matrix containing the start time, stop time, and event for each observation \item[strata] for stratified fits, the strata of each subject \item[offset] the offset, usually a vector of zeros \item[init] initial estimate for the coefficients \item[control] results of the coxph.control function \item[weights] case weights, often a vector of ones. \item[method] how ties are handled: 1=Breslow, 2=Efron \item[rownames] used to label the residuals \end{description} If the data set has any observations whose (start, stop] interval does not overlap any death times, those rows of data play no role in the computation, and we push them to the end of the sort order and report a smaller $n$ to the C routine. The reason for this has less to do with efficiency than with safety: one user, for example, created a data set with a time*covariate interaction, to be used for testing proportional hazards with an \code{x:ns(time, df=4)} term. They had cut the data up by day using survSplit, there was a long no-event stretch of time before the last censor, and this generated some large outliers in the extrapolated spline --- large enough to force an exp() overflow. <>= agreg.fit <- function(x, y, strata, offset, init, control, weights, method, rownames, resid=TRUE, nocenter=NULL) { nvar <- ncol(x) event <- y[,3] if (all(event==0)) stop("Can't fit a Cox model with 0 failures") if (missing(offset) || is.null(offset)) offset <- rep(0.0, nrow(y)) if (missing(weights)|| is.null(weights))weights<- rep(1.0, nrow(y)) else if (any(weights<=0)) stop("Invalid weights, must be >0") else weights <- as.vector(weights) # Find rows to be ignored. We have to match within strata: a # value that spans a death in another stratum, but not it its # own, should be removed. Hence the per stratum delta if (length(strata) ==0) {y1 <- y[,1]; y2 <- y[,2]} else { if (is.numeric(strata)) strata <- as.integer(strata) else strata <- as.integer(as.factor(strata)) delta <- strata* (1+ max(y[,2]) - min(y[,1])) y1 <- y[,1] + delta y2 <- y[,2] + delta } event <- y[,3] > 0 dtime <- sort(unique(y2[event])) indx1 <- findInterval(y1, dtime) indx2 <- findInterval(y2, dtime) # indx1 != indx2 for any obs that spans an event time ignore <- (indx1 == indx2) nused <- sum(!ignore) # Sort the data (or rather, get a list of sorted indices) # For both stop and start times, the indices go from last to first if (length(strata)==0) { sort.end <- order(ignore, -y[,2]) -1L #indices start at 0 for C code sort.start<- order(ignore, -y[,1]) -1L strata <- rep(0L, nrow(y)) } else { sort.end <- order(ignore, strata, -y[,2]) -1L sort.start<- order(ignore, strata, -y[,1]) -1L } if (is.null(nvar) || nvar==0) { # A special case: Null model. Just return obvious stuff # To keep the C code to a small set, we call the usual routines, but # with a dummy X matrix and 0 iterations nvar <- 1 x <- matrix(as.double(1:nrow(y)), ncol=1) #keep the .C call happy maxiter <- 0 nullmodel <- TRUE if (length(init) !=0) stop("Wrong length for inital values") init <- 0.0 #dummy value to keep a .C call happy (doesn't like 0 length) } else { nullmodel <- FALSE maxiter <- control$iter.max if (is.null(init)) init <- rep(0., nvar) if (length(init) != nvar) stop("Wrong length for inital values") } # 2021 change: pass in per covariate centering. This gives # us more freedom to experiment. Default is to leave 0/1 variables alone if (is.null(nocenter)) zero.one <- rep(FALSE, ncol(x)) zero.one <- apply(x, 2, function(z) all(z %in% nocenter)) # the returned value of agfit$coef starts as a copy of init, so make sure # is is a vector and not a matrix; as.double suffices. # Solidify the storage mode of other arguments storage.mode(y) <- storage.mode(x) <- "double" storage.mode(offset) <- storage.mode(weights) <- "double" agfit <- .Call(Cagfit4, nused, y, x, strata, weights, offset, as.double(init), sort.start, sort.end, as.integer(method=="efron"), as.integer(maxiter), as.double(control$eps), as.double(control$toler.chol), ifelse(zero.one, 0L, 1L)) # agfit4 centers variables within strata, so does not return a vector # of means. Use a fill in consistent with other coxph routines agmeans <- ifelse(zero.one, 0, colMeans(x)) <> <> rval } @ Upon return we need to clean up three simple things. The first is the rare case that the agfit routine failed. These cases are rare, usually involve an overflow or underflow, and we encourage users to let us have a copy of the data when it occurs. (They end up in the \code{fail} directory of the library.) The second is that if any of the covariates were redudant then this will be marked by zeros on the diagonal of the variance matrix. Replace these coefficients and their variances with NA. The last is to post a warning message about possible infinite coefficients. The algorithm for determining this is unreliable, unfortunately. Sometimes coefficients are marked as infinite when the solution is not tending to infinity (usually associated with a very skewed covariate), and sometimes one that is tending to infinity is not marked. Que sera sera. Don't complain if the user asked for only one iteration; they will already know that it has not converged. <>= vmat <- agfit$imat coef <- agfit$coef if (agfit$flag[1] < nvar) which.sing <- diag(vmat)==0 else which.sing <- rep(FALSE,nvar) if (maxiter >1) { infs <- abs(agfit$u %*% vmat) if (any(!is.finite(coef)) || any(!is.finite(vmat))) stop("routine failed due to numeric overflow.", "This should never happen. Please contact the author.") if (agfit$flag[4] > 0) warning("Ran out of iterations and did not converge") else { infs <- (!is.finite(agfit$u) | infs > control$toler.inf*(1+ abs(coef))) if (any(infs)) warning(paste("Loglik converged before variable ", paste((1:nvar)[infs],collapse=","), "; beta may be infinite. ")) } } @ The last of the code is very standard. Compute residuals and package up the results. One design decision is that we return all $n$ residuals and predicted values, even though the model fit ignored useless observations. (All those obs have a residual of 0). <>= lp <- as.vector(x %*% coef + offset - sum(coef * agmeans)) if (resid) { if (any(lp > log(.Machine$double.xmax))) { # prevent a failure message due to overflow # this occurs with near-infinite coefficients temp <- lp + log(.Machine$double.xmax) - (1 + max(lp)) score <- exp(temp) } else score <- exp(lp) residuals <- .Call(Cagmart3, nused, y, score, weights, strata, sort.start, sort.end, as.integer(method=='efron')) names(residuals) <- rownames } # The if-then-else below is a real pain in the butt, but the tccox # package's test suite assumes that the ORDER of elements in a coxph # object will never change. # if (nullmodel) { rval <- list(loglik=agfit$loglik[2], linear.predictors = offset, method= method, class = c("coxph.null", 'coxph') ) if (resid) rval$residuals <- residuals } else { names(coef) <- dimnames(x)[[2]] if (maxiter > 0) coef[which.sing] <- NA # always leave iter=0 alone flag <- agfit$flag names(flag) <- c("rank", "rescale", "step halving", "convergence") if (resid) { rval <- list(coefficients = coef, var = vmat, loglik = agfit$loglik, score = agfit$sctest, iter = agfit$iter, linear.predictors = as.vector(lp), residuals = residuals, means = agmeans, first = agfit$u, info = flag, method= method, class = "coxph") } else { rval <- list(coefficients = coef, var = vmat, loglik = agfit$loglik, score = agfit$sctest, iter = agfit$iter, linear.predictors = as.vector(lp), means = agmeans, first = agfit$u, info = flag, method = method, class = "coxph") } rval } @ The details of the C code contain the more challenging part of the computations. It starts with the usual dull stuff. My standard coding style for a variable zed to to use [[zed2]] as the variable name for the R object, and [[zed]] for the pointer to the contents of the object, i.e., what the C code will manipulate. For the matrix objects I make use of ragged arrays, this allows for reference to the i,j element as \code{cmat[i][j]} and makes for more readable code. <>= #include #include "survS.h" #include "survproto.h" SEXP agfit4(SEXP nused2, SEXP surv2, SEXP covar2, SEXP strata2, SEXP weights2, SEXP offset2, SEXP ibeta2, SEXP sort12, SEXP sort22, SEXP method2, SEXP maxiter2, SEXP eps2, SEXP tolerance2, SEXP doscale2) { int i,j,k, person; int indx1, istrat, p, p1; int nrisk, nr; int nused, nvar; int rank=0, rank2, fail; /* =0 to keep -Wall happy */ double **covar, **cmat, **imat; /*ragged array versions*/ double *a, *oldbeta; double *scale; double *a2, **cmat2; double *eta; double denom, zbeta, risk; double dtime =0; /* initial value to stop a -Wall message */ double temp, temp2; double newlk =0; int halving; /*are we doing step halving at the moment? */ double tol_chol, eps; double meanwt; int deaths; double denom2, etasum; double recenter; /* inputs */ double *start, *tstop, *event; double *weights, *offset; int *sort1, *sort2, maxiter; int *strata; double method; /* saving this as double forces some double arithmetic */ int *doscale; /* returned objects */ SEXP imat2, beta2, u2, loglik2; double *beta, *u, *loglik; SEXP sctest2, flag2, iter2; double *sctest; int *flag, *iter; SEXP rlist; static const char *outnames[]={"coef", "u", "imat", "loglik", "sctest", "flag", "iter", ""}; int nprotect; /* number of protect calls I have issued */ /* get sizes and constants */ nused = asInteger(nused2); nvar = ncols(covar2); nr = nrows(covar2); /*nr = number of rows, nused = how many we use */ method= asInteger(method2); eps = asReal(eps2); tol_chol = asReal(tolerance2); maxiter = asInteger(maxiter2); doscale = INTEGER(doscale2); /* input arguments */ start = REAL(surv2); tstop = start + nr; event = tstop + nr; weights = REAL(weights2); offset = REAL(offset2); sort1 = INTEGER(sort12); sort2 = INTEGER(sort22); strata = INTEGER(strata2); /* ** scratch space ** nvar: a, a2, oldbeta, scale ** nvar*nvar: cmat, cmat2 ** nr: eta */ eta = (double *) R_alloc(nr + 4*nvar + 2*nvar*nvar, sizeof(double)); a = eta + nr; a2= a + nvar; scale = a2 + nvar; oldbeta = scale + nvar; /* ** Set up the ragged arrays ** covar2 might not need to be duplicated, even though ** we are going to modify it, due to the way this routine was ** was called. But check */ PROTECT(imat2 = allocMatrix(REALSXP, nvar, nvar)); nprotect =1; if (MAYBE_REFERENCED(covar2)) { PROTECT(covar2 = duplicate(covar2)); nprotect++; } covar= dmatrix(REAL(covar2), nr, nvar); imat = dmatrix(REAL(imat2), nvar, nvar); cmat = dmatrix(oldbeta+ nvar, nvar, nvar); cmat2= dmatrix(oldbeta+ nvar + nvar*nvar, nvar, nvar); /* ** create the output structures */ PROTECT(rlist = mkNamed(VECSXP, outnames)); nprotect++; beta2 = SET_VECTOR_ELT(rlist, 0, duplicate(ibeta2)); beta = REAL(beta2); u2 = SET_VECTOR_ELT(rlist, 1, allocVector(REALSXP, nvar)); u = REAL(u2); SET_VECTOR_ELT(rlist, 2, imat2); loglik2 = SET_VECTOR_ELT(rlist, 3, allocVector(REALSXP, 2)); loglik = REAL(loglik2); sctest2 = SET_VECTOR_ELT(rlist, 4, allocVector(REALSXP, 1)); sctest = REAL(sctest2); flag2 = SET_VECTOR_ELT(rlist, 5, allocVector(INTSXP, 4)); flag = INTEGER(flag2); for (i=0; i<4; i++) flag[i]=0; iter2 = SET_VECTOR_ELT(rlist, 6, allocVector(INTSXP, 1)); iter = INTEGER(iter2); /* ** Subtract the mean from each covar, as this makes the variance ** computation more stable. The mean is taken per stratum, ** the scaling is overall. */ for (i=0; i0) temp = temp2/temp; /* 1/scale */ else temp = 1.0; /* rare case of a constant covariate */ scale[i] = temp; for (person=0; person> <> } @ As we walk through the risk sets observations are both added and removed from a set of running totals. We have 6 running totals: \begin{itemize} \item sum of the weights, denom = $\sum w_i r_i$ \item totals for each covariate a[j] = $\sum w_ir_i x_{ij}$ \item totals for each covariate pair cmat[j,k]= $\sum w_ir_i x_{ij} x_{ik}$ \item the same three quantities, but only for times that are exactly tied with the current death time, named denom2, a2, cmat2. This allows for easy compuatation of the Efron approximation for ties. \end{itemize} At one point I spent a lot of time worrying about $r_i$ values that are too large, but it turns out that the overall scale of the weights does not really matter since they always appear as a ratio. (Assuming we avoid exponential overflow and underflow, of course.) What does get the code in trouble is when there are large and small weights and we get an update of (large + small) - large. For example suppose a data set has a time dependent covariate which grows with time and the data has values like below: \begin{center} \begin{tabular}{ccccc} time1 & time2 & status & x \\ \hline 0 & 90 & 1 & 1 \\ 0 & 105 & 0 & 2 \\ 100 & 120 & 1 & 50 \\ 100 & 124 & 0 & 51 \end{tabular} \end{center} The code moves from large times to small, so the first risk set has subjects 3 and 4, the second has 1 and 2. The original code would do removals only when necessary, i.e., at the event times of 120 and 90, and additions as they came along. This leads to adding in subjects 1 and 2 before the update at time 90 when observations 3 and 4 are removed; for a coefficient greater than about .6 this leads to a loss of all of the significant digits. The defense is to remove subjects from the risk set as early as possible, and defer additions for as long as possible. Every time we hit a new (unique) death time, and only then, update the totals: first remove any old observations no longer in the risk set and then add any new ones. One interesting edge case is observations that are not part of any risk set. (A call to survSplit with too fine a partition can create these, or using a subset of data that excluded some of the deaths.) Observations that are not part of any risk set add unnecessary noise since they will be added and then subtracted from all the totals, but the intermediate values are never used. If said observation had a large risk score this could be exceptionally bad. The parent routine has already dealt with such observations: their indices never appear in the sort1 or sort2 vector. The three primary quantities for the Cox model are the log-likelihood $L$, the score vector $U$ and the Hessian matrix $H$. \begin{align*} L &= \sum_i w_i \delta_i \left[\eta_i - \log(d(t)) \right] \\ d(t) &= \sum_j w_j r_j Y_j(t) \\ U_k &= \sum_i w_i \delta_i \left[ (X_{ik} - \mu_k(t_i)) \right] \\ \mu_k(t) &= \frac{\sum_j w_j r_j Y_j(t) X_{jk}} {d(t)} \\ H_{kl} &= \sum_i w_i \delta_i V_{kl}(t_i) \\ V_{kl}(t) &= \frac{\sum_j w_j r_j Y_j(t) [X_{jk} - \mu_k(t)] [X_{jl}- \mu_l(t)]} {d(t)} \\ &= \frac{\sum_j w_j r_j Y_j(t) X_{jk}X_{jl}} {d(t)} - d(t) \mu_k(t) \mu_l(t) \end{align*} In the above $\delta_i =1$ for an event and 0 otherwise, $w_i$ is the per subject weight, $\eta_i$ is the current linear predictor $X\beta$ for the subject, $r_i = \exp(\eta_i)$ is the risk score and $Y_i(t)$ is 1 if observation $i$ is at risk at time $t$. The vector $\mu(t)$ is the weighted mean of the covariates at time $t$ using a weight of $w r Y(t)$ for each subject, and $V(t)$ is the weighted variance matrix of $X$ at time $t$. Tied deaths and the Efron approximation add a small complication to the formula. Say there are three tied deaths at some particular time $t$. When calculating the denominator $d(t)$, mean $\mu(t)$ and variance $V(t)$ at that time the inclusion value $Y_i(t)$ is 0 or 1 for all other subjects, as usual, but for the three tied deaths Y(t) is taken to be 1 for the first death, 2/3 for the second, and 1/3 for the third. The idea is that if the tied death times were randomly broken by adding a small random amount then each of these three would be in the first risk set, have 2/3 chance of being in the second, and 1/3 chance of being in the risk set for the third death. In the code this means that at a death time we add the \code{denom2}, \code{a2} and \code{c2} portions in a little at at time: for three tied death the code will add in 1/3, update totals, add in another 1/3, update totals, then the last 1/3, and update totals. The variance formula is stable if $\mu$ is small relative to the total variance. This is guarranteed by having a working estimate $m$ of the mean along with the formula: \begin{align*} (1/n) \sum w_ir_i(x_i- \mu)^2 &= (1/n)\sum w_ir_i(x-m)^2 - (\mu -m)^2 \\ \mu &= (1/n) \sum w_ir_i (x_i -m)\\ n &= \sum w_ir_i \end{align*} A refinement of this is to scale the covariates, since the Cholesky decomposition can lose precision when variables are on vastly different scales. We do this centering and scaling once at the beginning of the calculation. Centering is done per strata --- what if someone had two strata and a covariate with mean 0 in the first but mean one million in the second? (Users do amazing things). Scaling is required to be a single value for each covariate, however. For a univariate model scaling does not add any precision. Weighted sums can still be unstable if the weights get out of hand. Because of the exponential $r_i = exp(\eta_i)$ the original centering of the $X$ matrix may not be enough. A particular example was a data set on hospital adverse events with ``number of nurse shift changes to date'' as a time dependent covariate. At any particular time point the covariate varied only by $\pm 3$ between subjects (weekends often use 12 hour nurse shifts instead of 8 hour). The regression coefficient was around 1 and the data duration was 11 weeks (about 200 shifts) so that $eta$ values could be over 100 even after centering. We keep a time dependent average of $\eta$ and use it to update a recentering constant as necessary. A case like this should be rare, but it is not as unusual as one might think. The last numerical problem is when one or more coefficients gets too large, leading to a huge weight exp(eta). This usually happens when a coefficient is tending to infinity, but can also be due to a bad step in the intermediate Newton-Raphson path. In the infinite coefficient case the log-likelihood trends to an asymptote and there is a race between three conditions: convergence of the loglik, singularity of the variance matrix, or an invalid log-likelihood. The first of these wins the race most of the time, especially if the data set is small, and is the simplest case. The last occurs when the denominator becomes $<0$ due to round off so that log(denom) is undefined, the second when extreme weights cause the second derivative to lose precision. In all 3 we revert to step halving, since a bad Newton-Raphson step can cause the same issues to arise. The next section of code adds up the totals for a given iteration. This is the workhorse. For a given death time all of the events tied at that time must be handled together, hence the main loop below proceeds in batches: \begin{enumerate} \item Find the time of the next death. Whenever crossing a stratum boundary, zero cetain intermediate sums. \item Remove all observations in the stratum with time1 $>$ dtime. When survSplit was used to create a data set, this will often remove all. If so we can rezero temporaries and regain precision. \item Add new observations to the risk set and to the death counts. \end{enumerate} <>= for (person=0; person> /* ** add any new subjects who are at risk ** denom2, a2, cmat2, meanwt and deaths count only the deaths */ denom2= 0; meanwt =0; deaths=0; for (i=0; i> risk = exp(eta[p] - recenter) * weights[p]; if (event[p] ==1 ){ deaths++; denom2 += risk; meanwt += weights[p]; newlk += weights[p]* (eta[p] - recenter); for (i=0; i> } /* end of accumulation loop */ @ The last step in the above loop adds terms to the loglik, score and information matrices. Assume that there were 3 tied deaths. The difference between the Efron and Breslow approximations is that for the Efron the three tied subjects are given a weight of 1/3 for the first, 2/3 for the second, and 3/3 for the third death; for the Breslow they get 3/3 for all of them. Note that \code{imat} is symmetric, and that the cholesky routine will utilize the upper triangle of the matrix as input, using the lower part for its own purposes. The inverse from \code{chinv} is also in the upper triangle. <>= /* ** Add results into u and imat for all events at this time point */ if (method==0 || deaths ==1) { /*Breslow */ denom += denom2; newlk -= meanwt*log(denom); /* sum of death weights*/ for (i=0; i>= /* ** subtract out the subjects whose start time is to the right ** If everyone is removed reset the totals to zero. (This happens when ** the survSplit function is used, so it is worth checking). */ for (; indx1>= /* ** We must avoid overflow in the exp function (~709 on Intel) ** and want to act well before that, but not take action very often. ** One of the case-cohort papers suggests an offset of -100 meaning ** that etas of 50-100 can occur in "ok" data, so make it larger ** than this. ** If the range of eta is more then log(1e16) = 37 then the data is ** hopeless: some observations will have effectively 0 weight. Keeping ** the mean sensible has sufficed to keep the max in check. */ if (fabs(etasum/nrisk - recenter) > 200) { flag[1]++; /* a count, for debugging/profiling purposes */ temp = etasum/nrisk - recenter; recenter = etasum/nrisk; if (denom > 0) { /* we can skip this if there is no one at risk */ if (fabs(temp) > 709) error("exp overflow due to covariates\n"); temp = exp(-temp); /* the change in scale, for all the weights */ denom *= temp; for (i=0; i>= /* main loop */ halving =0 ; /* =1 when in the midst of "step halving" */ fail =0; for (*iter=0; *iter<= maxiter; (*iter)++) { R_CheckUserInterrupt(); /* be polite -- did the user hit cntrl-C? */ <> if (*iter==0) { loglik[0] = newlk; loglik[1] = newlk; /* compute the score test, but don't corrupt u */ for (i=0; i0) break; for (i=0; i1 && ((newlk -loglik[1])/ fabs(loglik[1])) < -eps) { /* ** "Once more unto the breach, dear friends, once more; ..." **The last iteration above was worse than one of the earlier ones, ** by more than roundoff error. ** We need to use beta and imat at the last good value, not the ** last attempted value. We have tossed the old imat away, so ** recompute it. ** It will happen very rarely that we run out of iterations, and ** even less often that it is right in the middle of halving. */ for (i=0; i> rank2 = cholesky2(imat, nvar, tol_chol); } break; } if (fail >0 || newlk < loglik[1]) { /* ** The routine has not made progress past the last good value. */ halving++; flag[2]++; for (i=0; i>= flag[0] = rank; loglik[1] = newlk; chinv2(imat, nvar); for (i=0; i y_j, x_i > x_j$ or $y_i < y_j, x_i < x_j$. For a Cox model remember that the predicted survival $\hat y$ is longer if the risk score $X\beta$ is lower, so we have to flip the definition and count ``discordant'' pairs at the end of the routine. The concordance is the fraction of concordant pairs. One wrinkle is what to do with ties in either $y$ or $x$. Such pairs can be ignored in the count (treated as incomparable), treated as discordant, or given a score of 1/2. \begin{itemize} \item Kendall's $\tau$-a scores ties as 0. \item Kendall's $\tau$-b and the Goodman-Kruskal $\gamma$ ignore ties in either $y$ or $x$. \item Somers' $d$ treats ties in $y$ as incomparable, pairs that are tied in $x$ (but not $y$) score as 1/2. The AUC from logistic regression is equal to Somers' $d$. \end{itemize} All three of the above range from -1 to 1, the concordance is $(d +1)/2$. For survival data any pairs which cannot be ranked with certainty are considered incomparable. For instance $s_i$ is censored at time 10 and $s_j$ is an event (or censor) at time 20. Subject $i$ may or may not survive longer than subject $j$. Note that if $s_i$ is censored at time 10 and $s_j$ is an event at time 10 then $s_i > s_j$. Observations that are in different strata are also incomparable, since the Cox model only compares within strata. The program creates 4 variables, which are the number of concordant pairs, discordant, tied on time, and tied on $x$ but not on time. The default concordance is based on the AUC definition, but all 4 values are reported back so that a user can recreate the others if desired. Here is the main routine. <>= concordance <- function(x, ...) UseMethod("concordance") concordance.formula <- function(formula, data, weights, subset, na.action, group, ymin=NULL, ymax=NULL, timewt=c("S", "n", "S/G", "n/G", "n/G2"), influence=FALSE, id, reverse) { Call <- match.call() # save a copy of of the call, as documentation timewt <- match.arg(timewt) index <- match(c("formula", "data", "weights", "subset", "na.action", "group"), names(Call), nomatch=0) temp <- Call[c(1, index)] temp[[1L]] <- quote(stats::model.frame) special <- c("strata", "cluster") temp$formula <- if(missing(data)) terms(formula, special) else terms(formula, special, data=data) mf <- eval(temp, parent.frame()) # model frame if (nrow(mf) ==0) stop("No (non-missing) observations") Terms <- terms(mf) Y <- model.response(mf) if (!inherits(Y, "Surv")) { if (is.numeric(Y) && is.vector(Y)) Y <- Surv(Y) else stop("left hand side of the formula must be a numeric vector or a surival") } n <- nrow(Y) wt <- model.weights(mf) offset<- attr(Terms, "offset") if (length(offset)>0) stop("Offset terms not allowed") stemp <- untangle.specials(Terms, "strata") if (length(stemp$vars)) { if (length(stemp$vars)==1) strat <- m[[stemp$vars]] else strat <- strata(m[,stemp$vars], shortlabel=TRUE) Terms <- Terms[-stemp$terms] } else strat <- NULL id <- model.extract(mf, "id") cluster<- attr(Terms, "specials")$cluster if (length(cluster)) { if (length(id)) stop("cannot have both a cluster() term and an 'id' argument") tempc <- untangle.specials(Terms, 'cluster', 1:10) ord <- attr(Terms, 'order')[tempc$terms] if (any(ord>1)) stop ("Cluster can not be used in an interaction") cluster <- strata(mf[,tempc$vars], shortlabel=TRUE) #allow multiples Terms <- Terms[-tempc$terms] # toss it away } else if (length(id)) cluster <- id x <- model.matrix(Terms, m)[,-1, drop=FALSE] #remove the intercept if (ncol(x) > 1) stop("Only one predictor variable allowed") if (!is.null(ymin) & (length(ymin)> 1 || !is.numeric(ymin))) stop("ymin must be a single number") if (!is.null(ymax) & (length(ymax)> 1 || !is.numeric(ymax))) stop("ymax must be a single number") cfit <- concordance.fit(Y, x, strat, wt, ymin, ymax, timewt, influence, cluster) concordance <- (cfit$tau +1)/2 if (missing(reverse)) reverse <- (concordance < .5) if (!is.logical(reverse)) stop ("the reverse argument must be TRUE/FALSE") if (reverse) concordance <- 1- concordance fit <- c(concordance= concordance, cfit, call=Call) na.action <- attr(m, "na.action") if (length(na.action)) fit$na.action <- na.action class(fit) <- 'concordance' fit } print.concordance <- function(x, ...) { if(!is.null(cl <- x$call)) { cat("Call:\n") dput(cl) cat("\n") } omit <- x$na.action if(length(omit)) cat(" n=", x$n, " (", naprint(omit), ")\n", sep = "") else cat(" n=", x$n, "\n") cat("Concordance= ", format(x$concordance), " se= ", format(x$std.err), '\n', sep='') print(x$stats) invisible(x) } @ The concordance.fit function is broken out separately, since it is called by the \code{coxph} routine. If $y$ is not a survival quantity, then all of the options for the \code{timewt} parameter lead to the same result, so use the simplest one to compute in that case. <>= concordance.fit <- function(y, x, strata, weight, ymin, ymax, timewt, influence) { # The coxph program may occassionally fail, and this will kill the C # routine below if (any(is.na(x)) || any(is.na(y))) return(NULL) # these should only occur if something outside survival calls this routine n <- length(y) if (length(x) != n) stop("x and y are not the same length") if (missing(strata) || is.null(strata)) strata <- rep(0L, n) else if (length(strata) != n) stop("y and strata are not the same length") if (length(weight) != n) stop("y and weight are not the same length") # sort y and x, and get the weights if (!is.Surv(y)) { if (any(diff(order(strata, y)) < 1)) { ord <- order(strata, y) # y within strata y <- y[ord] x <- x[ord] wt <- wt[ord] strata <- strata[ord] sfit <- survfitKM(strata, Surv(y), wt, se.fit=FALSE) timewt <- "S" } } else { type <- attr(stime, "type") if (type %in% c("left", "interval")) stop("left or interval censored data is not supported") if (type %in% c("mright", "mcounting")) stop("multiple state survival is not supported") sfit <- survfitKM(strata, y, wt, se.fit=FALSE) if (timewt %in% c("S/G", "n/G", "n/G2")) { if (type != "right") stop("weights involving 1/G are only valid for right censored data") y2 <-y y2[,2)] <- 1- y[,2] gfit <- survfitKM(strata, y2, wt, se.fit=FALSE) } if (attr(stime, "type") %in% c("counting", "mcounting")) { sort.stop <- order(strata, -stime[,2], stime[,3]) sort.start <- order(strata, -stime[,1]) } else { ord <- order(strata, y[,1], -y[,2]) # death before censoring y <- y[ord,] x <- x[ord] wt <- wt[ord] strata <- strata[ord] } } # weights involve S(t) and G(t-), so we need to interpolate G per # stratum nstrat <- max(stratum) if (timewt %in% c("S/G", "n/G", "n/G2")) { gwt <- unlist(lapply(1:nstrat, function(i) { stemp <- sfit[i] gtemp <- gfit[i] etime <- stemp$time[stemp$nevent>0] e2 <- etime - min(diff(etime))/2 summary(gfit[i], times=e2, extend=TRUE)$surv })) } etime <- sfit$time[sfit$nevent>0] swt <- sfit$surv[sfit$nevent>0] nwt <- (sfit$nrisk- sfit$nevent)[sfit$nevent>0] twt <- switch(timewt) { "S" = swt "S/G" = swt/gwt, "n" = nwt, "n/G"= nwt/gwt, "n/G2" = nwt/gwt^2 } # match each score to the unique set (to deal with ties) uindex <- lapply(1:nstrat, function(i) { temp <- x[strata==i] match(temp, sort(unique(temp)))}) nindx <- sapply(uindex, max) # number of unique time values in each strata # create the indexing vectors <> temp <- btree(max(nindx)) parent <- as.integer(temp/2L) # node number of parent rchild <- as.integer(temp %%2) # is this node the right child? parent <- unlist(lapply(unidex, function(i) parent[i])) rchild <- unlist(lapply(unidex, function(i) rchild[i])) if (ncol(y) ==3) counts <- .Call(Cconcordance1, stime, x, wts, parent, child) else counts <- .Call(Ccondordance2, stime, x, wts, sort.start, sort.stop, parent, child) counts } @ The C code looks a lot like a Cox model: walk forward through time, keep track of the risk sets, and add something to the totals at each death. What needs to be summed is the rank of the event subject's $x$ value, as compared to the value for all others at risk at this time point. For notational simplicity let $Y_j(t_i)$ be an indicator that subject $j$ is at risk at event time $t_i$, and $Y^*_j(t_i)$ the more restrictive one that subject $j$ is both at risk and not a tied event time. The values we want at time $t_i$ are \begin{align} n_i &= \sum_j w_j Y^*_j(t_i) \nonumber \\ C_i &= (v_i/n_i) \delta_i w_i \sum_j w_j Y^*_j(t_i) \left[I(x_i < x_j) \right] \label{C} \\ D_i &= (v_i/n_i) \delta_i w_i \sum_j w_j Y^*_j(t_i) \left[I(x_i > x_j)\right] \label{D} \\ T_i &= (v_i/n_i) \delta_i w_i \sum_j w_j Y^*_j(t_i) \left[I(x_i = x_j) \right] \label{T} \\ m_i &= \delta_i \sum_j w_j (Y_j - Y^*_j) \nonumber \end{align} In the above $n$ is the number of comparable values at event time $t_i$ and $m$ is the number of exact ties, $(v_i/n_i)$ is treated as a fixed time-dependent weight (with no censoring it is a constant). $C$, $D$, and $T$ are the number of concordant, discordant, and tied pairs, respectively. The primary compuational question is how to do this efficiently, i.e., better than a naive algorithm that loops across all $n(n-1)/2$ possible pairs. There are two key ideas. \begin{enumerate} \item Rearrange the counting so that we do it by death times. For each death we count the number of other subjects in the risk set whose score is higher, lower, or tied and add it into the totals. This neatly solves the question of time-dependent covariates. \item Counting the number with higher, lower, and tied $x$ can be done in $O(\log_2 n)$ time if the $x$ data is kept in a binary tree. \end{enumerate} \begin{figure} \myfig{balance} \caption{A balanced tree of 13 nodes.} \label{treefig} \end{figure} Figure \ref{treefig} shows a balanced binary tree containing 13 risk scores. For each node the left child and all its descendants have a smaller value than the parent, the right child and all its descendents have a larger value. Each node in figure \ref{treefig} is also annotated with the total weight of observations in that node and the weight for all its left and right children (not shown on graph). Assume that the tree shown represents all of the subjects still alive at the time a particular subject ``Smith'' expires, and that Smith has the risk score of 19 in the tree. The concordant pairs are those with a risk score $>19$, i.e., both $\hat y$ and $y$ are larger, discordant are $<19$, and we have no ties. The totals can be found by \begin{enumerate} \item Initialize the counts for discordant, concordant and tied to the values from the left children, right children, and ties at this node, respectively, which will be $(C,D,T) = (1,1,0)$. \item Walk up the tree, and at each step add the (parent + left child) or (parent + right child) to either D or C, depending on what part of the tree has not yet been totaled. At the next node (8) $D= D+4$, and at the top node $C=C + 6$. \end{enumerate} There are 5 concordant and 7 discordant pairs. This takes a little less than $\log_2(n)$ steps on average, as compared to an average of $n/2$ for the naive method. The difference can matter when $n$ is large since this traversal must be done for each event. The classic way to store trees is as a linked list. There are several algorithms for adding and subtracting nodes from a tree while maintaining the balance (red-black trees, AA trees, etc) but we take a different approach. Since we need to deal with case weights in the model and we know all the risk score at the outset, the full set of risk scores is organised into a tree at the beginning, updating the sums of weights at each node as observations are added or removed from the risk set. If we internally index the nodes of the tree as 1 for the top, 2--3 for the next horizontal row, 4--7 for the next, \ldots then the parent-child traversal becomes particularly easy. The parent of node $i$ is $i/2$ (integer arithmetic) and the children of node $i$ are $2i$ and $2i +1$. In C code the indices start at 0 of course. The following bit of code arranges data into such a tree. <>= btree <- function(n, id=1L) { if (n==1) id else if (n==2) c(2L*id, id) else if (n==3) c(2L*id, id, 1L*id + 2L) else { split <- ceiling(n/2) c(btree(split-1, 2L*id), id, btree(n-split, 1L + 2L*id)) } } @ Referring again to figure \ref{treefig}, \code{btree(13)} yields the vector \code{ 8 4 2 10 5 7 1 12 6 3 14 7 9} meaning that the smallest element will be in position 8 of the tree, the next smallest in position 2, etc. This function does not fill the bottom row of the tree from left to right, which is okay; what is important in the algorithm is that the parent of each node has an index which is $\le n$. The next question is how to compute a variance for the result. One approach is to compute an infinitesimal jackknife (IJ) estimate, for which we need derivatives with respect to the weights. Looking back at equation \eqref{C} we have \begin{align} C &= \sum_i (v_i/m_i) w_i \delta_i \sum_j Y^*_j(t_i) w_j I(x_i < x_j) \nonumber\\ % \frac{\partial C}{\partial w_k} &= % (v_k/m_k)\delta_k \sum_j Y^*_{j}(t_k) I(x_k < x_j) + % \sum_i (v_i/m_i) w_i Y^*_k(t_i) I(x_i < x_k) \label{partialC} \end{align} A given subject's weight appears in two places, once when they are an event ($w_i \delta_i)$, and the second as part of the risk set for other's events. The solution is to keep two trees. One contains all of the subjects at risk and provides the multiplier for the first type. The second tree holds only the deaths. It it updated at each death, with subtotals of concordant, discordant, and tied. Just before a given subject enters the risk set add -1 times the current sums of concordant, discordant, tied, and total to their counter, and then just before they leave add the new totals. Deaths cause a separate addition when they occur. Keeping tied survival times and tied predictors as separate totals is a bit of a PITA. We can keep a separate total for current ties easily enough. But at the end of the tie we need to reset that entire tree to zero, which is one of the O(n) steps we are trying to avoid. The basic algorithm is to move through an outer and inner loop. The outer loop moves across unique death times, the inner for all obs that share a time. Move from largest to smallest time. \begin{enumerate} \item Move to a new event time \item For all data rows which leave the risk set at this time, add the current deaths triple to their IJ estimate (only applicable to start, stop data). \item For all subjects with an endpoint at this time \begin{enumerate} \item Add -1 times the deaths triple to the subject's IJ estimate \item If not an event, add the subject to the first total in the tree \item If an event, add the first triple to their IJ estimate, and also increment the global totals for $C$, $D$, and $T$. \end{enumerate} \item Go through the deaths at this event time again, adding their totals to both the overall and death triples in the tree \item At the end of each stratum, add the current deaths triple to all subjects still in the stratum. \end{enumerate} A second variance estimate is based on the Cox model. The insight used here is to consider a Cox model with time dependent covariates, where the covariate $x$ at each death time has been transformed into ${\rm rank}(x)$. It is easy to show that the Cox score statistic contribution at each death is $(D-C)/2$ where $C$ and $D$ are the number of concordant and discordant pairs contributed at that death time (for a Cox fit using the Breslow approximation). The contribution to the variance of the score statistic is $V(t) =\sum (r_i - \overline{r})^2 /n$, the $r_i$ being the ranks at that time point and $n$ the number at risk. How can we update this sum using an update formula? First remember the identity \begin{equation*} \sum w_i(x_i - \overline{x})^2 = \sum w_i(x_i-c)^2 - \sum w_i(c - \overline{x})^2 \end{equation*} true for any set of values $x$ and centering constant $c$. For weighted data define the rank of an observation with risk score $r_k$ as \begin{equation*} {\rm rank} = \sum_{r_ik} w_i(r_i - \mu_n)^2 - \sum_{i>k} w_i(r_i - \mu_g)^2 &= (\sum_{i>k} w_i) [(\mu_u -\mu_n)^2 - ((\mu_u-w_k) - \mu_g)^2] \nonumber \\ &= (\sum_{i>k} w_i) (\mu_n + z - 2\mu_u)(\mu_n -z) \label{upper1} \\ &= (\sum_{i>k} w_i) (\mu_n+z - 2\mu_u) (-w_k/2) \label{upper}\\ z&\equiv \mu_g+ w_k \nonumber \end{align} For items of tied rank, their rank increases by the same amount as the overall mean, and so their contribution to the total SS is unchanged. The final part of the update step is to add in the SS contributed by the new observation. An observation is removed from the tree whenver the current time becomes less than the (start, stop] interval of the datum. The ranks for observations of lower risk are unchanged by the removal so equation \eqref{lower1} applies just as before, but with the new mean smaller than the old so the last term in equation \eqref{lower} changes sign. For the observations of higher risk both the mean and the ranks change by $w_k$ and equation \eqref{upper1} holds but with $z=\mu_0- w_k$. We can now define the C-routine that does the bulk of the work. First we give the outline shell of the code and then discuss the parts one by one. This routine is for ordinary survival data, and will be called once per stratum. Input variables are \begin{description} \item[n] the number of observations \item[y] matrix containing the time and status, data is sorted by ascending time, with deaths preceding censorings. \item[indx] the tree node at which this observation's risk score resides %' \item[wt] case weight for the observation \item[sum] scratch space, weights for each node of the tree: 3 values are for the node, all left children, and all right children \item[count] the returned counts of concordant, discordant, tied on x, tied on time, and the variance \end{description} <>= #include "survS.h" SEXP concordance1(SEXP y, SEXP wt2, SEXP indx2, SEXP ntree2) { int i, j, k, index; int child, parent; int n, ntree; double *time, *status; double *twt, *nwt, *count; double vss, myrank, wsum1, wsum2, wsum3; /*sum of wts below, tied, above*/ double lmean, umean, oldmean, newmean; double ndeath; /* weighted number of deaths at this point */ SEXP count2; double *wt; int *indx; n = nrows(y); ntree = asInteger(ntree2); wt = REAL(wt2); indx = INTEGER(indx2); time = REAL(y); status = time + n; PROTECT(count2 = allocVector(REALSXP, 5)); count = REAL(count2); /* count5 contains the information matrix */ twt = (double *) R_alloc(2*ntree, sizeof(double)); nwt = twt + ntree; for (i=0; i< 2*ntree; i++) twt[i] =0.0; for (i=0; i<5; i++) count[i]=0.0; vss=0; <> UNPROTECT(1); return(count2); } @ The key part of our computation is to update the vectors of weights. We don't actually pass the risk score values $r$ into the routine, %' it is enough for each observation to point to the appropriate tree node. The tree contains the weights for everyone whose survival is larger than the time currently under review, so starts with all weights equal to zero. For any pair of observations $i,j$ we need to add [[wt[i]*wt[j]]] to the appropriate count. Starting at the largest time (which is sorted last), walk through the tree. \begin{itemize} \item If it is a death time, we need to process all the deaths tied at this time. \begin{enumerate} \item Add [[wt[i] * wt[j]]] to the tied-on-time total, for all pairs $i,j$ of tied times. \item The addition to tied-on-r will be the weight of this observation times the sum of weights for all others with the same risk score and a a greater time, i.e., the weight found at [[indx[i]]] in the tree. \item Similarly for those with smaller or larger risk scores. First add in the children of this node. The left child will be smaller risk scores (and longer times) adding to the concordant pairs, the right child discordant. Then walk up the tree to the root. At each step up we add in data for the 'not me' branch. If we were the right branch (even number node) of a parent then when moving up we add in the left branch counts, and vice-versa. \end{enumerate} \item Now add this set of subject weights into the tree. The weight for a node is [[nwt]] and for the node and all its children is [[twt]]. \end{itemize} <>= for (i=n-1; i>=0; ) { ndeath =0; if (status[i]==1) { /* process all tied deaths at this point */ for (j=i; j>=0 && status[j]==1 && time[j]==time[i]; j--) { ndeath += wt[j]; index = indx[j]; for (k=i; k>j; k--) count[3] += wt[j]*wt[k]; /* tied on time */ count[2] += wt[j] * nwt[index]; /* tied on x */ child = (2*index) +1; /* left child */ if (child < ntree) count[0] += wt[j] * twt[child]; /*left children */ child++; if (child < ntree) count[1] += wt[j] * twt[child]; /*right children */ while (index >0) { /* walk up the tree */ parent = (index-1)/2; if (index & 1) /* I am the left child */ count[1] += wt[j] * (twt[parent] - twt[index]); else count[0] += wt[j] * (twt[parent] - twt[index]); index = parent; } } } else j = i-1; /* Add the weights for these obs into the tree and update variance*/ for (; i>j; i--) { wsum1=0; oldmean = twt[0]/2; index = indx[i]; nwt[index] += wt[i]; twt[index] += wt[i]; wsum2 = nwt[index]; child = 2*index +1; /* left child */ if (child < ntree) wsum1 += twt[child]; while (index >0) { parent = (index-1)/2; twt[parent] += wt[i]; if (!(index&1)) /* I am a right child */ wsum1 += (twt[parent] - twt[index]); index=parent; } wsum3 = twt[0] - (wsum1 + wsum2); /* sum of weights above */ lmean = wsum1/2; umean = wsum1 + wsum2 + wsum3/2; /* new upper mean */ newmean = twt[0]/2; myrank = wsum1 + wsum2/2; vss += wsum1*(newmean+ oldmean - 2*lmean) * (newmean - oldmean); vss += wsum3*(newmean+ oldmean+ wt[i]- 2*umean) *(oldmean-newmean); vss += wt[i]* (myrank -newmean)*(myrank -newmean); } count[4] += ndeath * vss/twt[0]; } @ The code for [start, stop) data is quite similar. As in the agreg routines there are two sort indices, the first indexes the data by stop time, longest to earliest, and the second by start time. The [[y]] variable now has three columns. <>= SEXP concordance2(SEXP y, SEXP wt2, SEXP indx2, SEXP ntree2, SEXP sortstop, SEXP sortstart) { int i, j, k, index; int child, parent; int n, ntree; int istart, iptr, jptr; double *time1, *time2, *status, dtime; double *twt, *nwt, *count; int *sort1, *sort2; double vss, myrank; double wsum1, wsum2, wsum3; /*sum of wts below, tied, above*/ double lmean, umean, oldmean, newmean; double ndeath; SEXP count2; double *wt; int *indx; n = nrows(y); ntree = asInteger(ntree2); wt = REAL(wt2); indx = INTEGER(indx2); sort2 = INTEGER(sortstop); sort1 = INTEGER(sortstart); time1 = REAL(y); time2 = time1 + n; status= time2 + n; PROTECT(count2 = allocVector(REALSXP, 5)); count = REAL(count2); twt = (double *) R_alloc(2*ntree, sizeof(double)); nwt = twt + ntree; for (i=0; i< 2*ntree; i++) twt[i] =0.0; for (i=0; i<5; i++) count[i]=0.0; vss =0; <> UNPROTECT(1); return(count2); } @ The processing changes in 2 ways \begin{itemize} \item The loops go from $0$ to $n-1$ instead of $n-1$ to 0. We need to use [[sort1[i]]] instead of [[i]] as the subscript for the time2 and wt vectors. (The sort vectors go backwards in time.) This happens enough that we use a temporary variables [[iptr]] and [[jptr]] to avoid the double subscript. \item As we move from the longest time to the shortest observations are added into the tree of weights whenever we encounter their stop time. This is just as before. Weights now also need to be removed from the tree whenever we encounter an observation's start time. %' It is convenient ``catch up'' on this second task whenever we encounter a death. \end{itemize} <>= istart = 0; /* where we are with start times */ for (i=0; i= dtime; istart++) { wsum1 =0; oldmean = twt[0]/2; jptr = sort1[istart]; index = indx[jptr]; nwt[index] -= wt[jptr]; twt[index] -= wt[jptr]; wsum2 = nwt[index]; child = 2*index +1; /* left child */ if (child < ntree) wsum1 += twt[child]; while (index >0) { parent = (index-1)/2; twt[parent] -= wt[jptr]; if (!(index&1)) /* I am a right child */ wsum1 += (twt[parent] - twt[index]); index=parent; } wsum3 = twt[0] - (wsum1 + wsum2); lmean = wsum1/2; umean = wsum1 + wsum2 + wsum3/2; /* new upper mean */ newmean = twt[0]/2; myrank = wsum1 + wsum2/2; vss += wsum1*(newmean+ oldmean - 2*lmean) * (newmean-oldmean); oldmean -= wt[jptr]; /* the z in equations above */ vss += wsum3*(newmean+ oldmean -2*umean) * (newmean-oldmean); vss -= wt[jptr]* (myrank -newmean)*(myrank -newmean); } /* Process deaths */ for (j=i; j 0) { /* walk up the tree */ parent = (index-1)/2; if (index &1) /* I am the left child */ count[1] += wt[jptr] * (twt[parent] - twt[index]); else count[0] += wt[jptr] * (twt[parent] - twt[index]); index = parent; } } } else j = i+1; /* Add the weights for these obs into the tree and compute variance */ for (; i0) { parent = (index-1)/2; twt[parent] += wt[iptr]; if (!(index&1)) /* I am a right child */ wsum1 += (twt[parent] - twt[index]); index=parent; } wsum3 = twt[0] - (wsum1 + wsum2); lmean = wsum1/2; umean = wsum1 + wsum2 + wsum3/2; /* new upper mean */ newmean = twt[0]/2; myrank = wsum1 + wsum2/2; vss += wsum1*(newmean+ oldmean - 2*lmean) * (newmean-oldmean); vss += wsum3*(newmean+ oldmean +wt[iptr] - 2*umean) * (oldmean-newmean); vss += wt[iptr]* (myrank -newmean)*(myrank -newmean); } count[4] += ndeath * vss/twt[0]; } @ One last wrinkle is tied risk scores: they are all set to point to the same node of the tree. This part of the compuation is a separate function, since it is also called by the coxph routines. Although we are very careful to create integers and/or doubles for the arguments to .Call I still wrap them in the appropriate as.xxx construction: ``belt and suspenders''. Also, referring to the the mathematics many paragraphs ago, the C routine returns the variance of $(C-D)/2$ and we return the standard deviation of $(C-D)$. If this routine is called with all the x values identical, then $C$ and $D$ will both be zero, but the calculated variance of $C-D$ can be a nonzero tiny number due to round off error. Since this can cause a warning message from the sqrt function we check and correct this. <>= survConcordance.fit <- function(y, x, strata, weight) { # The coxph program may occassionally fail, and this will kill the C # routine below if (any(is.na(x)) || any(is.na(y))) return(NULL) <> docount <- function(stime, risk, wts) { if (attr(stime, 'type') == 'right') { ord <- order(stime[,1], -stime[,2]) ux <- sort(unique(risk)) n2 <- length(ux) index <- btree(n2)[match(risk[ord], ux)] - 1L .Call(Cconcordance1, stime[ord,], as.double(wts[ord]), as.integer(index), as.integer(length(ux))) } else if (attr(stime, 'type') == "counting") { sort.stop <- order(-stime[,2], stime[,3]) sort.start <- order(-stime[,1]) ux <- sort(unique(risk)) n2 <- length(ux) index <- btree(n2)[match(risk, ux)] - 1L .Call(Cconcordance2, stime, as.double(wts), as.integer(index), as.integer(length(ux)), as.integer(sort.stop-1L), as.integer(sort.start-1L)) } else stop("Invalid survival type for concordance") } if (missing(weight) || length(weight)==0) weight <- rep(1.0, length(x)) storage.mode(y) <- "double" if (missing(strata) || length(strata)==0) { count <- docount(y, x, weight) if (count[1]==0 && count[2]==0) count[5]<-0 else count[5] <- 2*sqrt(count[5]) names(count) <- c("concordant", "discordant", "tied.risk", "tied.time", "std(c-d)") } else { strata <- as.factor(strata) ustrat <- levels(strata)[table(strata) >0] #some strata may have 0 obs count <- matrix(0., nrow=length(ustrat), ncol=5) for (i in 1:length(ustrat)) { keep <- which(strata == ustrat[i]) count[i,] <- docount(y[keep,,drop=F], x[keep], weight[keep]) } count[,5] <- 2*sqrt(ifelse(count[,1]+count[,2]==0, 0, count[,5])) dimnames(count) <- list(ustrat, c("concordant", "discordant", "tied.risk", "tied.time", "std(c-d)")) } count } @ survival/noweb/residuals.survfit.Rnw0000644000176200001440000012464014027425473017442 0ustar liggesusers\section{Residuals for survival curves} \subsection{R-code} For all the more complex cases, the variance of a survival curve is based on the infinitesimal jackknife: $$ D_i(t) = \frac{\partial S(t)}{\partial w_i} $$ evaluated at the the observed vector of weights. The variance at a given time is then $D'WD'$ where $D$ is a diagonal matrix of the case weights. When there are multiple states $S$ is replaced by the vector $p(t)$, with one element per state, and the formula gets a bit more complex. The predicted curve from a Cox model is the most complex case. Realizing that we need to return the matrix $D$ to the user, in order to compute the variance of derived quantities like the restricted mean time in state, the code has been changed from a primarily internal focus (compute within the survfit routine) to an external one. The underlying C code is very similar to that in survfitkm.c One major difference in the routines is that this code is designed to return values at a fixed set of time points; it is an error if the user does not provide them. This allows the result to be presented as a matrix or array. Computational differences will be discussed later. The method argument is for debugging. For multi-state it uses either C code or the optimized R method. The double call below is because we want residuals to return a simple matrix, but the pseudo function needs to get back a little bit more. \section{Residuals for survival curves} \subsection{R-code} For all the more complex cases, the variance of a survival curve is based on the infinitesimal jackknife: $$ D_i(t) = \frac{\partial S(t)}{\partial w_i} $$ evaluated at the the observed vector of weights. The variance at a given time is then $D'WD'$ where $D$ is a diagonal matrix of the case weights. When there are multiple states $S$ is replaced by the vector $p(t)$, with one element per state, and the formula gets a bit more complex. The predicted curve from a Cox model is the most complex case. Realizing that we need to return the matrix $D$ to the user, in order to compute the variance of derived quantities like the restricted mean time in state, the code has been changed from a primarily internal focus (compute within the survfit routine) to an external one. The underlying C code is very similar to that in survfitkm.c One major difference in the routines is that this code is designed to return values at a fixed set of time points; it is an error if the user does not provide them. This allows the result to be presented as a matrix or array. Computational differences will be discussed later. The method argument is for debugging. For multi-state it uses either C code or the optimized R method. The double call below is because we want residuals to return a simple matrix, but the pseudo function needs to get back a little bit more. <>= # residuals for a survfit object residuals.survfit <- function(object, times, type= "pstate", collapse, weighted=FALSE, method=1, ...){ if (!inherits(object, "survfit")) stop("argument must be a survfit object") if (missing(times)) stop("the times argument is required") # allow a set of alias temp <- c("pstate", "cumhaz", "sojourn", "survival", "chaz", "rmst", "rmts", "auc") type <- match.arg(casefold(type), temp) itemp <- c(1,2,3,1,2,3,3,3)[match(type, temp)] type <- c("pstate", "cumhaz", "auc")[itemp] if (missing(collapse)) fit <- survresid.fit(object, times, type, weighted=weighted, method= method) else fit <- survresid.fit(object, times, type, collapse= collapse, weighted= weighted, method= method) fit$residuals } survresid.fit <- function(object, times, type= "pstate", collapse, weighted=FALSE, method=1) { survfitms <- inherits(object, "survfitms") coxsurv <- inherits(object, "survfitcox") timefix <- (is.null(object$timefix) || object$timefix) start.time <- object$start.time if (is.null(start.time)) start.time <- min(c(0, object$time)) # check input arguments if (missing(times)) stop ("the times argument is required") else { if (!is.numeric(times)) stop("times must be a numeric vector") times <- sort(unique(times)) if (timefix) times <- aeqSurv(Surv(times))[,1] } # get the data <> if (missing(collapse)) collapse <- (!(is.null(id)) && any(duplicated(id))) if (collapse && is.null(id)) stop("collapse argument requires an id or cluster argument in the survfit call") ny <- ncol(newY) if (collapse && any(X != X[1])) { # If the same id shows up in multiple curves, we just can't deal # with it. temp <- unlist(lapply(split(id, X), unique)) if (any(duplicated(temp))) stop("same id appears in multiple curves, cannot collapse") } timelab <- signif(times, 3) # used for dimnames # What type of survival curve? if (!coxsurv) { stype <- Call$stype if (is.null(stype)) stype <- 1 ctype <- Call$ctype if (is.null(ctype)) ctype <- 1 if (!survfitms) { resid <- rsurvpart1(newY, X, casewt, times, type, stype, ctype, object) if (collapse) { resid <- rowsum(resid, id, reorder=FALSE) dimnames(resid) <- list(id= unique(id), times=timelab) curve <- (as.integer(X))[!duplicated(id)] #which curve for each } else { if (length(id) >0) dimnames(resid) <- list(id=id, times=timelab) curve <- as.integer(X) } } else { # multi-state if (!collapse) { if (length(id >0)) d1name <- id else d1name <- NULL cluster <- d1name curve <- as.integer(X) } else { d1name <- unique(id) cluster <- match(id, d1name) curve <- (as.integer(X))[!duplicated(id)] } resid <- rsurvpart2(newY, X, casewt, istate, times, cluster, type, object, method=method, collapse=collapse) if (type == "cumhaz") { ntemp <- colnames(object$cumhaz) if (length(dim(resid)) ==3) dimnames(resid) <- list(id=d1name, times=timelab, cumhaz= ntemp) else dimnames(resid) <- list(id=d1name, cumhaz=ntemp) } else { ntemp <- object$states if (length(dim(resid)) ==3) dimnames(resid) <- list(id=d1name, times=timelab, state= ntemp) else dimnames(resid) <- list(id=d1name, state= ntemp) } } } else stop("coxph survival curves not yet available") if (weighted && any(casewt !=1)) resid <- resid*casewt list(residuals= resid, curve= curve, id= id, idname=idname) } @ The first part of the work is retrieve the data set. This is done in multiple places in the survival code, all essentially the same. If I gave up (like lm) and forced the model frame to be saved this would be easier of course. <>= Call <- object$call # remember the name of the id variable, if present. # but we don't try to parse it: id= mydata$clinic becomes NULL idname <- Call$id if (is.name(idname)) idname <- as.character(idname) else idname <- NULL # I always need the model frame if (coxsurv) { mf <- model.frame(object) if (is.null(object$y)) Y <- model.response(mf) else Y <- object$y } else { formula <- formula(object) # the chunk below is shared with survfit.formula na.action <- getOption("na.action") if (is.character(na.action)) na.action <- get(na.action) # this is a temporary hack <> # end of shared code } xlev <- levels(X) # Deal with ties if (is.null(Call$timefix) || Call$timefix) newY <- aeqSurv(Y) else newY <- Y @ This code has 3 primary sections: single state survival, multi-state survival, and post-Cox survival. A motivating idea in all of them is to avoid an $O(nd)$ calculation that involves the increment to each subject's leverage at each of the $d$ event times. Since $d$ often grows with $n$ this can get very slow. This routine is designed for the case where the number of time points in the output matrix is modest, so we aim for $O(n)$ processes that repeat for each output time. \subsection{Simple survival} The Nelson-Aalen estimate of cumulative hazard is a simple sum \begin{align} H(t) &= H(t-) + h(t) \nonumber \\ \frac{\partial H(t)}{\partial w_i} &= \frac{\partial H(t-)}{\partial w_i} + [dN_i(t) - Y_i(t)h(t)]/r(t) \nonumber \\ &= \sum_{d_j \le t} dN_i(d_j)/r(d_j) - Y_i(d_j)h(d_j)/r(d_j) \label{NAderiv} \end{align} where $H$ the cumulative hazard, $h$ is the increment to the cumulative hazard, $Y_i$ is 1 when a subject is at risk, and $dN_i$ marks an event for the subject. Our basic strategy for the NA estimate is to use a two stage estimate. First, compute three vectors, each with one element per event time. \begin{itemize} \item term1 = $1/r(d_j)$ is the increment to the derivative for any observation with an event at event time $d_j$ \item term2 = $-h(d_j)/r(d_j)$ is the increment for any observation that is at risk at time $d_j$ \item term3 = cumulative sum of term2 \end{itemize} For any given observation $i$ whose follow-up interval is $(s_i, t_i)$, their derivative at time $z$ is the sum of \begin{itemize} \item term3(min($z$, $t_i$)) - term3(min($z$, $s_i$)) \item term1($t_i$) if $t_i \le z$ and observation $i$ is an event \end{itemize} The Fleming-Harrington estimate of survival is \begin{align*} S(t) &= e^{-H(t)} \\ \partial{S(t)}{\partial w_i} &= -S(t)\partial{H(t)}{\partial w_i} \end{align*} So has exactly the same computation, with a multiplication at the end. <>= rsurvpart1 <- function(Y, X, casewt, times, type, stype, ctype, fit) { ntime <- length(times) etime <- (fit$n.event >0) ny <- ncol(Y) event <- (Y[,ny] >0) status <- Y[,ny] # # Create a list whose first element contains the location of # the death times in curve 1, second element the death times for curve 2, # if (is.null(fit$strata)) { fitrow <- list(which(etime)) } else { temp1 <- cumsum(fit$strata) temp2 <- c(1, temp1+1) fitrow <- lapply(1:length(fit$strata), function(i) { indx <- seq(temp2[i], temp1[i]) indx[etime[indx]] # keep the death times }) } ff <- unlist(fitrow) # for each time x, the index of the last death time which is <=x. # 0 if x is before the first death time in the fit object. # The result is an index to the survival curve matchfun <- function(x, fit, index) { dtime <- fit$time[index] # subset to this curve i2 <- findInterval(x, dtime, left.open=FALSE) c(0, index)[i2 +1] } # output matrix D will have one row per observation, one col for each # reporting time. tindex and yindex have the same dimension as D. # tindex points to the last death time in fit which # is <= the reporting time. (If there is only 1 curve, each col of # tindex will be a repeat of the same value.) tindex <- matrix(0L, nrow(Y), length(times)) for (i in 1:length(fitrow)) { yrow <- which(as.integer(X) ==i) temp <- matchfun(times, fit, fitrow[[i]]) tindex[yrow, ] <- rep(temp, each= length(yrow)) } tindex[,] <- match(tindex, c(0,ff)) -1L # the [,] preserves dimensions # repeat the indexing for Y onto fit$time. Each row of yindex points # to the last row of fit with death time <= Y[,ny] ny <- ncol(Y) yindex <- matrix(0L, nrow(Y), length(times)) event <- (Y[,ny] >0) if (ny==3) startindex <- yindex for (i in 1:length(fitrow)) { yrow <- (as.integer(X) ==i) # rows of Y for this curve temp <- matchfun(Y[yrow,ny-1], fit, fitrow[[i]]) yindex[yrow,] <- rep(temp, ncol(yindex)) if (ny==3) { temp <- matchfun(Y[yrow,1], fit, fitrow[[i]]) startindex[yrow,] <- rep(temp, ncol(yindex)) } } yindex[,] <- match(yindex, c(0,ff)) -1L if (ny==3) { startindex[,] <- match(startindex, c(0,ff)) -1L # no subtractions for report times before subject's entry startindex <- pmin(startindex, tindex) } # Now do the work if (type=="cumhaz" || stype==2) { # result based on hazards if (ctype==1) { <> } else { <> } } else { # not hazard based <> } D } @ The Nelson-Aalen is the simplest case. We don't have to worry about case weights of the data, since that has already been accounted for by the survfit function. <>= death <- (yindex <= tindex & rep(event, ntime)) # an event occured at <= t term1 <- 1/fit$n.risk[ff] term2 <- lapply(fitrow, function(i) fit$n.event[i]/fit$n.risk[i]^2) term3 <- unlist(lapply(term2, cumsum)) sum1 <- c(0, term1)[ifelse(death, 1+yindex, 1)] sum2 <- c(0, term3)[1 + pmin(yindex, tindex)] if (ny==3) sum3 <- c(0, term3)[1 + pmin(startindex, tindex)] if (ny==2) D <- matrix(sum1 - sum2, ncol=ntime) else D <- matrix(sum1 + sum3 - sum2, ncol=ntime) # survival is exp(-H) so the derivative is a simple transform of D if (type== "pstate") D <- -D* c(1,fit$surv[ff])[1+ tindex] else if (type == "auc") { <> } @ The sojourn time is the area under the survival curve. Let $x_j$ be the widths of the rectangles under the curve from event time $d_j$ to $min(d_{j+1}, t)$, zero if $t \le d_j$, or $t-d_m$ if $t$ is after the last event time. \begin{align*} A(0,t) &= \sum_{j=1}^m x_j S(d_j) \\ \frac{\partial A(0,t)}{\partial w_i} &= \sum_{j=1}^m -x_j S(d_j) \frac{\partial H(d_j)}{\partial w_i} \\ &= \sum_{j=1}^m -x_jS(d_j) \sum_{k \le j} \frac{\partial h(d_k)}{\partial w_i} \\ &= \sum_{k=1}^m \frac{\partial h(d_k)}{\partial w_i} \left(\sum_{j\ge k} -x_j S(d_j) \right) \\ &= \sum_{k=1}^m -A(d_k, t) \frac{\partial h(d_k)}{\partial w_i} \end{align*} For an observation at risk over the interval $(a,b)$ we have exactly the same calculus as the cumulative hazard with respect to which $h(d_k)$ terms are counted for the observation, but now they are weighted sums. The weights are different for each output time, so we set them up as a matrix. We need the AUC at each event time $d_k$, and the AUC at the output times. Matrix subscripts are a little used feature of R. If y is a matrix of values and x is a 2 colum matrix containing m (row, col) pairs, the result will be a vector of length m that plucks out the [x[1,1], x[1,2]] value of y, then the [x[2,1], x[2,2]] value of y, etc. They are rarely useful, but very handy in the few cases where they apply. <>= auc1 <- lapply(fitrow, function(i) { if (length(i) <=1) 0 else c(0, cumsum(diff(fit$time[i]) * (fit$surv[i])[-length(i)])) }) # AUC at each event time auc2 <- lapply(fitrow, function(i) { if (length(i) <=1) 0 else { xx <- sort(unique(c(fit$time[i], times))) # all the times yy <- (fit$surv[i])[findInterval(xx, fit$time[i])] auc <- cumsum(c(diff(xx),0) * yy) c(0, auc)[match(times, xx)] }}) # AUC at the output times # Most often this function is called with a single curve, so make that case # faster. (Or I presume so: mapply and do.call may be more efficient than # I think for lists of length 1). if (length(fitrow)==1) { # simple case, most common to ask for auc wtmat <- pmin(outer(auc1[[1]], -auc2[[1]], '+'),0) term1 <- term1 * wtmat term2 <- unlist(term2) * wtmat term3 <- apply(term2, 2, cumsum) } else { #more than one curve, compute weighted cumsum per curve wtmat <- mapply(function(x, y) pmin(outer(x, -y, "+"), 0), auc1, auc2) term1 <- term1 * do.call(rbind, wtmat) temp <- mapply(function(x, y) apply(x*y, 2, cumsum), term2, wtmat) term3 <- do.call(rbind, temp) } sum1 <- sum2 <- matrix(0, nrow(yindex), ntime) if (ny ==3) sum3 <- sum1 for (i in 1:ntime) { sum1[,i] <- c(0, term1[,i])[ifelse(death[,i], 1 + yindex[,i], 1)] sum2[,i] <- c(0, term3[,i])[1 + pmin(yindex[,i], tindex[,i])] if (ny==3) sum3[,i] <- c(0, term3[,i])[1 + pmin(startindex[,i], tindex[,i])] } # Perhaps a bit faster(?), but harder to read. And for AUC people usually only # ask for one time point #sum1 <- rbind(0, term1)[cbind(c(ifelse(death, 1+yindex, 1)), c(col(yindex)))] #sum2 <- rbind(0, term3)[cbind(c(1 + pmin(yindex, tindex)), c(col(yindex)))] #if (ny==3) sum3 <- # rbind(0, term3)[c(cbind(1 + pmin(startindex, tindex)), # c(col(yindex)))] if (ny==2) D <- matrix(sum1 - sum2, ncol=ntime) else D <- matrix(sum1 + sum3 - sum2, ncol=ntime) @ \paragraph{Fleming-Harrington} For the Fleming-Harrington estimator the calculation at a tied time differs slightly. If there were 10 at risk and 3 tied events, the Nelson-Aalen has an increment of 3/10, while the FH has an increment of (1/10 + 1/9 + 1/8). The underlying idea is that the true time values are continuous and we observe ties due to coarsening of the data. The derivative will have 3 terms as well. In this case the needed value cannot be pulled directly from the survfit object. Computationally, the number of distinct times at which a tie occurs is normally quite small and the for loop below will not be too expensive. <>= stop("residuals function still imcomplete, for FH estimate") if (any(casewt != casewt[1])) { # Have to reconstruct the number of obs with an event, the curve only # contains the weighted sum nevent <- unlist(lapply(seq(along.with=levels(X)), function(i) { keep <- which(as.numeric(X) ==i) counts <- table(Y[keep, ny-1], status) as.vector(counts[, ncol(counts)]) })) } else nevent <- fit$n.event n2 <- fit$n.risk risk2 <- 1/fit$n.risk ltemp <- risk2^2 for (i in which(nevent>1)) { # assume not too many ties denom <- fit$n.risk[i] - fit$n.event[i]*(0:(nevent[i]-1))/nevent[i] risk2[i] <- mean(1/denom) # multiplier for the event ltemp[i] <- mean(1/denom^2) n2[i] <- mean(denom) } death <- (yindex <= tindex & rep(event, ntime)) term1 <- risk2[ff] term2 <- lapply(fitrow, function(i) event[i]*ltemp[i]) term3 <- unlist(lapply(term2, cumsum)) sum1 <- c(0, term1)[ifelse(death, 1+yindex, 1)] sum2 <- c(0, term3)[1 + pmin(yindex, tindex)] if (ny==3) sum3 <- c(0, term3)[1 + pmin(startindex, tindex)] if (ny==2) D <- matrix(sum1 - sum2, ncol=ntime) else D <- matrix(sum1 + sum3 - sum2, ncol=ntime) if (type=="pstate") D <- -D* c(0,fit$surv[ff])[1+ tindex] else if (type=="auc") { <> } @ \paragraph{Kaplan-Meier} For the Kaplan-Meier (a special case of the Aalen-Johansen) the underlying algorithm is multiplicative, but we can turn it into an additive algoritm with a slight of hand. \begin{align*} S(t) &= \prod_{d_j\le t} (1- h(d_j)) \\ &= \exp \left(\sum_{d_j\le t} \log(1- h(d_j)) \right) \\ &= \exp \left(\sum_{d_j\le t} \log(r(d_j) - dN(d_j)) - log(r(d_j)) \right) \\ \frac{\partial S(t)}{\partial w_i} &= S(t) \sum_{d_j\le t} \frac{Y_i(d_j) - dN_i(d_j)}{r(d_j) - dN(d_j)} - \frac{Y_i(d_j)}{ r(d_j)} \end{align*} The addend for term2 is now $1/n(n-e)$ where $e$ is the number of events, i.e., the same term as in the Greenwood variance, and term1 is $-1/n(n-e)$. The jumps in the KM curve are just a big larger than jumps in a FH estimate, so it makes sense that these are just a bit larger. <>= death <- (yindex <= tindex & rep(event, ntime)) # dtemp avoids 1/0. (When this occurs the influence is 0, since # the curve has dropped to zero; and this avoids Inf in term1 and term2). dtemp <- ifelse(fit$n.risk==fit$n.event, 0, 1/(fit$n.risk- fit$n.event)) term1 <- dtemp[ff] term2 <- lapply(fitrow, function(i) dtemp[i]*fit$n.event[i]/fit$n.risk[i]) term3 <- unlist(lapply(term2, cumsum)) add1 <- c(0, term1)[ifelse(death, 1+yindex, 1)] add2 <- c(0, term3)[1 + pmin(yindex, tindex)] if (ny==3) add3 <- c(0, term3)[1 + pmin(startindex, tindex)] if (ny==2) D <- matrix(add1 - add2, ncol=ntime) else D <- matrix(add1 + add3 - add2, ncol=ntime) # survival is exp(-H) so the derivative is a simple transform of D if (type== "pstate") D <- -D* c(1,fit$surv[ff])[1+ tindex] else if (type == "auc") { <> } @ \subsection{Multi-state Aalen-Johansen estimate} For multi-state models a correction for ties of similar spirit to the Efron approximation in a Cox model (the ctype=2 argument for \code{survfit}) is difficult: the 'right' answer depends on the study. Thus the ctype argument is not present. Both stype 1 and 2 are feasible, but currently only \code{stype=1} is supported. This makes the code somewhat simpler, but this is more than offset by the multi-state nature. With multiple states we also need to account for influence on the starting state $p(0)$. One thing that can make this code slow is data that has been divided into a very large number of intervals, giving a large number of observations for each cluster. We first deal with that by collapsing adjacent observations. <>= rsurvpart2 <- function(Y, X, casewt, istate, times, cluster, type, fit, method, collapse) { ny <- ncol(Y) ntime <- length(times) nstate <- length(fit$states) # ensure that Y, istate, and fit all use the same set of states states <- fit$states if (!identical(attr(Y, "states"), fit$states)) { map <- match(attr(Y, "states"), fit$states) Y[,ny] <- c(0, map)[1+ Y[,ny]] # 0 = censored attr(Y, "states") <- fit$states } if (is.null(istate)) istate <- rep(1L, nrow(Y)) #everyone starts in s0 else { if (is.character(istate)) istate <- factor(istate) if (is.factor(istate)) { if (!identical(levels(istate), fit$states)) { map <- match(levels(istate), fit$states) if (any(is.na(map))) stop ("invalid levels in istate") istate <- map[istate] } } # istate is numeric, we take what we get and hope it is right } # collapse redundant rows in Y, for efficiency # a redundant row is a censored obs in the middle of a chain of times # if the user wants individial obs, however, we would just have to # expand it again if (ny==3 && collapse & any(duplicated(cluster))) { ord <- order(cluster, X, istate, Y[,1]) cfit <- .Call(Ccollapse, Y, X, istate, cluster, casewt, ord -1L) if (nrow(cfit) < .8*length(X)) { # shrinking the data by 20 percent is worth it temp <- Y[ord,] Y <- cbind(temp[cfit[,1], 1], temp[cfit[2], 2:3]) X <- X[cfit[,1]] istate <- istate[cfit[1,]] cluster <- cluster[cfit[1,]] } } # Compute the initial leverage inf0 <- NULL if (is.null(fit$call$p0) && any(istate != istate[1])) { #p0 was not supplied by the user, and the intitial states vary inf0 <- matrix(0., nrow=nrow(Y), ncol=nstate) i0fun <- function(i, fit, inf0) { # reprise algorithm in survfitCI p0 <- fit$p0 t0 <- fit$time[1] if (ny==2) at.zero <- which(as.numeric(X) ==i) else at.zero <- which(as.numeric(X) ==i & (Y[,1] < t0 & Y[,2] >= t0)) for (j in 1:nstate) { inf0[at.zero, j] <- (ifelse(istate[at.zero]==states[j], 1, 0) - p0[j])/sum(casewt[at.zero]) } inf0 } if (is.null(fit$strata)) inf0 <- i0fun(1, fit, inf0) else for (i in 1:length(levels(X))) inf0 <- i0fun(i, fit[i], inf0) # each iteration fills in some rows } p0 <- fit$p0 # needed for method==1, type != cumhaz fit <- survfit0(fit) # package the initial state into the picture start.time <- fit$time[1] # This next block is identical to the one in rsurvpart1, more comments are # there etime <- (rowSums(fit$n.event) >0) event <- (Y[,ny] >0) # # Create a list whose first element contains the location of # the death times in curve 1, second element for curve 2, etc. # if (is.null(fit$strata)) fitrow <- list(which(etime)) else { temp1 <- cumsum(fit$strata) temp2 <- c(1, temp1+1) fitrow <- lapply(1:length(fit$strata), function(i) { indx <- seq(temp2[i], temp1[i]) indx[etime[indx]] # keep the death times }) } ff <- unlist(fitrow) # for each time x, the index of the last death time which is <=x. # 0 if x is before the first death time matchfun <- function(x, fit, index) { dtime <- fit$time[index] # subset to this curve i2 <- findInterval(x, dtime, left.open=FALSE) c(0, index)[i2 +1] } if (type== "cumhaz") { <> } else { <> } # since we may have done a partial collapse (removing redundant rows), the # parent routine can't collapse the data if (collapse & any(duplicated(cluster))) { if (length(dim(D)) ==2) D <- rowsum(D, cluster, reorder=FALSE) else { #rowsums has to be fooled dd <- dim(D) temp <- rowsum(matrix(D, nrow=dd[1]), cluster) D <- array(temp, dim=c(nrow(temp), dd[2:3])) } } D } @ \paragraph{Nelson-Aalen} The multi-state Nelson-Aalen estimate of the cumulative hazard at time $t$ is a vector with one element for each observed transition pair. If there were $k$ states there are potentially $k(k-1)$ transition pairs, though normally only a small number will occur in a given fit. We ignore transitions from state $j$ to state $j$. Let $r(t)$ be the weighted number at risk at time $t$, in each state. When some subject makes a $j:k$ transition, the $j:k$ transition will have an increment of $w_i/r_j(t)$. This is precisely the same increment as the ordinary Nelson estimate. The only change then is that we loop over the set of possible transitions, creating a large output object. <>= # output matrix D will have one row per observation, one col for each # reporting time. tindex and yindex have the same dimension as D. # tindex points to the last death time in fit which # is <= the reporting time. (If there is only 1 curve, each col of # tindex will be a repeat of the same value.) tindex <- matrix(0L, nrow(Y), length(times)) for (i in 1:length(fitrow)) { yrow <- which(as.integer(X) ==i) temp <- matchfun(times, fit, fitrow[[i]]) tindex[yrow, ] <- rep(temp, each= length(yrow)) } tindex[,] <- match(tindex, c(0,ff)) -1L # the [,] preserves dimensions # repeat the indexing for Y onto fit$time. Each row of yindex points # to the last row of fit with death time <= Y[,ny] ny <- ncol(Y) yindex <- matrix(0L, nrow(Y), length(times)) event <- (Y[,ny] >0) if (ny==3) startindex <- yindex for (i in 1:length(fitrow)) { yrow <- (as.integer(X) ==i) # rows of Y for this curve temp <- matchfun(Y[yrow,ny-1], fit, fitrow[[i]]) yindex[yrow,] <- rep(temp, ncol(yindex)) if (ny==3) { temp <- matchfun(Y[yrow,1], fit, fitrow[[i]]) startindex[yrow,] <- rep(temp, ncol(yindex)) } } yindex[,] <- match(yindex, c(0,ff)) -1L if (ny==3) { startindex[,] <- match(startindex, c(0, ff)) -1L # no subtractions for report times before subject's entry startindex <- pmin(startindex, tindex) } dstate <- Y[,ncol(Y)] istate <- as.integer(istate) ntrans <- ncol(fit$cumhaz) # the number of possible transitions D <- array(0, dim=c(nrow(Y), ntime, ntrans)) scount <- table(istate[dstate!=0], dstate[dstate!=0]) # observed transitions state1 <- row(scount)[scount>0] state2 <- col(scount)[scount>0] temp <- paste(rownames(scount)[state1], colnames(scount)[state2], sep='.') if (!identical(temp, colnames(fit$cumhaz))) stop("setup error") for (k in length(state1)) { e2 <- Y[,ny] == state2[k] add1 <- (yindex <= tindex & rep(e2, ntime)) lsum <- unlist(lapply(fitrow, function(i) cumsum(fit$n.event[i,k]/fit$n.risk[i,k]^2))) term1 <- c(0, 1/fit$n.risk[ff,k])[ifelse(add1, 1+yindex, 1)] term2 <- c(0, lsum)[1+pmin(yindex, tindex)] if (ny==3) term3 <- c(0, lsum)[1 + startindex] if (ny==2) D[,,k] <- matrix(term1 - term2, ncol=ntime) else D[,,k] <- matrix(term1 + term3 - term2, ncol=ntime) } @ \paragraph{Aalen-Johansen} The multi-state AJ estimate is more complex. Let $p(t)$ be the vector of probability in state at time $t$. Then \begin{align} p(t) &= p(t-) [I+ A(t)]\nonumber\\ \frac{\partial p(t)}{\partial w_i} &= \frac{\partial p(t-)}{\partial w_i} [I+ A(t)] + p(t-) \frac{\partial A(t)}{\partial w_i} \nonumber\\ &= U_i(t-) [I+ A(t)] + p(t-) \frac{\partial A(t)}{\partial w_i} \label{ajresidx}\\ \end{align} When we expand the left hand portion of \eqref{ajresidx} to include all observations it becomes simple matrix multiplication, not so with the right hand portion. Each individual subject $i$ has a subject-specific nstate * nstate derivative matrix $dA$, which will be non-zero only for the state (row) $j$ that the subject occupies at time $t-$. The $j$th row of $p(t-) dH$ is added to each subject's derivative. The $A$ matrix at time $t$ has off diagonal elements and derivative \begin{align} A(t)_{jk} &= \frac{\sum_i w_i Y_{ij}(t) dN{ik}(t)} {\sum_i w_iY_{ij}(t)} \\ &= \lambda_{jk}(t) \\ \frac{\partial A(t)}{\partial w_i} &= \frac{dN_{ik}(t) - \lambda_{jk}(t)} {\sum_i w_iY_{ij}(t)} \label{Aderiv} \end{align} This is the standard counting process notation: $Y_{ij}(t)$ is 1 if subject $i$ is in state $j$ and at risk at time $t-$, and $dN_{ik}(t)$ is a transition to state $k$ at time $t$. Each observation at risk appears in at most 1 row of $A(t)$, since they can only be in one state. The diagonal element of $A$ are set so that each row sums to 0. If there are no transitions out of state $j$ at some time point, then that row of $A$ is zero. Since the row sums are constant, the sum of the derivatives for each row must be zero. If we evaluate equation \label{ajresidx} directly there will be $O(nk^2)$ operations at each death time for the matrix product, and another $O(nk)$ to add in the new increment. For a large data set $d$ is often of the same order as $n$, which makes this an expensive calculation. But, this is what the C-code version currently does, because I have code that actually works. <>= if (method==1) { # Compute the result using the direct method, in C code # the routine is called separately for each curve, data in sorted order # is1 <- as.integer(istate) -1L # 0 based subscripts for C if (is.null(inf0)) inf0 <- matrix(0, nrow=nrow(Y), ncol=nstate) if (all(as.integer(X) ==1)) { # only one curve if (ny==2) asort1 <- 0L else asort1 <- order(Y[,1], Y[,2]) -1L asort2 <- order(Y[,ny-1]) -1L tfit <- .Call(Csurvfitresid, Y, asort1, asort2, is1, casewt, p0, inf0, times, start.time, type== "auc") if (ntime==1) { if (type=="auc") D <- tfit[[2]] else D <- tfit[[1]] } else { if (type=="auc") D <- array(tfit[[2]], dim=c(nrow(Y), nstate, ntime)) else D <- array(tfit[[1]], dim=c(nrow(Y), nstate, ntime)) } } else { # one curve at a time ix <- as.numeric(X) # 1, 2, etc if (ntime==1) D <- matrix(0, nrow(Y), nstate) else D <- array(0, dim=c(nrow(Y), nstate, ntime)) for (curve in 1:max(ix)) { j <- which(ix==curve) ytemp <- Y[j,,drop=FALSE] if (ny==2) asort1 <- 0L else asort1 <- order(ytemp[,1], ytemp[,2]) -1L asort2 <- order(ytemp[,ny-1]) -1L # call with a subset of the data j <- which(ix== curve) tfit <- .Call(Csurvfitresid, ytemp, asort1, asort2, is1[j], casewt[j], p0[curve,], inf0[j,], times, start.time, type=="auc") if (ntime==1) { if (type=="auc") D[j,] <- tfit[[2]] else D[j,] <- tfit[[1]] } else { if (type=="auc") D[j,,] <- tfit[[2]] else D[j,,] <- tfit[[1]] } } } # the C code makes time the last dimension, we want it to be second if (ntime > 1) D <- aperm(D, c(1,3,2)) } else { # method 2 <> } @ Can we speed this up? An alternate is to look at the direct expansion. \begin{align} p(t) &= p(0) \prod_{d_j \le t} [I+ A(d_j)] \nonumber \\ \frac{\partial p(t)}{\partial w_i} &= \frac{\partial p(0)}{\partial w_i} \prod_{d_j \le t} [I+ A(d_j)] \\ & + p(0)\sum_{d_j \le t} \left( \prod_{kk$. Let $D(x)$ be the diagonal matrix. \begin{align} T_{01} &= D(p'(0))[I+ A(d_1)] & T_{02} &= T_{01}[I + A(d_2)] & T_{03} &= T_{02} [I + A(d_3)] & \ldots \\ T_{11} &= D(p(d_1)) B(d_1) & T_{12} &= T_{11}[I + A(d_2)] & T_{13} &= T_{12}[I + A(d_3)] & \ldots \\ T_{21} &= 0 & T_{22} &= D(p(d_2)) B(d_2) & T_{23} &= T_{22}[I+ A(d_2)] & \ldots \\ T_{31} &= 0 & T_{32}&=0 & T_{33} &= D(p(d_3)) B(d_3) &\ldots \end{align} (According to the latex guide the above should be nicely spaced, but I get equations that are touching. Why?) If $p(0)$ is a fixed value specified by the user then $p'(0)$ =0. Otherwise $p(0)$ is the emprical distribution of the initial states, just before the first death time $d_1$. Let $n_0$ be the (weighted) count of subjects who are at risk at that time. The $j$th row of $p'(0)$ is defined as the deviative wrt $w_i$ for a subject who starts in state $j$. If no one starts in state $j$ that row of the matrix will be 0, otherwise it contains $(1-p_j(0)$ in the $jth$ element and $p_j(0)/n_0$ elsewhere. Define the matrix $W_{jk} = \sum_{l=1}^j T_{lk}$, with $W_{j0}=0$. Then for someone who enters at time $s$ such that $d_a < s \le d_{a+1}$, is censored or has an event at time $t$ such that $d_b \le t >= Yold <- Y utime <- fit$time[fit$time <= max(times) & etime] # unique death times ndeath <- length(utime) # number of unique event times delta <- diff(c(start.time, utime)) # Expand Y if (ny==2) split <- .Call(Csurvsplit, rep(0., nrow(Y)), Y[,1], times) else split <- .Call(Csurvsplit, Y[,1], Y[,2], times) X <- X[split$row] casewt <- casewt[split$row] istate <- istate[split$row] Y <- cbind(split$start, split$end, ifelse(split$censor, 0, Y[split$row,ny])) ny <- 3 # Create a vector containing the index of each end time into the fit object yindex <- ystart <- double(nrow(Y)) for (i in 1:length(fitrow)) { yrow <- (as.integer(X) ==i) # rows of Y for this curve yindex[yrow] <- matchfun(Y[yrow, 2], fit, fitrow[[i]]) ystart[yrow] <- matchfun(Y[yrow, 1], fit, fitrow[[i]]) } # And one indexing the reporting times into fit tindex <- matrix(0L, nrow=length(fitrow), ncol=ntime) for (i in 1:length(fitrow)) { tindex[i,] <- matchfun(times, fit, fitrow[[i]]) } yindex[,] <- match(yindex, c(0,ff)) -1L tindex[,] <- match(tindex, c(0,ff)) -1L ystart[,] <- pmin(match(ystart, c(0,ff)) -1L, tindex) # Create the array of C matrices cmat <- array(0, dim=c(nstate, nstate, ndeath)) # max(i2) = ndeath, by design Hmat <- cmat # We only care about observations that had a transition; any transitions # after the last reporting time are not relevant transition <- (Y[,ny] !=0 & Y[,ny] != istate & Y[,ny-1] <= max(times)) # obs that had a transition i2 <- match(yindex, sort(unique(yindex))) # which C matrix this obs goes to i2 <- i2[transition] from <- as.numeric(istate[transition]) # from this state to <- Y[transition, ny] # to this state nrisk <- fit$n.risk[cbind(yindex[transition], from)] # number at risk wt <- casewt[transition] for (i in seq(along.with =from)) { j <- c(from[i], to[i]) haz <- wt[i]/nrisk[i] cmat[from[i], j, i2[i]] <- cmat[from[i], j, i2[i]] + c(-haz, haz) } for (i in 1:ndeath) Hmat[,,i] <- cmat[,,i] + diag(nstate) # The transformation matrix H(t) at time t is cmat[,,t] + I # Create the set of W and V matrices. # dindex <- which(etime & fit$time <= max(times)) Wmat <- Vmat <- array(0, dim=c(nstate, nstate, ndeath)) for (i in ndeath:1) { j <- match(dindex[i], tindex, nomatch=0) if (j > 0) { # this death matches one of the reporting times Wmat[,,i] <- diag(nstate) Vmat[,,i] <- matrix(0, nstate, nstate) } else { Wmat[,,i] <- Hmat[,,i+1] %*% Wmat[,,i+1] Vmat[,,i] <- delta[i] + Hmat[,,i+1] %*% Wmat[,,i+1] } } @ The above code has created the Wmat array for all reporting times and for all the curves (if more than one). Each of them reaches forward to the next reporting time. Now work forward in time. <>= iterm <- array(0, dim=c(nstate, nstate, ndeath)) # term in equation itemp <- vtemp <- matrix(0, nstate, nstate) # cumulative sum, temporary isum <- isum2 <- iterm # cumulative sum vsum <- vsum2 <- vterm <- iterm for (i in 1:ndeath) { j <- dindex[i] n0 <- ifelse(fit$n.risk[j,] ==0, 1, fit$n.risk[j,]) # avoid 0/0 iterm[,,i] <- ((fit$pstate[j-1,]/n0) * cmat[,,i]) %*% Wmat[,,i] vterm[,,i] <- ((fit$pstate[j-1,]/n0) * cmat[,,i]) %*% Vmat[,,i] itemp <- itemp + iterm[,,i] vtemp <- vtemp + vterm[,,i] isum[,,i] <- itemp vsum[,,i] <- vtemp j <- match(dindex[i], tindex, nomatch=0) if (j>0) itemp <- vtemp <- matrix(0, nstate, nstate) # reset isum2[,,i] <- itemp vsum2[,,i] <- vtemp } # We want to add isum[state,, entry time] - isum[state,, exit time] for # each subject, and for those with an a:b transition there will be an # additional vector with -1, 1 in the a and b position. i1 <- match(ystart, sort(unique(yindex)), nomatch=0) # start at 0 gives 0 i2 <- match(yindex, sort(unique(yindex))) D <- matrix(0., nrow(Y), nstate) keep <- (Y[,2] <= max(times)) # any intervals after the last reporting time # will have 0 influence for (i in which(keep)) { if (Y[i,3] !=0 && istate[i] != Y[i,3]) { z <- fit$pstate[yindex[i]-1, istate[i]]/fit$n.risk[yindex[i], istate[i]] temp <- double(nstate) temp[istate[i]] = -z temp[Y[i,3]] = z temp <- temp %*% Wmat[,,i2[i]] - isum[istate[i],,i2[i]] if (i1[i] >0) temp <- temp + isum2[istate[i],, i1[i]] D[i,] <- temp } else { if (i1[i] >0) D[i,] = isum2[istate[i],,i1[i]] - isum[istate[i],, i2[i]] else D[i,] = -isum[istate[i],, i2[i]] } } @ By design, each row of $Y$, and hence each row of $D$, corresponds to a unique curve, and also to a unique period in the reporting intervals. (Any Y intervals after the last reporting time will have D=0 for the row.) If there are multiple reporting intervals, create an array with one n by nstate slice for each. If a row lies in the first interval, $D$ currently contains its influence on that interval. It's influence on the second interval is the vector times $\prod H(d_k)$ where $k$ is the set of event times $>$ the first reporting time and $\le$ the second one. <>= Dsave <- D if (!is.null(inf0)) { # add in the initial influence, to the first row of each obs # (inf0 was created on unsplit data) j <- which(!duplicated(split$row)) D[j,] <- D[j,] + (inf0%*% Hmat[,,1] %*% Wmat[,,1]) } if (ntime > 1) { interval <- findInterval(yindex, tindex, left.open=TRUE) D2 <- array(0., dim=c(dim(D), ntime)) D2[interval==0,,1] <- D[interval==0,] for (i in 1:(ntime-1)) { D2[interval==i,,i+1] = D[interval==i,] j <- tindex[i] D2[,,i+1] = D2[,,i+1] + D2[,,i] %*% (Hmat[,,j] %*% Wmat[,,j]) } D <- D2 } # undo any artificial split if (any(duplicated(split$row))) { if (ntime==1) D <- rowsum(D, split$row) else { # rowsums has to be fooled temp <- rowsum(matrix(D, ncol=(nstate*ntime)), split$row) # then undo it D <- array(temp, dim=c(nrow(temp), nstate, ntime)) } } @ survival/noweb/code.nw0000644000176200001440000242411014110720442014516 0ustar liggesusers\documentclass{article} \usepackage{noweb} \usepackage{amsmath} \usepackage{fancyvrb} \usepackage{graphicx} \addtolength{\textwidth}{1in} \addtolength{\oddsidemargin}{-.5in} \setlength{\evensidemargin}{\oddsidemargin} \newcommand{\myfig}[1]{\includegraphics[width=\textwidth]{figures/#1.pdf}} \newcommand{\code}[1]{\texttt{#1}} \newcommand{\xbar}{\overline{x}} \newcommand{\sign}{{\rm sign}} \noweboptions{breakcode} \title{Survival Package Functions} \author{Terry Therneau} \begin{document} \maketitle \tableofcontents \section{Introduction} \begin{quotation} Let us change or traditional attitude to the construction of programs. Instead of imagining that our main task is to instruct a \emph{computer} what to do, let us concentrate rather on explaining to \emph{humans} what we want the computer to do. (Donald E. Knuth, 1984). \end{quotation} This is the definition of a coding style called \emph{literate programming}. I first made use of it in the \emph{coxme} library and have become a full convert. For the survival library only selected objects are documented in this way; as I make updates and changes I am slowly converting the source code. The first motivation for this is to make the code easier for me, both to create and to maintain. As to maintinance, I have found that whenver I need to update code I spend a lot of time in the ``what was I doing in these x lines?'' stage. The code never has enough documentation, even for the author. (The survival library is already better than the majority of packages in R, whose comment level is abysmal. In the pre-noweb source code about 1 line in 6 has a comment, for the noweb document the documentation/code ratio is 2:1.) I also find it helps in creating new code to have the real documentation of intent --- formulas with integrals and such --- closely integrated. The second motivation is to leave code that is well enough explained that someone else can take it over. The source code is structured using \emph{noweb}, one of the simpler literate programming environments. The source code files look remakably like Sweave, and the .Rnw mode of emacs works perfectly for them. This is not too surprising since Sweave was also based on noweb. Sweave is not sufficient to process the files, however, since it has a different intention: it is designed to \emph{execute} the code and make the results into a report, while noweb is designed to \emph{explain} the code. We do this using the \code{noweb} library in R, which contains the \code{noweave} and \code{notangle} functions. (It would in theory be fairly simple to extend \code{knitr} to do this task, which is a topic for further exploration one day. A downside to noweb is that like Sweave it depends on latex, which has an admittedly steep learning curve, and markdown is thus attractive.) \section{Cox Models} \subsection{Coxph} The [[coxph]] routine is the underlying basis for all the models. The source was converted to noweb when adding time-transform terms. The call starts out with the basic building of a model frame and proceeds from there. The aeqSurv function is used to adjucate near ties in the time variable, numerical precision issues that occur when users base caculations on days/365.25 instead of days. A cluster term in the model is an exception. The variable mentioned is never part of the formal model, and so it is not kept as part of the saved terms structure. The analysis for multi-state data is a bit more complex. \begin{itemize} \item If the formula statement is a list, we preprocess this to find out any potential extra variables, and create a new global formula which will be used to create the data frame. \item In the above case missing value processing needs to be deferred, since some covariates may apply only to select transitions. \item After the data frame is constructed, the transitions matrix can be used to check that all the state names actually exist, construct the cmap matrix, and do missing value removal. \end{itemize} <>= #tt <- function(x) x coxph <- function(formula, data, weights, subset, na.action, init, control, ties= c("efron", "breslow", "exact"), singular.ok =TRUE, robust, model=FALSE, x=FALSE, y=TRUE, tt, method=ties, id, cluster, istate, statedata, nocenter=c(-1, 0, 1), ...) { ties <- match.arg(ties) Call <- match.call() ## We want to pass any ... args to coxph.control, but not pass things ## like "dats=mydata" where someone just made a typo. The use of ... ## is simply to allow things like "eps=1e6" with easier typing extraArgs <- list(...) if (length(extraArgs)) { controlargs <- names(formals(coxph.control)) #legal arg names indx <- pmatch(names(extraArgs), controlargs, nomatch=0L) if (any(indx==0L)) stop(gettextf("Argument %s not matched", names(extraArgs)[indx==0L]), domain = NA) } if (missing(control)) control <- coxph.control(...) # Move any cluster() term out of the formula, and make it an argument # instead. This makes everything easier. But, I can only do that with # a local copy, doing otherwise messes up future use of update() on # the model object for a user stuck in "+ cluster()" mode. if (missing(formula)) stop("a formula argument is required") ss <- "cluster" if (is.list(formula)) Terms <- if (missing(data)) terms(formula[[1]], specials=ss) else terms(formula[[1]], specials=ss, data=data) else Terms <- if (missing(data)) terms(formula, specials=ss) else terms(formula, specials=ss, data=data) tcl <- attr(Terms, 'specials')$cluster if (length(tcl) > 1) stop("a formula cannot have multiple cluster terms") if (length(tcl) > 0) { # there is one factors <- attr(Terms, 'factors') if (any(factors[tcl,] >1)) stop("cluster() cannot be in an interaction") if (attr(Terms, "response") ==0) stop("formula must have a Surv response") if (is.null(Call$cluster)) Call$cluster <- attr(Terms, "variables")[[1+tcl]][[2]] else warning("cluster appears both in a formula and as an argument, formula term ignored") # [.terms is broken at least through R 4.1; use our # local drop.special() function instead. Terms <- drop.special(Terms, tcl) formula <- Call$formula <- formula(Terms) } # create a call to model.frame() that contains the formula (required) # and any other of the relevant optional arguments # but don't evaluate it just yet indx <- match(c("formula", "data", "weights", "subset", "na.action", "cluster", "id", "istate"), names(Call), nomatch=0) if (indx[1] ==0) stop("A formula argument is required") tform <- Call[c(1,indx)] # only keep the arguments we wanted tform[[1L]] <- quote(stats::model.frame) # change the function called # if the formula is a list, do the first level of processing on it. if (is.list(formula)) { <> } else { multiform <- FALSE # formula is not a list of expressions covlist <- NULL dformula <- formula } # add specials to the formula special <- c("strata", "tt", "frailty", "ridge", "pspline") tform$formula <- if(missing(data)) terms(formula, special) else terms(formula, special, data=data) # Make "tt" visible for coxph formulas, without making it visible elsewhere if (!is.null(attr(tform$formula, "specials")$tt)) { coxenv <- new.env(parent= environment(formula)) assign("tt", function(x) x, envir=coxenv) environment(tform$formula) <- coxenv } # okay, now evaluate the formula mf <- eval(tform, parent.frame()) Terms <- terms(mf) # Grab the response variable, and deal with Surv2 objects n <- nrow(mf) Y <- model.response(mf) isSurv2 <- inherits(Y, "Surv2") if (isSurv2) { # this is Surv2 style data # if there were any obs removed due to missing, remake the model frame if (length(attr(mf, "na.action"))) { tform$na.action <- na.pass mf <- eval.parent(tform) } if (!is.null(attr(Terms, "specials")$cluster)) stop("cluster() cannot appear in the model statement") new <- surv2data(mf) mf <- new$mf istate <- new$istate id <- new$id Y <- new$y n <- nrow(mf) } else { if (!is.Surv(Y)) stop("Response must be a survival object") id <- model.extract(mf, "id") istate <- model.extract(mf, "istate") } if (n==0) stop("No (non-missing) observations") type <- attr(Y, "type") multi <- FALSE if (type=="mright" || type == "mcounting") multi <- TRUE else if (type!='right' && type!='counting') stop(paste("Cox model doesn't support \"", type, "\" survival data", sep='')) data.n <- nrow(Y) #remember this before any time transforms if (!multi && multiform) stop("formula is a list but the response is not multi-state") if (multi && length(attr(Terms, "specials")$frailty) >0) stop("multi-state models do not currently support frailty terms") if (multi && length(attr(Terms, "specials")$pspline) >0) stop("multi-state models do not currently support pspline terms") if (multi && length(attr(Terms, "specials")$ridge) >0) stop("multi-state models do not currently support ridge penalties") if (control$timefix) Y <- aeqSurv(Y) <> # The time transform will expand the data frame mf. To do this # it needs Y and the strata. Everything else (cluster, offset, weights) # should be extracted after the transform # strats <- attr(Terms, "specials")$strata hasinteractions <- FALSE dropterms <- NULL if (length(strats)) { stemp <- untangle.specials(Terms, 'strata', 1) if (length(stemp$vars)==1) strata.keep <- mf[[stemp$vars]] else strata.keep <- strata(mf[,stemp$vars], shortlabel=TRUE) istrat <- as.integer(strata.keep) for (i in stemp$vars) { #multiple strata terms are allowed # The factors attr has one row for each variable in the frame, one # col for each term in the model. Pick rows for each strata # var, and find if it participates in any interactions. if (any(attr(Terms, 'order')[attr(Terms, "factors")[i,] >0] >1)) hasinteractions <- TRUE } if (!hasinteractions) dropterms <- stemp$terms } else istrat <- NULL if (hasinteractions && multi) stop("multi-state coxph does not support strata*covariate interactions") timetrans <- attr(Terms, "specials")$tt if (missing(tt)) tt <- NULL if (length(timetrans)) { if (multi || isSurv2) stop("the tt() transform is not implemented for multi-state or Surv2 models") <> } xlevels <- .getXlevels(Terms, mf) # grab the cluster, if present. Using cluster() in a formula is no # longer encouraged cluster <- model.extract(mf, "cluster") weights <- model.weights(mf) # The user can call with cluster, id, robust, or any combination # Default for robust: if cluster or any id with > 1 event or # any weights that are not 0 or 1, then TRUE # If only id, treat it as the cluster too has.cluster <- !(missing(cluster) || length(cluster)==0) has.id <- !(missing(id) || length(id)==0) has.rwt<- (!is.null(weights) && any(weights != floor(weights))) #has.rwt<- FALSE # we are rethinking this has.robust <- (!missing(robust) && !is.null(robust)) # arg present if (has.id) id <- as.factor(id) if (missing(robust) || is.null(robust)) { if (has.cluster || has.rwt || (has.id && (multi || anyDuplicated(id[Y[,ncol(Y)]==1])))) robust <- TRUE else robust <- FALSE } if (!is.logical(robust)) stop("robust must be TRUE/FALSE") if (has.cluster) { if (!robust) { warning("cluster specified with robust=FALSE, cluster ignored") ncluster <- 0 clname <- NULL } else { if (is.factor(cluster)) { clname <- levels(cluster) cluster <- as.integer(cluster) } else { clname <- sort(unique(cluster)) cluster <- match(cluster, clname) } ncluster <- length(clname) } } else { if (robust && has.id) { # treat the id as both identifier and clustering clname <- levels(id) cluster <- as.integer(id) ncluster <- length(clname) } else { ncluster <- 0 # has neither } } # if the user said "robust", (time1,time2) data, and no cluster or # id, complain about it if (robust && is.null(cluster)) { if (ncol(Y) ==2 || !has.robust) cluster <- seq.int(1, nrow(mf)) else stop("one of cluster or id is needed") } contrast.arg <- NULL #due to shared code with model.matrix.coxph attr(Terms, "intercept") <- 1 # always have a baseline hazard if (multi) { <> } <> <> if (multi) { <> } # infinite covariates are not screened out by the na.omit routines # But this needs to be done after the multi-X part if (!all(is.finite(X))) stop("data contains an infinite predictor") # init is checked after the final X matrix has been made if (missing(init)) init <- NULL else { if (length(init) != ncol(X)) stop("wrong length for init argument") temp <- X %*% init - sum(colMeans(X) * init) + offset # it's okay to have a few underflows, but if all of them are too # small we get all zeros if (any(exp(temp) > .Machine$double.xmax) || all(exp(temp)==0)) stop("initial values lead to overflow or underflow of the exp function") } <> <> <> } @ Multi-state models have a multi-state response, optionally they have a formula that is a list. If the formula is a list then the first element is the default formula with a survival response and covariates on the right. Further elements are of the form from/to ~ covariates / options and specify other covariates for all from:to transitions. Steps in processing such a formula are \begin{enumerate} \item Gather all the variables that appear on a right-hand side, and create a master formula y ~ all of them. This is used to create the model.frame. We also need to defer missing value processing, since some covariates might appear for only some transitions. \item Get the data. The response, id, and statedata variables can now be checked for consistency with the formulas. \item After X has been formed, expand it. \end{enumerate} Here is code for the first step. <>= multiform <- TRUE dformula <- formula[[1]] # the default formula for transitions if (missing(statedata)) covlist <- parsecovar1(formula[-1]) else { if (!inherits(statedata, "data.frame")) stop("statedata must be a data frame") if (is.null(statedata$state)) stop("statedata data frame must contain a 'state' variable") covlist <- parsecovar1(formula[-1], names(statedata)) } # create the master formula, used for model.frame # the term.labels + reformulate + environment trio is used in [.terms; # if it's good enough for base R it's good enough for me tlab <- unlist(lapply(covlist$rhs, function(x) attr(terms.formula(x$formula), "term.labels"))) tlab <- c(attr(terms.formula(dformula), "term.labels"), tlab) newform <- reformulate(tlab, dformula[[2]]) environment(newform) <- environment(dformula) formula <- newform tform$na.action <- na.pass # defer any missing value work to later @ <>= # check for consistency of the states, and create a transition # matrix if (length(id)==0) stop("an id statement is required for multi-state models") mcheck <- survcheck2(Y, id, istate) # error messages here if (mcheck$flag["overlap"] > 0) stop("data set has overlapping intervals for one or more subjects") transitions <- mcheck$transitions istate <- mcheck$istate states <- mcheck$states # build tmap, which has one row per term, one column per transition if (missing(statedata)) covlist2 <- parsecovar2(covlist, NULL, dformula= dformula, Terms, transitions, states) else covlist2 <- parsecovar2(covlist, statedata, dformula= dformula, Terms, transitions, states) tmap <- covlist2$tmap if (!is.null(covlist)) { <> } @ For multi-state models we can't tell what observations should be removed until any extra formulas have been processed. There may be rows that are missing \emph{some} of the covariates but are okay for \emph{some} transitions. Others could be useless. Those rows can be removed from the model frame before creating the X matrix. Also identify partially used rows, ones where the necessary covariates are present for some of the possible transitions but not all. Those obs are dealt with later by the stacker function. <>= # first vector will be true if there is at least 1 transition for which all # covariates are present, second if there is at least 1 for which some are not good.tran <- bad.tran <- rep(FALSE, nrow(Y)) # We don't need to check interaction terms termname <- rownames(attr(Terms, 'factors')) trow <- (!is.na(match(rownames(tmap), termname))) # create a missing indicator for each term termiss <- matrix(0L, nrow(mf), ncol(mf)) for (i in 1:ncol(mf)) { xx <- is.na(mf[[i]]) if (is.matrix(xx)) termiss[,i] <- apply(xx, 1, any) else termiss[,i] <- xx } for (i in levels(istate)) { rindex <- which(istate ==i) j <- which(covlist2$mapid[,1] == match(i, states)) #possible transitions for (jcol in j) { k <- which(trow & tmap[,jcol] > 0) # the terms involved in that bad.tran[rindex] <- (bad.tran[rindex] | apply(termiss[rindex, k, drop=FALSE], 1, any)) good.tran[rindex] <- (good.tran[rindex] | apply(!termiss[rindex, k, drop=FALSE], 1, all)) } } n.partially.used <- sum(good.tran & bad.tran & !is.na(Y)) omit <- (!good.tran & bad.tran) | is.na(Y) if (all(omit)) stop("all observations deleted due to missing values") temp <- setNames(seq(omit)[omit], attr(mf, "row.names")[omit]) attr(temp, "class") <- "omit" mf <- mf[!omit,, drop=FALSE] attr(mf, "na.action") <- temp Y <- Y[!omit] id <- id[!omit] if (length(istate)) istate <- istate[!omit] # istate can be NULL @ For a multi-state model, create the expanded X matrix. Sometimes it is much expanded. The first step is to create the cmap matrix from tmap by expanding terms; factors turn into multiple columns for instance. If tmap has rows (terms) for strata, then we have to deal with the complication that a strata might be applied to some transitions and not to others. <>= if (length(strats) >0) { stratum_map <- tmap[c(1L, strats),] # strats includes Y, + tmap has an extra row stratum_map[-1,] <- ifelse(stratum_map[-1,] >0, 1L, 0L) if (nrow(stratum_map) > 2) { temp <- stratum_map[-1,] if (!all(apply(temp, 2, function(x) all(x==0) || all(x==1)))) { # the hard case: some transitions use one strata variable, some # transitions use another. We need to keep them separate strata.keep <- mf[,strats] # this will be a data frame istrat <- sapply(strata.keep, as.numeric) } } } else stratum_map <- tmap[1,,drop=FALSE] @ Also create the initial values vector. The stacker function will create a separate block of observations for every unique value in \code{stratum\_map}. Now say that two transitions A:B and A:C share the same baseline hazard. Then either a B or a C outcome will be an ``event'' in that stratum; they would only be distinguished by perhaps having different covariates. The first thing we do with the result is to rebuild the transitions matrix: the working version was created before removing missings and can seriously overstate the number of transitions available. Then set up the data. <>= cmap <- parsecovar3(tmap, colnames(X), attr(X, "assign"), covlist2$phbaseline) xstack <- stacker(cmap, stratum_map, as.integer(istate), X, Y, strata=istrat, states=states) rkeep <- unique(xstack$rindex) transitions <- survcheck2(Y[rkeep,], id[rkeep], istate[rkeep])$transitions X <- xstack$X Y <- xstack$Y istrat <- xstack$strata if (length(offset)) offset <- offset[xstack$rindex] if (length(weights)) weights <- weights[xstack$rindex] if (length(cluster)) cluster <- cluster[xstack$rindex] @ The next step for multi X is to remake the assign attribute. It is a list with one element per term, and needs to be expanded in the same way as \code{tmap}, which has one row per term (+ an intercept row). For \code{predict, type='terms'} to work, no label can be repeated in the final assign object. If a variable `fred' were common across all the states we would want to use that as the label, but if it appears twice, as separate terms for two different transitions, then we label it as fred\_x:y where x:y is the transition. <>= t2 <- tmap[-c(1, strats),,drop=FALSE] # remove the intercept row and strata rows r2 <- row(t2)[!duplicated(as.vector(t2)) & t2 !=0] c2 <- col(t2)[!duplicated(as.vector(t2)) & t2 !=0] a2 <- lapply(seq(along.with=r2), function(i) {cmap[assign[[r2[i]]], c2[i]]}) # which elements are unique? tab <- table(r2) count <- tab[r2] names(a2) <- ifelse(count==1, row.names(t2)[r2], paste(row.names(t2)[r2], colnames(cmap)[c2], sep="_")) assign <- a2 @ An increasingly common error is for users to put the time variable on both sides of the formula, in the mistaken idea that this will deal with a failure of proportional hazards. Add a test for such models, but don't bail out. There will be cases where someone has the the stop variable in an expression on the right hand side, to create current age say. The \code{variables} attribute of the Terms object is the expression form of a list that contains the response variable followed by the predictors. Subscripting this, element 1 is the call to ``list'' itself so we always retain it. My \code{terms.inner} function works only with formula objects. <>= if (length(attr(Terms, 'variables')) > 2) { # a ~1 formula has length 2 ytemp <- terms.inner(formula[1:2]) suppressWarnings(z <- as.numeric(ytemp)) # are any of the elements numeric? ytemp <- ytemp[is.na(z)] # toss numerics, e.g. Surv(t, 1-s) xtemp <- terms.inner(formula[-2]) if (any(!is.na(match(xtemp, ytemp)))) warning("a variable appears on both the left and right sides of the formula") } @ At this point we deal with any time transforms. The model frame is expanded to a ``fake'' data set that has a separate stratum for each unique event-time/strata combination, and any tt() terms in the formula are processed. The first step is to create the index vector [[tindex]] and new strata [[.strata.]]. This last is included in a model.frame call (for others to use), internally the code simply replaces the \code{istrat} variable. A (modestly) fast C-routine first counts up and indexes the observations. We start out with error checks; since the computation can be slow we want to complain early. <>= timetrans <- untangle.specials(Terms, 'tt') ntrans <- length(timetrans$terms) if (is.null(tt)) { tt <- function(x, time, riskset, weights){ #default to O'Brien's logit rank obrien <- function(x) { r <- rank(x) (r-.5)/(.5+length(r)-r) } unlist(tapply(x, riskset, obrien)) } } if (is.function(tt)) tt <- list(tt) #single function becomes a list if (is.list(tt)) { if (any(!sapply(tt, is.function))) stop("The tt argument must contain function or list of functions") if (length(tt) != ntrans) { if (length(tt) ==1) { temp <- vector("list", ntrans) for (i in 1:ntrans) temp[[i]] <- tt[[1]] tt <- temp } else stop("Wrong length for tt argument") } } else stop("The tt argument must contain a function or list of functions") if (ncol(Y)==2) { if (length(strats)==0) { sorted <- order(-Y[,1], Y[,2]) newstrat <- rep.int(0L, nrow(Y)) newstrat[1] <- 1L } else { sorted <- order(istrat, -Y[,1], Y[,2]) #newstrat marks the first obs of each strata newstrat <- as.integer(c(1, 1*(diff(istrat[sorted])!=0))) } if (storage.mode(Y) != "double") storage.mode(Y) <- "double" counts <- .Call(Ccoxcount1, Y[sorted,], as.integer(newstrat)) tindex <- sorted[counts$index] } else { if (length(strats)==0) { sort.end <- order(-Y[,2], Y[,3]) sort.start<- order(-Y[,1]) newstrat <- c(1L, rep(0, nrow(Y) -1)) } else { sort.end <- order(istrat, -Y[,2], Y[,3]) sort.start<- order(istrat, -Y[,1]) newstrat <- c(1L, as.integer(diff(istrat[sort.end])!=0)) } if (storage.mode(Y) != "double") storage.mode(Y) <- "double" counts <- .Call(Ccoxcount2, Y, as.integer(sort.start -1L), as.integer(sort.end -1L), as.integer(newstrat)) tindex <- counts$index } @ The C routine has returned a list with 4 elements \begin{description} \item[nrisk] a vector containing the number at risk at each event time \item[time] the vector of event times \item[status] a vector of status values \item[index] a vector containing the set of subjects at risk for event time 1, followed by those at risk at event time 2, those at risk at event time 3, etc. \end{description} The new data frame is then a simple creation. The subtle part below is a desire to retain transformation information so that a downstream call to \code{termplot} will work. The tt function supplied by the user often finishes with a call to \code{pspline} or \code{ns}. If the returned value of the \code{tt} call has a class for which a \code{makepredictcall} method exists then we need to do 2 things: \begin{enumerate} \item Construct a fake call, e.g., ``pspline(age)'', then feed it and the result of tt as arguments to \code{makepredictcall} \item Replace that componenent in the predvars attribute of the terms. \end{enumerate} The \code{timetrans\$terms} value is a count of the right hand side of the formula. Some objects in the terms structure are unevaluated calls that include y, this adds 2 to the count (the call to ``list'' and the response). <>= Y <- Surv(rep(counts$time, counts$nrisk), counts$status) type <- 'right' # new Y is right censored, even if the old was (start, stop] mf <- mf[tindex,] istrat <- rep(1:length(counts$nrisk), counts$nrisk) weights <- model.weights(mf) if (!is.null(weights) && any(!is.finite(weights))) stop("weights must be finite") tcall <- attr(Terms, 'variables')[timetrans$terms+2] pvars <- attr(Terms, 'predvars') pmethod <- sub("makepredictcall.", "", as.vector(methods("makepredictcall"))) for (i in 1:ntrans) { newtt <- (tt[[i]])(mf[[timetrans$var[i]]], Y[,1], istrat, weights) mf[[timetrans$var[i]]] <- newtt nclass <- class(newtt) if (any(nclass %in% pmethod)) { # It has a makepredictcall method dummy <- as.call(list(as.name(class(newtt)[1]), tcall[[i]][[2]])) ptemp <- makepredictcall(newtt, dummy) pvars[[timetrans$terms[i]+2]] <- ptemp } } attr(Terms, "predvars") <- pvars @ This is the C code for time-transformation. For the first case it expects y to contain time and status sorted from longest time to shortest, and strata=1 for the first observation of each strata. <>= #include "survS.h" /* ** Count up risk sets and identify who is in each */ SEXP coxcount1(SEXP y2, SEXP strat2) { int ntime, nrow; int i, j, n; int stratastart=0; /* start row for this strata */ int nrisk=0; /* number at risk (=0 to stop -Wall complaint)*/ double *time, *status; int *strata; double dtime; SEXP rlist, rlistnames, rtime, rn, rindex, rstatus; int *rrindex, *rrstatus; n = nrows(y2); time = REAL(y2); status = time +n; strata = INTEGER(strat2); /* ** First pass: count the total number of death times (risk sets) ** and the total number of rows in the new data set. */ ntime=0; nrow=0; for (i=0; i> /* ** Pass 2, fill them in */ ntime=0; for (i=0; i> } @ The start-stop case is a bit more work. The set of subjects still at risk is an arbitrary set so we have to keep an index vector [[atrisk]]. At each new death time we write out the set of those at risk, with the deaths last. I toyed with the idea of a binary tree then realized it was not useful: at each death we need to list out all the subjects at risk into the index vector which is an $O(n)$ process, tree or not. <>= #include "survS.h" /* count up risk sets and identify who is in each, (start,stop] version */ SEXP coxcount2(SEXP y2, SEXP isort1, SEXP isort2, SEXP strat2) { int ntime, nrow; int i, j, istart, n; int nrisk=0, *atrisk; double *time1, *time2, *status; int *strata; double dtime; int iptr, jptr; SEXP rlist, rlistnames, rtime, rn, rindex, rstatus; int *rrindex, *rrstatus; int *sort1, *sort2; n = nrows(y2); time1 = REAL(y2); time2 = time1+n; status = time2 +n; strata = INTEGER(strat2); sort1 = INTEGER(isort1); sort2 = INTEGER(isort2); /* ** First pass: count the total number of death times (risk sets) ** and the total number of rows in the new data set */ ntime=0; nrow=0; istart =0; /* walks along the sort1 vector (start times) */ for (i=0; i= dtime; istart++) nrisk--; for(j= i+1; j> atrisk = (int *)R_alloc(n, sizeof(int)); /* marks who is at risk */ /* ** Pass 2, fill them in */ ntime=0; nrisk=0; j=0; /* pointer to time1 */; istart=0; for (i=0; i=dtime; istart++) { atrisk[sort1[istart]]=0; nrisk--; } for (j=1; j> } @ <>= /* ** Allocate memory */ PROTECT(rtime = allocVector(REALSXP, ntime)); PROTECT(rn = allocVector(INTSXP, ntime)); PROTECT(rindex=allocVector(INTSXP, nrow)); PROTECT(rstatus=allocVector(INTSXP,nrow)); rrindex = INTEGER(rindex); rrstatus= INTEGER(rstatus); @ <>= /* return the list */ PROTECT(rlist = allocVector(VECSXP, 4)); SET_VECTOR_ELT(rlist, 0, rn); SET_VECTOR_ELT(rlist, 1, rtime); SET_VECTOR_ELT(rlist, 2, rindex); SET_VECTOR_ELT(rlist, 3, rstatus); PROTECT(rlistnames = allocVector(STRSXP, 4)); SET_STRING_ELT(rlistnames, 0, mkChar("nrisk")); SET_STRING_ELT(rlistnames, 1, mkChar("time")); SET_STRING_ELT(rlistnames, 2, mkChar("index")); SET_STRING_ELT(rlistnames, 3, mkChar("status")); setAttrib(rlist, R_NamesSymbol, rlistnames); unprotect(6); return(rlist); @ We now return to the original thread of the program, though perhaps with new data, and build the $X$ matrix. Creation of the $X$ matrix for a Cox model requires just a bit of trickery. The baseline hazard for a Cox model plays the role of an intercept, but does not appear in the $X$ matrix. However, to create the columns of $X$ for factor variables correctly, we need to call the model.matrix routine in such a way that it \emph{thinks} there is an intercept, and so we set the intercept attribute to 1 in the terms object before calling model.matrix, ignoring any -1 term the user may have added. One simple way to handle all this is to call model.matrix on the original formula and then remove the terms we don't need. However, \begin{enumerate} \item The cluster() term, if any, could lead to thousands of extraneous ``intercept'' columns which are never needed. \item Likewise, nested case-control models can have thousands of strata, again leading many intercepts we never need. They never have strata by covariate interactions, however. \item If there are strata by covariate interactions in the model, the dummy intercepts-per-strata columns are necessary information for the model.matrix routine to correctly compute other columns of $X$. \end{enumerate} On later reflection \code{cluster} should never have been in the model statement in the first place, something that became painfully apparent with addition of multi-state models. In the future we will discourage it. For reason 2 above the usual plan is to also remove strata terms from the ``Terms'' object \emph{before} calling model.matrix, unless there are strata by covariate interactions in which case we remove them after. If anything is pre-dropped, for documentation purposes we want the returned assign attribute to match the Terms structure that we will hand back. (Do we ever use it?) In particular, the numbers therein correspond to the column names in \code{attr(Terms, 'factors')} The requires a shift. The cluster and strata terms are seen as main effects, so appear early in that list. We have found a case where terms get relabeled: <>= t1 <- terms( ~(x1 + x2):x3 + strata(x4)) t2 <- terms( ~(x1 + x2):x3) t3 <- t1[-1] colnames(attr(t1, "factors")) colnames(attr(t2, "factors")) colnames(attr(t3, "factors")) @ In t1 the strata term appears first, as it is the only thing that looks like a main effect, and the column labels are strata(x4), x1:x3, x2:x3. In t3 the column labels are x1:x3 and x3:x2 --- note left-right swap of the second. This means that using match() on the labels is not a reliable approach. We instead assume that nothing is reordered and do a shift. <>= if (length(dropterms)) { Terms2 <- Terms[-dropterms] X <- model.matrix(Terms2, mf, constrasts.arg=contrast.arg) # we want to number the terms wrt the original model matrix temp <- attr(X, "assign") shift <- sort(dropterms) for (i in seq(along.with=shift)) temp <- temp + 1*(shift[i] <= temp) attr(X, "assign") <- temp } else X <- model.matrix(Terms, mf, contrasts.arg=contrast.arg) # drop the intercept after the fact, and also drop strata if necessary Xatt <- attributes(X) if (hasinteractions) adrop <- c(0, untangle.specials(Terms, "strata")$terms) else adrop <- 0 xdrop <- Xatt$assign %in% adrop #columns to drop (always the intercept) X <- X[, !xdrop, drop=FALSE] attr(X, "assign") <- Xatt$assign[!xdrop] attr(X, "contrasts") <- Xatt$contrasts @ Finish the setup. If someone includes an init statement or offset, make sure that it does not lead to instant code failure due to overflow/underflow. <>= offset <- model.offset(mf) if (is.null(offset) | all(offset==0)) offset <- rep(0., nrow(mf)) else if (any(!is.finite(offset) | !is.finite(exp(offset)))) stop("offsets must lead to a finite risk score") weights <- model.weights(mf) if (!is.null(weights) && any(!is.finite(weights))) stop("weights must be finite") assign <- attrassign(X, Terms) contr.save <- attr(X, "contrasts") <> @ Check for a rare edge case: a data set with no events. In this case the return structure is simple. The coefficients will all be NA, since they can't be estimated. The variance matrix is all zeros, in line with the usual rule to zero out any row and col corresponding to an NA coef. The loglik is the sum of zero terms, which we set to zero like the usual R result for sum(numeric(0)). An overall idea is to return something that won't blow up later code. <>= if (sum(Y[, ncol(Y)]) == 0) { # No events in the data! ncoef <- ncol(X) ctemp <- rep(NA, ncoef) names(ctemp) <- colnames(X) concordance= c(concordant=0, discordant=0, tied.x=0, tied.y=0, tied.xy=0, concordance=NA, std=NA, timefix=FALSE) rval <- list(coefficients= ctemp, var = matrix(0.0, ncoef, ncoef), loglik=c(0,0), score =0, iter =0, linear.predictors = offset, residuals = rep(0.0, data.n), means = colMeans(X), method=method, n = data.n, nevent=0, terms=Terms, assign=assign, concordance=concordance, wald.test=0.0, y = Y, call=Call) class(rval) <- "coxph" return(rval) } @ Check for penalized terms in the model, and set up infrastructure for the fitting routines to deal with them. <>= pterms <- sapply(mf, inherits, 'coxph.penalty') if (any(pterms)) { pattr <- lapply(mf[pterms], attributes) pname <- names(pterms)[pterms] # # Check the order of any penalty terms ord <- attr(Terms, "order")[match(pname, attr(Terms, 'term.labels'))] if (any(ord>1)) stop ('Penalty terms cannot be in an interaction') pcols <- assign[match(pname, names(assign))] fit <- coxpenal.fit(X, Y, istrat, offset, init=init, control, weights=weights, method=method, row.names(mf), pcols, pattr, assign, nocenter= nocenter) } @ <>= else { rname <- row.names(mf) if (multi) rname <- rname[xstack$rindex] if( method=="breslow" || method =="efron") { if (grepl('right', type)) fit <- coxph.fit(X, Y, istrat, offset, init, control, weights=weights, method=method, rname, nocenter=nocenter) else fit <- agreg.fit(X, Y, istrat, offset, init, control, weights=weights, method=method, rname, nocenter=nocenter) } else if (method=='exact') { if (type== "right") fit <- coxexact.fit(X, Y, istrat, offset, init, control, weights=weights, method=method, rname, nocenter=nocenter) else fit <- agexact.fit(X, Y, istrat, offset, init, control, weights=weights, method=method, rname, nocenter=nocenter) } else stop(paste ("Unknown method", method)) } @ <>= if (is.character(fit)) { fit <- list(fail=fit) class(fit) <- 'coxph' } else { if (!is.null(fit$coefficients) && any(is.na(fit$coefficients))) { vars <- (1:length(fit$coefficients))[is.na(fit$coefficients)] msg <-paste("X matrix deemed to be singular; variable", paste(vars, collapse=" ")) if (!singular.ok) stop(msg) # else warning(msg) # stop being chatty } fit$n <- data.n fit$nevent <- sum(Y[,ncol(Y)]) fit$terms <- Terms fit$assign <- assign class(fit) <- fit$class fit$class <- NULL # don't compute a robust variance if there are no coefficients if (robust && !is.null(fit$coefficients) && !all(is.na(fit$coefficients))) { fit$naive.var <- fit$var # a little sneaky here: by calling resid before adding the # na.action method, I avoid having missings re-inserted # I also make sure that it doesn't have to reconstruct X and Y fit2 <- c(fit, list(x=X, y=Y, weights=weights)) if (length(istrat)) fit2$strata <- istrat if (length(cluster)) { temp <- residuals.coxph(fit2, type='dfbeta', collapse=cluster, weighted=TRUE) # get score for null model if (is.null(init)) fit2$linear.predictors <- 0*fit$linear.predictors else fit2$linear.predictors <- c(X %*% init) temp0 <- residuals.coxph(fit2, type='score', collapse=cluster, weighted=TRUE) } else { temp <- residuals.coxph(fit2, type='dfbeta', weighted=TRUE) fit2$linear.predictors <- 0*fit$linear.predictors temp0 <- residuals.coxph(fit2, type='score', weighted=TRUE) } fit$var <- t(temp) %*% temp u <- apply(as.matrix(temp0), 2, sum) fit$rscore <- coxph.wtest(t(temp0)%*%temp0, u, control$toler.chol)$test } #Wald test if (length(fit$coefficients) && is.null(fit$wald.test)) { #not for intercept only models, or if test is already done nabeta <- !is.na(fit$coefficients) # The init vector might be longer than the betas, for a sparse term if (is.null(init)) temp <- fit$coefficients[nabeta] else temp <- (fit$coefficients - init[1:length(fit$coefficients)])[nabeta] fit$wald.test <- coxph.wtest(fit$var[nabeta,nabeta], temp, control$toler.chol)$test } # Concordance. Done here so that we can use cluster if it is present # The returned value is a subset of the full result, partly because it # is all we need, but more for backward compatability with survConcordance.fit if (length(cluster)) temp <- concordancefit(Y, fit$linear.predictors, istrat, weights, cluster=cluster, reverse=TRUE, timefix= FALSE) else temp <- concordancefit(Y, fit$linear.predictors, istrat, weights, reverse=TRUE, timefix= FALSE) if (is.matrix(temp$count)) fit$concordance <- c(colSums(temp$count), concordance=temp$concordance, std=sqrt(temp$var)) else fit$concordance <- c(temp$count, concordance=temp$concordance, std=sqrt(temp$var)) na.action <- attr(mf, "na.action") if (length(na.action)) fit$na.action <- na.action if (model) { if (length(timetrans)) { stop("'model=TRUE' not supported for models with tt terms") } fit$model <- mf } if (x) { fit$x <- X if (length(timetrans)) fit$strata <- istrat else if (length(strats)) fit$strata <- strata.keep } if (y) fit$y <- Y fit$timefix <- control$timefix # remember this option } @ If any of the weights were not 1, save the results. Add names to the means component, which are occassionally useful to survfit.coxph. Other objects below are used when we need to recreate a model frame. <>= if (!is.null(weights) && any(weights!=1)) fit$weights <- weights if (multi) { fit$transitions <- transitions fit$states <- states fit$cmap <- cmap fit$stratum_map <- stratum_map # why not 'stratamap'? Confusion with fit$strata fit$resid <- rowsum(fit$resid, xstack$rindex) # add a suffix to each coefficent name. Those that map to multiple transitions # get the first transition they map to single <- apply(cmap, 1, function(x) all(x %in% c(0, max(x)))) #only 1 coef cindx <- col(cmap)[match(1:length(fit$coefficients), cmap)] rindx <- row(cmap)[match(1:length(fit$coefficients), cmap)] suffix <- ifelse(single[rindx], "", paste0("_", colnames(cmap)[cindx])) names(fit$coefficients) <- paste0(names(fit$coefficients), suffix) if (x) fit$strata <- istrat # save the expanded strata class(fit) <- c("coxphms", class(fit)) } names(fit$means) <- names(fit$coefficients) fit$formula <- formula(Terms) if (length(xlevels) >0) fit$xlevels <- xlevels fit$contrasts <- contr.save if (any(offset !=0)) fit$offset <- offset fit$call <- Call fit @ The model.matrix and model.frame routines are called after a Cox model to reconstruct those portions. Much of their code is shared with the coxph routine. <>= # In internal use "data" will often be an already derived model frame. # We detect this via it having a terms attribute. model.matrix.coxph <- function(object, data=NULL, contrast.arg=object$contrasts, ...) { # # If the object has an "x" component, return it, unless a new # data set is given if (is.null(data) && !is.null(object[['x']])) return(object[['x']]) #don't match "xlevels" Terms <- delete.response(object$terms) if (is.null(data)) mf <- stats::model.frame(object) else { if (is.null(attr(data, "terms"))) mf <- stats::model.frame(Terms, data, xlev=object$xlevels) else mf <- data #assume "data" is already a model frame } cluster <- attr(Terms, "specials")$cluster if (length(cluster)) { temp <- untangle.specials(Terms, "cluster") dropterms <- temp$terms } else dropterms <- NULL strats <- attr(Terms, "specials")$strata hasinteractions <- FALSE if (length(strats)) { stemp <- untangle.specials(Terms, 'strata', 1) if (length(stemp$vars)==1) strata.keep <- mf[[stemp$vars]] else strata.keep <- strata(mf[,stemp$vars], shortlabel=TRUE) istrat <- as.integer(strata.keep) for (i in stemp$vars) { #multiple strata terms are allowed # The factors attr has one row for each variable in the frame, one # col for each term in the model. Pick rows for each strata # var, and find if it participates in any interactions. if (any(attr(Terms, 'order')[attr(Terms, "factors")[i,] >0] >1)) hasinteractions <- TRUE } if (!hasinteractions) dropterms <- c(dropterms, stemp$terms) } else istrat <- NULL <> X } @ In parallel is the model.frame routine, which reconstructs the model frame. This routine currently doesn't do all that we want. To wit, the following code fails: \begin{verbatim} > tfun <- function(formula, ndata) { fit <- coxph(formula, data=ndata) model.frame(fit) } > tfun(Surv(time, status) ~ age, lung) Error: ndata not found \end{verbatim} The genesis of this problem is hard to unearth, but has to do with non standard evaluation rules used by model.frame.default. In essence it pays attention to the environment of the formula, but the enclos argument of eval appears to be ignored. I've not yet found a solution. <>= model.frame.coxph <- function(formula, ...) { dots <- list(...) nargs <- dots[match(c("data", "na.action", "subset", "weights", "id", "cluster", "istate"), names(dots), 0)] # If nothing has changed and the coxph object had a model component, # simply return it. if (length(nargs) ==0 && !is.null(formula$model)) return(formula$model) else { # Rebuild the original call to model.frame Terms <- terms(formula) fcall <- formula$call indx <- match(c("formula", "data", "weights", "subset", "na.action", "cluster", "id", "istate"), names(fcall), nomatch=0) if (indx[1] ==0) stop("The coxph call is missing a formula!") temp <- fcall[c(1,indx)] # only keep the arguments we wanted temp[[1]] <- quote(stats::model.frame) # change the function called temp$xlev <- formula$xlevels # this will turn strings to factors temp$formula <- Terms #keep the predvars attribute # Now, any arguments that were on this call overtake the ones that # were in the original call. if (length(nargs) >0) temp[names(nargs)] <- nargs # Make "tt" visible for coxph formulas, if (!is.null(attr(temp$formula, "specials")$tt)) { coxenv <- new.env(parent= environment(temp$formula)) assign("tt", function(x) x, envir=coxenv) environment(temp$formula) <- coxenv } # The documentation for model.frame implies that the environment arg # to eval will be ignored, but if we omit it there is a problem. if (is.null(environment(formula$terms))) mf <- eval(temp, parent.frame()) else mf <- eval(temp, environment(formula$terms), parent.frame()) if (!is.null(attr(formula$terms, "dataClasses"))) .checkMFClasses(attr(formula$terms, "dataClasses"), mf) if (is.null(attr(Terms, "specials")$tt)) return(mf) else { # Do time transform tt <- eval(formula$call$tt) Y <- aeqSurv(model.response(mf)) strats <- attr(Terms, "specials")$strata if (length(strats)) { stemp <- untangle.specials(Terms, 'strata', 1) if (length(stemp$vars)==1) strata.keep <- mf[[stemp$vars]] else strata.keep <- strata(mf[,stemp$vars], shortlabel=TRUE) istrat <- as.numeric(strata.keep) } <> mf[[".strata."]] <- istrat return(mf) } } } @ \subsection{Exact partial likelihood} Let $r_i = \exp(X_i\beta)$ be the risk score for observation $i$. For one of the time points assume that there that there are $d$ tied deaths among $n$ subjects at risk. For convenience we will index them as $i= 1,\ldots,d$ in the $n$ at risk. Then for the exact parial likelihood, the contribution at this time point is \begin{align*} L &= \sum_{i=1}^d \log(r_i) - \log(D) \\ \frac{\partial L}{\partial \beta_j} &= x_{ij} - (1/D) \frac{\partial D}{\partial \beta_j} \\ \frac{\partial^2 L}{\partial \beta_j \partial \beta_k} &= (1/D^2)\left[D\frac{\partial^2D}{\partial \beta_j \partial \beta_k} - \frac{\partial D}{\partial \beta_j}\frac{\partial D}{\partial \beta_k} \right] \end{align*} The hard part of this computation is $D$, which is a sum \begin{equation*} D = \sum_{S(d,n)} r_{s_1}r_{s_2} \ldots r_{s_d} \end{equation*} where $S(d,n)$ is the set of all possible subsets of size $d$ from $n$ objects, and $s_1, s_2, \ldots$ indexes the current selection. So if $n=6$ and $d=2$ we would have the 15 pairs 12, 13, .... 56; for $n=5$ and $d=3$ there would be 10 triples 123, 124, 125, \ldots, 345. The brute force computation of all subsets can take a very long time. Gail et al \cite{Gail81} show simple recursion formulas that speed this up considerably. Let $D(d,n)$ be the denominator with $d$ deaths and $n$ subjects. Then \begin{align} D(d,n) &= r_nD(d-1, n-1) + D(d, n-1) \label{d0}\\ \frac{\partial D(d,n)}{\partial \beta_j} &= \frac{\partial D(d, n-1)}{\partial \beta_j} + r_n \frac{\partial D(d-1, n-1)}{\partial \beta_j} + x_{nj}r_n D(d-1, n-1) \label{d1}\\ \frac{\partial^2D(d,n}{\partial \beta_j \partial \beta_k} &= \frac{\partial^2D(d,n-1)}{\partial \beta_j \partial \beta_k} + r_n\frac{\partial^2D(d-1,n-1)}{\partial \beta_j \partial \beta_k} + x_{nj}r_n\frac{\partial D(d-1, n-1)}{\partial \beta_k} + \nonumber \\ & x_{nk}r_n\frac{\partial D(d-1, n-1)}{\partial \beta_j} + x_{nj}x_{nk}r_n D(d-1, n-1) \label{d2} \end{align} The above recursion is captured in the three routines below. The first calculates $D$. It is called with $d$, $n$, an array that will contain all the values of $D(d,n)$ computed so far, and the the first dimension of the array. The intial condition $D(0,n)=1$ is important to all three routines. <>= #define NOTDONE -1.1 double coxd0(int d, int n, double *score, double *dmat, int dmax) { double *dn; if (d==0) return(1.0); dn = dmat + (n-1)*dmax + d -1; /* pointer to dmat[d,n] */ if (*dn == NOTDONE) { /* still to be computed */ *dn = score[n-1]* coxd0(d-1, n-1, score, dmat, dmax); if (d>= double coxd1(int d, int n, double *score, double *dmat, double *d1, double *covar, int dmax) { int indx; indx = (n-1)*dmax + d -1; /*index to the current array member d1[d.n]*/ if (d1[indx] == NOTDONE) { /* still to be computed */ d1[indx] = score[n-1]* covar[n-1]* coxd0(d-1, n-1, score, dmat, dmax); if (d1) d1[indx] += score[n-1]* coxd1(d-1, n-1, score, dmat, d1, covar, dmax); } return(d1[indx]); } double coxd2(int d, int n, double *score, double *dmat, double *d1j, double *d1k, double *d2, double *covarj, double *covark, int dmax) { int indx; indx = (n-1)*dmax + d -1; /*index to the current array member d1[d,n]*/ if (d2[indx] == NOTDONE) { /*still to be computed */ d2[indx] = coxd0(d-1, n-1, score, dmat, dmax)*score[n-1] * covarj[n-1]* covark[n-1]; if (d1) d2[indx] += score[n-1] * ( coxd2(d-1, n-1, score, dmat, d1j, d1k, d2, covarj, covark, dmax) + covarj[n-1] * coxd1(d-1, n-1, score, dmat, d1k, covark, dmax) + covark[n-1] * coxd1(d-1, n-1, score, dmat, d1j, covarj, dmax)); } return(d2[indx]); } @ Now for the main body. Start with the dull part of the code: declarations. I use [[maxiter2]] for the S structure and [[maxiter]] for the variable within it, and etc for the other input arguments. All the input arguments except strata are read-only. The output beta vector starts as a copy of ibeta. <>= #include #include "survS.h" #include "survproto.h" #include <> SEXP coxexact(SEXP maxiter2, SEXP y2, SEXP covar2, SEXP offset2, SEXP strata2, SEXP ibeta, SEXP eps2, SEXP toler2) { int i,j,k; int iter; double **covar, **imat; /*ragged arrays */ double *time, *status; /* input data */ double *offset; int *strata; int sstart; /* starting obs of current strata */ double *score; double *oldbeta; double zbeta; double newlk=0; double temp; int halving; /*are we doing step halving at the moment? */ int nrisk =0; /* number of subjects in the current risk set */ int dsize, /* memory needed for one coxc0, coxc1, or coxd2 array */ dmemtot, /* amount needed for all arrays */ ndeath; /* number of deaths at the current time point */ double maxdeath; /* max tied deaths within a strata */ double dtime; /* time value under current examiniation */ double *dmem0, **dmem1, *dmem2; /* pointers to memory */ double *dtemp; /* used for zeroing the memory */ double *d1; /* current first derivatives from coxd1 */ double d0; /* global sum from coxc0 */ /* copies of scalar input arguments */ int nused, nvar, maxiter; double eps, toler; /* returned objects */ SEXP imat2, beta2, u2, loglik2; double *beta, *u, *loglik; SEXP rlist, rlistnames; int nprotect; /* number of protect calls I have issued */ <> <> <> <> } @ Setup is ordinary. Grab S objects and assign others. I use \verb!R_alloc! for temporary ones since it is released automatically on return. <>= nused = LENGTH(offset2); nvar = ncols(covar2); maxiter = asInteger(maxiter2); eps = asReal(eps2); /* convergence criteria */ toler = asReal(toler2); /* tolerance for cholesky */ /* ** Set up the ragged array pointer to the X matrix, ** and pointers to time and status */ covar= dmatrix(REAL(covar2), nused, nvar); time = REAL(y2); status = time +nused; strata = INTEGER(PROTECT(duplicate(strata2))); offset = REAL(offset2); /* temporary vectors */ score = (double *) R_alloc(nused+nvar, sizeof(double)); oldbeta = score + nused; /* ** create output variables */ PROTECT(beta2 = duplicate(ibeta)); beta = REAL(beta2); PROTECT(u2 = allocVector(REALSXP, nvar)); u = REAL(u2); PROTECT(imat2 = allocVector(REALSXP, nvar*nvar)); imat = dmatrix(REAL(imat2), nvar, nvar); PROTECT(loglik2 = allocVector(REALSXP, 5)); /* loglik, sctest, flag,maxiter*/ loglik = REAL(loglik2); nprotect = 5; @ The data passed to us has been sorted by strata, and reverse time within strata (longest subject first). The variable [[strata]] will be 1 at the start of each new strata. Separate strata are completely separate computations: time 10 in one strata and time 10 in another are not comingled. Compute the largest product (size of strata)* (max tied deaths in strata) for allocating scratch space. When computing $D$ it is advantageous to create all the intermediate values of $D(d,n)$ in an array since they will be used in the derivative calculation. Likewise, the first derivatives are used in calculating the second. Even more importantly, say we have a large data set. It will be sorted with the shortest times first. If there is a death with 30 at risk and another with 40 at risk, the intermediate sums we computed for the n=30 case are part of the computation for n=40. To make this work we need to index our matrices, within any strata, by the maximum number of tied deaths in the strata. We save this in the strata variable: first obs of a new strata has the number of events. And what if a strata had 0 events? We mark it with a 1. Note that the maxdeath variable is floating point. I had someone call this routine with a data set that gives an integer overflow in that situation. We now keep track of this further below and fail with a message. Such a run would take longer than forever to complete even if integer subscripts did not overflow. <>= strata[0] =1; /* in case the parent forgot (e.g., no strata case)*/ temp = 0; /* temp variable for dsize */ maxdeath =0; j=0; /* first obs of current stratum */ ndeath=0; nrisk=0; for (i=0; i0) { /* assign data for the prior stratum, just finished */ /* If maxdeath <2 leave the strata alone at it's current value of 1 */ if (maxdeath >1) strata[j] = maxdeath; j = i; if (maxdeath*nrisk > temp) temp = maxdeath*nrisk; } maxdeath =0; /* max tied deaths at any time in this strata */ nrisk=0; ndeath =0; } dtime = time[i]; ndeath =0; /*number tied here */ while (time[i] ==dtime) { nrisk++; ndeath += status[i]; i++; if (i>=nused || strata[i] >0) break; /* don't cross strata */ } if (ndeath > maxdeath) maxdeath = ndeath; } /* data for the final stratum */ if (maxdeath*nrisk > temp) temp = maxdeath*nrisk; if (maxdeath >1) strata[j] = maxdeath; /* Now allocate memory for the scratch arrays Each per-variable slice is of size dsize */ dsize = temp; temp = temp * ((nvar*(nvar+1))/2 + nvar + 1); dmemtot = dsize * ((nvar*(nvar+1))/2 + nvar + 1); if (temp != dmemtot) { /* the subscripts will overflow */ error("(number at risk) * (number tied deaths) is too large"); } dmem0 = (double *) R_alloc(dmemtot, sizeof(double)); /*pointer to memory */ dmem1 = (double **) R_alloc(nvar, sizeof(double*)); dmem1[0] = dmem0 + dsize; /*points to the first derivative memory */ for (i=1; i>= sstart =0; /* a line to make gcc stop complaining */ for (i=0; i0) { /* first obs of a new strata */ maxdeath= strata[i]; dtemp = dmem0; for (j=0; j=nused || strata[i] >0) break; } /* We have added up over the death time, now process it */ if (ndeath >0) { /* Add to the loglik */ d0 = coxd0(ndeath, nrisk, score+sstart, dmem0, maxdeath); R_CheckUserInterrupt(); newlk -= log(d0); dmem2 = dmem0 + (nvar+1)*dsize; /*start for the second deriv memory */ for (j=0; j 3) R_CheckUserInterrupt(); u[j] -= d1[j]; for (k=0; k<= j; k++) { /* second derivative*/ temp = coxd2(ndeath, nrisk, score+sstart, dmem0, dmem1[j], dmem1[k], dmem2, covar[j] + sstart, covar[k] + sstart, maxdeath); if (ndeath > 5) R_CheckUserInterrupt(); imat[k][j] += temp/d0 - d1[j]*d1[k]; dmem2 += dsize; } } } } @ Do the first iteration of the solution. The first iteration is different in 3 ways: it is used to set the initial log-likelihood, to compute the score test, and we pay no attention to convergence criteria or diagnositics. (I expect it not to converge in one iteration). <>= /* ** do the initial iteration step */ newlk =0; for (i=0; i> loglik[0] = newlk; /* save the loglik for iteration zero */ loglik[1] = newlk; /* and it is our current best guess */ /* ** update the betas and compute the score test */ for (i=0; i> } /* ** Never, never complain about convergence on the first step. That way, ** if someone has to they can force one iter at a time. */ for (i=0; i>= halving =0 ; /* =1 when in the midst of "step halving" */ for (iter=1; iter<=maxiter; iter++) { newlk =0; for (i=0; i> /* am I done? ** update the betas and test for convergence */ loglik[3] = cholesky2(imat, nvar, toler); if (fabs(1-(loglik[1]/newlk))<= eps && halving==0) { /* all done */ loglik[1] = newlk; <> } if (iter==maxiter) break; /*skip the step halving and etc */ if (newlk < loglik[1]) { /*it is not converging ! */ halving =1; for (i=0; i> @ The common code for finishing. Invert the information matrix, copy it to be symmetric, and put together the output structure. <>= loglik[4] = iter; chinv2(imat, nvar); for (i=1; i>= agreg.fit <- function(x, y, strata, offset, init, control, weights, method, rownames, resid=TRUE, nocenter=NULL) { nvar <- ncol(x) event <- y[,3] if (all(event==0)) stop("Can't fit a Cox model with 0 failures") if (missing(offset) || is.null(offset)) offset <- rep(0.0, nrow(y)) if (missing(weights)|| is.null(weights))weights<- rep(1.0, nrow(y)) else if (any(weights<=0)) stop("Invalid weights, must be >0") else weights <- as.vector(weights) # Find rows to be ignored. We have to match within strata: a # value that spans a death in another stratum, but not it its # own, should be removed. Hence the per stratum delta if (length(strata) ==0) {y1 <- y[,1]; y2 <- y[,2]} else { if (is.numeric(strata)) strata <- as.integer(strata) else strata <- as.integer(as.factor(strata)) delta <- strata* (1+ max(y[,2]) - min(y[,1])) y1 <- y[,1] + delta y2 <- y[,2] + delta } event <- y[,3] > 0 dtime <- sort(unique(y2[event])) indx1 <- findInterval(y1, dtime) indx2 <- findInterval(y2, dtime) # indx1 != indx2 for any obs that spans an event time ignore <- (indx1 == indx2) nused <- sum(!ignore) # Sort the data (or rather, get a list of sorted indices) # For both stop and start times, the indices go from last to first if (length(strata)==0) { sort.end <- order(ignore, -y[,2]) -1L #indices start at 0 for C code sort.start<- order(ignore, -y[,1]) -1L strata <- rep(0L, nrow(y)) } else { sort.end <- order(ignore, strata, -y[,2]) -1L sort.start<- order(ignore, strata, -y[,1]) -1L } if (is.null(nvar) || nvar==0) { # A special case: Null model. Just return obvious stuff # To keep the C code to a small set, we call the usual routines, but # with a dummy X matrix and 0 iterations nvar <- 1 x <- matrix(as.double(1:nrow(y)), ncol=1) #keep the .C call happy maxiter <- 0 nullmodel <- TRUE if (length(init) !=0) stop("Wrong length for inital values") init <- 0.0 #dummy value to keep a .C call happy (doesn't like 0 length) } else { nullmodel <- FALSE maxiter <- control$iter.max if (is.null(init)) init <- rep(0., nvar) if (length(init) != nvar) stop("Wrong length for inital values") } # 2021 change: pass in per covariate centering. This gives # us more freedom to experiment. Default is to leave 0/1 variables alone if (is.null(nocenter)) zero.one <- rep(FALSE, ncol(x)) zero.one <- apply(x, 2, function(z) all(z %in% nocenter)) # the returned value of agfit$coef starts as a copy of init, so make sure # is is a vector and not a matrix; as.double suffices. # Solidify the storage mode of other arguments storage.mode(y) <- storage.mode(x) <- "double" storage.mode(offset) <- storage.mode(weights) <- "double" agfit <- .Call(Cagfit4, nused, y, x, strata, weights, offset, as.double(init), sort.start, sort.end, as.integer(method=="efron"), as.integer(maxiter), as.double(control$eps), as.double(control$toler.chol), ifelse(zero.one, 0L, 1L)) # agfit4 centers variables within strata, so does not return a vector # of means. Use a fill in consistent with other coxph routines agmeans <- ifelse(zero.one, 0, colMeans(x)) <> <> rval } @ Upon return we need to clean up three simple things. The first is the rare case that the agfit routine failed. These cases are rare, usually involve an overflow or underflow, and we encourage users to let us have a copy of the data when it occurs. (They end up in the \code{fail} directory of the library.) The second is that if any of the covariates were redudant then this will be marked by zeros on the diagonal of the variance matrix. Replace these coefficients and their variances with NA. The last is to post a warning message about possible infinite coefficients. The algorithm for determining this is unreliable, unfortunately. Sometimes coefficients are marked as infinite when the solution is not tending to infinity (usually associated with a very skewed covariate), and sometimes one that is tending to infinity is not marked. Que sera sera. Don't complain if the user asked for only one iteration; they will already know that it has not converged. <>= vmat <- agfit$imat coef <- agfit$coef if (agfit$flag[1] < nvar) which.sing <- diag(vmat)==0 else which.sing <- rep(FALSE,nvar) if (maxiter >1) { infs <- abs(agfit$u %*% vmat) if (any(!is.finite(coef)) || any(!is.finite(vmat))) stop("routine failed due to numeric overflow.", "This should never happen. Please contact the author.") if (agfit$flag[4] > 0) warning("Ran out of iterations and did not converge") else { infs <- (!is.finite(agfit$u) | infs > control$toler.inf*(1+ abs(coef))) if (any(infs)) warning(paste("Loglik converged before variable ", paste((1:nvar)[infs],collapse=","), "; beta may be infinite. ")) } } @ The last of the code is very standard. Compute residuals and package up the results. One design decision is that we return all $n$ residuals and predicted values, even though the model fit ignored useless observations. (All those obs have a residual of 0). <>= lp <- as.vector(x %*% coef + offset - sum(coef * agmeans)) if (resid) { if (any(lp > log(.Machine$double.xmax))) { # prevent a failure message due to overflow # this occurs with near-infinite coefficients temp <- lp + log(.Machine$double.xmax) - (1 + max(lp)) score <- exp(temp) } else score <- exp(lp) residuals <- .Call(Cagmart3, nused, y, score, weights, strata, sort.start, sort.end, as.integer(method=='efron')) names(residuals) <- rownames } # The if-then-else below is a real pain in the butt, but the tccox # package's test suite assumes that the ORDER of elements in a coxph # object will never change. # if (nullmodel) { rval <- list(loglik=agfit$loglik[2], linear.predictors = offset, method= method, class = c("coxph.null", 'coxph') ) if (resid) rval$residuals <- residuals } else { names(coef) <- dimnames(x)[[2]] if (maxiter > 0) coef[which.sing] <- NA # always leave iter=0 alone flag <- agfit$flag names(flag) <- c("rank", "rescale", "step halving", "convergence") if (resid) { rval <- list(coefficients = coef, var = vmat, loglik = agfit$loglik, score = agfit$sctest, iter = agfit$iter, linear.predictors = as.vector(lp), residuals = residuals, means = agmeans, first = agfit$u, info = flag, method= method, class = "coxph") } else { rval <- list(coefficients = coef, var = vmat, loglik = agfit$loglik, score = agfit$sctest, iter = agfit$iter, linear.predictors = as.vector(lp), means = agmeans, first = agfit$u, info = flag, method = method, class = "coxph") } rval } @ The details of the C code contain the more challenging part of the computations. It starts with the usual dull stuff. My standard coding style for a variable zed to to use [[zed2]] as the variable name for the R object, and [[zed]] for the pointer to the contents of the object, i.e., what the C code will manipulate. For the matrix objects I make use of ragged arrays, this allows for reference to the i,j element as \code{cmat[i][j]} and makes for more readable code. <>= #include #include "survS.h" #include "survproto.h" SEXP agfit4(SEXP nused2, SEXP surv2, SEXP covar2, SEXP strata2, SEXP weights2, SEXP offset2, SEXP ibeta2, SEXP sort12, SEXP sort22, SEXP method2, SEXP maxiter2, SEXP eps2, SEXP tolerance2, SEXP doscale2) { int i,j,k, person; int indx1, istrat, p, p1; int nrisk, nr; int nused, nvar; int rank=0, rank2, fail; /* =0 to keep -Wall happy */ double **covar, **cmat, **imat; /*ragged array versions*/ double *a, *oldbeta; double *scale; double *a2, **cmat2; double *eta; double denom, zbeta, risk; double dtime =0; /* initial value to stop a -Wall message */ double temp, temp2; double newlk =0; int halving; /*are we doing step halving at the moment? */ double tol_chol, eps; double meanwt; int deaths; double denom2, etasum; double recenter; /* inputs */ double *start, *tstop, *event; double *weights, *offset; int *sort1, *sort2, maxiter; int *strata; double method; /* saving this as double forces some double arithmetic */ int *doscale; /* returned objects */ SEXP imat2, beta2, u2, loglik2; double *beta, *u, *loglik; SEXP sctest2, flag2, iter2; double *sctest; int *flag, *iter; SEXP rlist; static const char *outnames[]={"coef", "u", "imat", "loglik", "sctest", "flag", "iter", ""}; int nprotect; /* number of protect calls I have issued */ /* get sizes and constants */ nused = asInteger(nused2); nvar = ncols(covar2); nr = nrows(covar2); /*nr = number of rows, nused = how many we use */ method= asInteger(method2); eps = asReal(eps2); tol_chol = asReal(tolerance2); maxiter = asInteger(maxiter2); doscale = INTEGER(doscale2); /* input arguments */ start = REAL(surv2); tstop = start + nr; event = tstop + nr; weights = REAL(weights2); offset = REAL(offset2); sort1 = INTEGER(sort12); sort2 = INTEGER(sort22); strata = INTEGER(strata2); /* ** scratch space ** nvar: a, a2, oldbeta, scale ** nvar*nvar: cmat, cmat2 ** nr: eta */ eta = (double *) R_alloc(nr + 4*nvar + 2*nvar*nvar, sizeof(double)); a = eta + nr; a2= a + nvar; scale = a2 + nvar; oldbeta = scale + nvar; /* ** Set up the ragged arrays ** covar2 might not need to be duplicated, even though ** we are going to modify it, due to the way this routine was ** was called. But check */ PROTECT(imat2 = allocMatrix(REALSXP, nvar, nvar)); nprotect =1; if (MAYBE_REFERENCED(covar2)) { PROTECT(covar2 = duplicate(covar2)); nprotect++; } covar= dmatrix(REAL(covar2), nr, nvar); imat = dmatrix(REAL(imat2), nvar, nvar); cmat = dmatrix(oldbeta+ nvar, nvar, nvar); cmat2= dmatrix(oldbeta+ nvar + nvar*nvar, nvar, nvar); /* ** create the output structures */ PROTECT(rlist = mkNamed(VECSXP, outnames)); nprotect++; beta2 = SET_VECTOR_ELT(rlist, 0, duplicate(ibeta2)); beta = REAL(beta2); u2 = SET_VECTOR_ELT(rlist, 1, allocVector(REALSXP, nvar)); u = REAL(u2); SET_VECTOR_ELT(rlist, 2, imat2); loglik2 = SET_VECTOR_ELT(rlist, 3, allocVector(REALSXP, 2)); loglik = REAL(loglik2); sctest2 = SET_VECTOR_ELT(rlist, 4, allocVector(REALSXP, 1)); sctest = REAL(sctest2); flag2 = SET_VECTOR_ELT(rlist, 5, allocVector(INTSXP, 4)); flag = INTEGER(flag2); for (i=0; i<4; i++) flag[i]=0; iter2 = SET_VECTOR_ELT(rlist, 6, allocVector(INTSXP, 1)); iter = INTEGER(iter2); /* ** Subtract the mean from each covar, as this makes the variance ** computation more stable. The mean is taken per stratum, ** the scaling is overall. */ for (i=0; i0) temp = temp2/temp; /* 1/scale */ else temp = 1.0; /* rare case of a constant covariate */ scale[i] = temp; for (person=0; person> <> } @ As we walk through the risk sets observations are both added and removed from a set of running totals. We have 6 running totals: \begin{itemize} \item sum of the weights, denom = $\sum w_i r_i$ \item totals for each covariate a[j] = $\sum w_ir_i x_{ij}$ \item totals for each covariate pair cmat[j,k]= $\sum w_ir_i x_{ij} x_{ik}$ \item the same three quantities, but only for times that are exactly tied with the current death time, named denom2, a2, cmat2. This allows for easy compuatation of the Efron approximation for ties. \end{itemize} At one point I spent a lot of time worrying about $r_i$ values that are too large, but it turns out that the overall scale of the weights does not really matter since they always appear as a ratio. (Assuming we avoid exponential overflow and underflow, of course.) What does get the code in trouble is when there are large and small weights and we get an update of (large + small) - large. For example suppose a data set has a time dependent covariate which grows with time and the data has values like below: \begin{center} \begin{tabular}{ccccc} time1 & time2 & status & x \\ \hline 0 & 90 & 1 & 1 \\ 0 & 105 & 0 & 2 \\ 100 & 120 & 1 & 50 \\ 100 & 124 & 0 & 51 \end{tabular} \end{center} The code moves from large times to small, so the first risk set has subjects 3 and 4, the second has 1 and 2. The original code would do removals only when necessary, i.e., at the event times of 120 and 90, and additions as they came along. This leads to adding in subjects 1 and 2 before the update at time 90 when observations 3 and 4 are removed; for a coefficient greater than about .6 this leads to a loss of all of the significant digits. The defense is to remove subjects from the risk set as early as possible, and defer additions for as long as possible. Every time we hit a new (unique) death time, and only then, update the totals: first remove any old observations no longer in the risk set and then add any new ones. One interesting edge case is observations that are not part of any risk set. (A call to survSplit with too fine a partition can create these, or using a subset of data that excluded some of the deaths.) Observations that are not part of any risk set add unnecessary noise since they will be added and then subtracted from all the totals, but the intermediate values are never used. If said observation had a large risk score this could be exceptionally bad. The parent routine has already dealt with such observations: their indices never appear in the sort1 or sort2 vector. The three primary quantities for the Cox model are the log-likelihood $L$, the score vector $U$ and the Hessian matrix $H$. \begin{align*} L &= \sum_i w_i \delta_i \left[\eta_i - \log(d(t)) \right] \\ d(t) &= \sum_j w_j r_j Y_j(t) \\ U_k &= \sum_i w_i \delta_i \left[ (X_{ik} - \mu_k(t_i)) \right] \\ \mu_k(t) &= \frac{\sum_j w_j r_j Y_j(t) X_{jk}} {d(t)} \\ H_{kl} &= \sum_i w_i \delta_i V_{kl}(t_i) \\ V_{kl}(t) &= \frac{\sum_j w_j r_j Y_j(t) [X_{jk} - \mu_k(t)] [X_{jl}- \mu_l(t)]} {d(t)} \\ &= \frac{\sum_j w_j r_j Y_j(t) X_{jk}X_{jl}} {d(t)} - d(t) \mu_k(t) \mu_l(t) \end{align*} In the above $\delta_i =1$ for an event and 0 otherwise, $w_i$ is the per subject weight, $\eta_i$ is the current linear predictor $X\beta$ for the subject, $r_i = \exp(\eta_i)$ is the risk score and $Y_i(t)$ is 1 if observation $i$ is at risk at time $t$. The vector $\mu(t)$ is the weighted mean of the covariates at time $t$ using a weight of $w r Y(t)$ for each subject, and $V(t)$ is the weighted variance matrix of $X$ at time $t$. Tied deaths and the Efron approximation add a small complication to the formula. Say there are three tied deaths at some particular time $t$. When calculating the denominator $d(t)$, mean $\mu(t)$ and variance $V(t)$ at that time the inclusion value $Y_i(t)$ is 0 or 1 for all other subjects, as usual, but for the three tied deaths Y(t) is taken to be 1 for the first death, 2/3 for the second, and 1/3 for the third. The idea is that if the tied death times were randomly broken by adding a small random amount then each of these three would be in the first risk set, have 2/3 chance of being in the second, and 1/3 chance of being in the risk set for the third death. In the code this means that at a death time we add the \code{denom2}, \code{a2} and \code{c2} portions in a little at at time: for three tied death the code will add in 1/3, update totals, add in another 1/3, update totals, then the last 1/3, and update totals. The variance formula is stable if $\mu$ is small relative to the total variance. This is guarranteed by having a working estimate $m$ of the mean along with the formula: \begin{align*} (1/n) \sum w_ir_i(x_i- \mu)^2 &= (1/n)\sum w_ir_i(x-m)^2 - (\mu -m)^2 \\ \mu &= (1/n) \sum w_ir_i (x_i -m)\\ n &= \sum w_ir_i \end{align*} A refinement of this is to scale the covariates, since the Cholesky decomposition can lose precision when variables are on vastly different scales. We do this centering and scaling once at the beginning of the calculation. Centering is done per strata --- what if someone had two strata and a covariate with mean 0 in the first but mean one million in the second? (Users do amazing things). Scaling is required to be a single value for each covariate, however. For a univariate model scaling does not add any precision. Weighted sums can still be unstable if the weights get out of hand. Because of the exponential $r_i = exp(\eta_i)$ the original centering of the $X$ matrix may not be enough. A particular example was a data set on hospital adverse events with ``number of nurse shift changes to date'' as a time dependent covariate. At any particular time point the covariate varied only by $\pm 3$ between subjects (weekends often use 12 hour nurse shifts instead of 8 hour). The regression coefficient was around 1 and the data duration was 11 weeks (about 200 shifts) so that $eta$ values could be over 100 even after centering. We keep a time dependent average of $\eta$ and use it to update a recentering constant as necessary. A case like this should be rare, but it is not as unusual as one might think. The last numerical problem is when one or more coefficients gets too large, leading to a huge weight exp(eta). This usually happens when a coefficient is tending to infinity, but can also be due to a bad step in the intermediate Newton-Raphson path. In the infinite coefficient case the log-likelihood trends to an asymptote and there is a race between three conditions: convergence of the loglik, singularity of the variance matrix, or an invalid log-likelihood. The first of these wins the race most of the time, especially if the data set is small, and is the simplest case. The last occurs when the denominator becomes $<0$ due to round off so that log(denom) is undefined, the second when extreme weights cause the second derivative to lose precision. In all 3 we revert to step halving, since a bad Newton-Raphson step can cause the same issues to arise. The next section of code adds up the totals for a given iteration. This is the workhorse. For a given death time all of the events tied at that time must be handled together, hence the main loop below proceeds in batches: \begin{enumerate} \item Find the time of the next death. Whenever crossing a stratum boundary, zero cetain intermediate sums. \item Remove all observations in the stratum with time1 $>$ dtime. When survSplit was used to create a data set, this will often remove all. If so we can rezero temporaries and regain precision. \item Add new observations to the risk set and to the death counts. \end{enumerate} <>= for (person=0; person> /* ** add any new subjects who are at risk ** denom2, a2, cmat2, meanwt and deaths count only the deaths */ denom2= 0; meanwt =0; deaths=0; for (i=0; i> risk = exp(eta[p] - recenter) * weights[p]; if (event[p] ==1 ){ deaths++; denom2 += risk; meanwt += weights[p]; newlk += weights[p]* (eta[p] - recenter); for (i=0; i> } /* end of accumulation loop */ @ The last step in the above loop adds terms to the loglik, score and information matrices. Assume that there were 3 tied deaths. The difference between the Efron and Breslow approximations is that for the Efron the three tied subjects are given a weight of 1/3 for the first, 2/3 for the second, and 3/3 for the third death; for the Breslow they get 3/3 for all of them. Note that \code{imat} is symmetric, and that the cholesky routine will utilize the upper triangle of the matrix as input, using the lower part for its own purposes. The inverse from \code{chinv} is also in the upper triangle. <>= /* ** Add results into u and imat for all events at this time point */ if (method==0 || deaths ==1) { /*Breslow */ denom += denom2; newlk -= meanwt*log(denom); /* sum of death weights*/ for (i=0; i>= /* ** subtract out the subjects whose start time is to the right ** If everyone is removed reset the totals to zero. (This happens when ** the survSplit function is used, so it is worth checking). */ for (; indx1>= /* ** We must avoid overflow in the exp function (~709 on Intel) ** and want to act well before that, but not take action very often. ** One of the case-cohort papers suggests an offset of -100 meaning ** that etas of 50-100 can occur in "ok" data, so make it larger ** than this. ** If the range of eta is more then log(1e16) = 37 then the data is ** hopeless: some observations will have effectively 0 weight. Keeping ** the mean sensible has sufficed to keep the max in check. */ if (fabs(etasum/nrisk - recenter) > 200) { flag[1]++; /* a count, for debugging/profiling purposes */ temp = etasum/nrisk - recenter; recenter = etasum/nrisk; if (denom > 0) { /* we can skip this if there is no one at risk */ if (fabs(temp) > 709) error("exp overflow due to covariates\n"); temp = exp(-temp); /* the change in scale, for all the weights */ denom *= temp; for (i=0; i>= /* main loop */ halving =0 ; /* =1 when in the midst of "step halving" */ fail =0; for (*iter=0; *iter<= maxiter; (*iter)++) { R_CheckUserInterrupt(); /* be polite -- did the user hit cntrl-C? */ <> if (*iter==0) { loglik[0] = newlk; loglik[1] = newlk; /* compute the score test, but don't corrupt u */ for (i=0; i0) break; for (i=0; i1 && ((newlk -loglik[1])/ fabs(loglik[1])) < -eps) { /* ** "Once more unto the breach, dear friends, once more; ..." **The last iteration above was worse than one of the earlier ones, ** by more than roundoff error. ** We need to use beta and imat at the last good value, not the ** last attempted value. We have tossed the old imat away, so ** recompute it. ** It will happen very rarely that we run out of iterations, and ** even less often that it is right in the middle of halving. */ for (i=0; i> rank2 = cholesky2(imat, nvar, tol_chol); } break; } if (fail >0 || newlk < loglik[1]) { /* ** The routine has not made progress past the last good value. */ halving++; flag[2]++; for (i=0; i>= flag[0] = rank; loglik[1] = newlk; chinv2(imat, nvar); for (i=0; i>= survfit.coxph <- function(formula, newdata, se.fit=TRUE, conf.int=.95, individual=FALSE, stype=2, ctype, conf.type=c("log", "log-log", "plain", "none", "logit", "arcsin"), censor=TRUE, start.time, id, influence=FALSE, na.action=na.pass, type, ...) { Call <- match.call() Call[[1]] <- as.name("survfit") #nicer output for the user object <- formula #'formula' because it has to match survfit <> <> <> <> <> if (missing(newdata)) { if (inherits(formula, "coxphms")) stop ("newdata is required for multi-state models") risk2 <- 1 } else { if (length(object$means)) risk2 <- exp(c(x2 %*% beta) + offset2 - xcenter) else risk2 <- exp(offset2 - xcenter) } <> <> } @ The third line \code{as.name('survfit')} causes the printout to say `survfit' instead of `survfit.coxph'. %' The setup for the has three main phases, first of course to sort out the options the user has given us, second to rebuild the data frame, X matrix, etc from the original Cox model, and third to create variables from the new data set. In the code below x2, y2, strata2, id2, etc. are variables from the new data, X, Y, strata etc from the old. One exception to the pattern is id= argument, oldid = id from original data, id2 = id from new. If the newdata argument is missing we use \code{object\$means} as the default value. This choice has lots of statistical shortcomings, particularly in a stratified model, but is common in other packages and a historic option here. If stype is missing we use the standard approach of exp(cumulative hazard), and ctype is pulled from the Cox model. That is, the \code{coxph} computation used for \code{ties='breslow'} is the same as the Nelson-Aalen hazard estimate, and the Efron approximation the tie-corrected hazard. One particular special case (that gave me fits for a while) is when there are non-heirarchical models, for example \code{~ age + age:sex}. The fit of such a model will \emph{not} be the same using the variable \code{age2 <- age-50}; I originally thought it was a flaw induced by my subtraction. The routine simply cannot give a sensible curve for a model like this. The issue continued to surprise me each time I rediscovered it, leading to an error message for my own protection. I'm not convinced at this time that there is a sensible survival curve that \emph{could} be calculated for such a model. A model with \code{age + age:strata(sex)} will be ok, because the coxph routine treats this last term as though it had a * in it, i.e., fits a stratified model. <>= Terms <- terms(object) robust <- !is.null(object$naive.var) # did the coxph model use robust var? if (!is.null(attr(object$terms, "specials")$tt)) stop("The survfit function can not process coxph models with a tt term") if (!missing(type)) { # old style argument if (!missing(stype) || !missing(ctype)) warning("type argument ignored") else { temp1 <- c("kalbfleisch-prentice", "aalen", "efron", "kaplan-meier", "breslow", "fleming-harrington", "greenwood", "tsiatis", "exact") survtype <- match(match.arg(type, temp1), temp1) stype <- c(1,2,2,1,2,2,2,2,2)[survtype] if (stype!=1) ctype <-c(1,1,2,1,1,2,1,1,1)[survtype] } } if (missing(ctype)) { # Use the appropriate one from the model temp1 <- match(object$method, c("exact", "breslow", "efron")) ctype <- c(1,1,2)[temp1] } else if (!(ctype %in% 1:2)) stop ("ctype must be 1 or 2") if (!(stype %in% 1:2)) stop("stype must be 1 or 2") if (!se.fit) conf.type <- "none" else conf.type <- match.arg(conf.type) tfac <- attr(Terms, 'factors') temp <- attr(Terms, 'specials')$strata has.strata <- !is.null(temp) if (has.strata) { stangle = untangle.specials(Terms, "strata") #used multiple times, later # Toss out strata terms in tfac before doing the test 1 line below, as # strata end up in the model with age:strat(grp) terms or *strata() terms # (There might be more than one strata term) for (i in temp) tfac <- tfac[,tfac[i,] ==0] # toss out strata terms } if (any(tfac >1)) stop("not able to create a curve for models that contain an interaction without the lower order effect") Terms <- object$terms n <- object$n[1] if (!has.strata) strata <- NULL else strata <- object$strata if (!missing(individual)) warning("the `id' option supersedes `individual'") missid <- missing(id) # I need this later, and setting id below makes # "missing(id)" always false if (!missid) individual <- TRUE else if (missid && individual) id <- rep(0L,n) #dummy value else id <- NULL if (individual & missing(newdata)) { stop("the id option only makes sense with new data") } @ In two places below we need to know if there are strata by covariate interactions, which requires looking at attributes of the terms object. The factors attribute will have a row for the strata variable, or maybe more than one (multiple strata terms are legal). If it has a 1 in a column that corresponds to something of order 2 or greater, that is a strata by covariate interaction. <>= if (has.strata) { temp <- attr(Terms, "specials")$strata factors <- attr(Terms, "factors")[temp,] strata.interaction <- any(t(factors)*attr(Terms, "order") >1) } @ I need to retrieve a copy of the original data. We always need the $X$ matrix and $y$, both of which might be found in the data object. If the fit was a multistate model, the original call included either strata, offset, weights, or id, or if either $x$ or $y$ are missing from the \code{coxph} object, then the model frame will need to be reconstructed. We have to use \code{object['x'}] instead of \texttt{object\$x} since the latter will pick off the \code{xlevels} component if the \code{x} component is missing (which is the default). <>= coxms <- inherits(object, "coxphms") if (coxms || is.null(object$y) || is.null(object[['x']]) || !is.null(object$call$weights) || !is.null(object$call$id) || (has.strata && is.null(object$strata)) || !is.null(attr(object$terms, 'offset'))) { mf <- stats::model.frame(object) } else mf <- NULL #useful for if statements later @ For a single state model we can grab the X matrix off the model frame, for multistate some more work needs to be done. We have to repeat some lines from coxph, but to do that we need some further material. We prefer \code{object\$y} to model.response, since the former will have been passed through aeqSurv with the options the user specified. For a multi-state model, however, we do have to recreate since the saved y has been expanded. In that case observe the saved status of timefix. Old saved objects might not have that element, if missing assume TRUE. <>= position <- NULL Y <- object[['y']] if (is.null(mf)) { weights <- object$weights # let offsets/weights be NULL until needed offset <- NULL X <- object[['x']] } else { weights <- model.weights(mf) offset <- model.offset(mf) X <- model.matrix.coxph(object, data=mf) if (is.null(Y) || coxms) { Y <- model.response(mf) if (is.null(object$timefix) || object$timefix) Y <- aeqSurv(Y) } oldid <- model.extract(mf, "id") if (length(oldid) && ncol(Y)==3) position <- survflag(Y, oldid) else position <- NULL if (!coxms && (nrow(Y) != object$n[1])) stop("Failed to reconstruct the original data set") if (has.strata) { if (length(strata)==0) { if (length(stangle$vars) ==1) strata <- mf[[stangle$vars]] else strata <- strata(mf[, stangle$vars], shortlabel=TRUE) } } } @ If a model frame was created, then it is trivial to grab \code{y} from the new frame and compare it to \code{object\$y} from the original one. This is to avoid nonsense results that arise when someone changes the data set under our feet. We can only check the size: with the addition of aeqSurv other packages were being flagged for tiny discrepancies. Later note: this check does not work for multi-state models, and we don't \emph{have} to have it. Removed by using if (FALSE) so as to preserve the code for future consideration. <>= if (FALSE) { if (!is.null(mf)){ y2 <- object[['y']] if (!is.null(y2)) { if (ncol(y2) != ncol(Y) || length(y2) != length(Y)) stop("Could not reconstruct the y vector") } } } type <- attr(Y, 'type') if (!type %in% c("right", "counting", "mright", "mcounting")) stop("Cannot handle \"", type, "\" type survival data") if (!missing(start.time)) { if (!is.numeric(start.time) || length(start.time) > 1) stop("start.time must be a single numeric value") # Start the curves after start.time # To do so, remove any rows of the data with an endpoint before that # time. if (ncol(Y)==3) { keep <- Y[,2] > start.time Y[keep,1] <- pmax(Y[keep,1], start.time) } else keep <- Y[,1] > start.time if (!any(Y[keep, ncol(Y)]==1)) stop("start.time argument has removed all endpoints") Y <- Y[keep,,drop=FALSE] X <- X[keep,,drop=FALSE] if (!is.null(offset)) offset <- offset[keep] if (!is.null(weights)) weights <- weights[keep] if (!is.null(strata)) strata <- strata[keep] if (length(id) >0 ) id <- id[keep] if (length(position) >0) position <- position[keep] n <- nrow(Y) } @ In the above code we see id twice. The first, kept as \code{oldid} is the identifier variable for subjects in the original data set, and is needed whenever it contained subjects with more than one row. The second is the user variable of this call, and is used to define multiple rows for a new subject. The latter usage should be rare but we need to allow for it. If a variable is deemed redundant the \code{coxph} routine will have set its coefficient to NA as a marker. We want to ignore that coefficient: treating it as a zero has the desired effect. Another special case is a null model, having either ~1 or only an offset on the right hand side. In that case we create a dummy covariate to allow the rest of the code to work without special if/else. The last special case is a model with a sparse frailty term. We treat the frailty coefficients as 0 variance (in essence as an offset). The frailty is removed from the model variables but kept in the risk score. This isn't statistically very defensible, but it is backwards compatatble. %' A non-sparse frailty does not need special code and works out like any other variable. Center the risk scores by subtracting $ \overline x \hat\beta$ from each. The reason for this is to avoid huge values when calculating $\exp(X\beta)$; this would happen if someone had a variable with a mean of 1000 and a variance of 1. Any constant can be subtracted, mathematically the results are identical as long as the same values are subtracted from the old and new $X$ data. The mean is used because it is handy, we just need to get $X\beta$ in the neighborhood of zero. <>= if (length(object$means) ==0) { # a model with only an offset term # Give it a dummy X so the rest of the code goes through # (This case is really rare) # se.fit <- FALSE X <- matrix(0., nrow=n, ncol=1) if (is.null(offset)) offset <- rep(0, n) xcenter <- mean(offset) coef <- 0.0 varmat <- matrix(0.0, 1, 1) risk <- rep(exp(offset- mean(offset)), length=n) } else { varmat <- object$var beta <- ifelse(is.na(object$coefficients), 0, object$coefficients) if (is.null(offset)) xcenter <- sum(object$means * beta) else xcenter <- sum(object$means * beta)+ mean(offset) if (!is.null(object$frail)) { keep <- !grepl("frailty(", dimnames(X)[[2]], fixed=TRUE) X <- X[,keep, drop=F] } if (is.null(offset)) risk <- c(exp(X%*% beta - xcenter)) else risk <- c(exp(X%*% beta + offset - xcenter)) } @ The \code{risk} vector and \code{x} matrix come from the original data, and are the raw data for the survival curve and its variance. We also need the risk score $\exp(X\beta)$ for the target subject(s). \begin{itemize} \item For predictions with time-dependent covariates the user will have either included an \code{id} statement (newer style) or specified the \code{individual=TRUE} option. If the latter, then \code{newdata} is presumed to contain only a single indivual represented by multiple rows. If the former then the \code{id} variable marks separate individuals. In either case we need to retrieve the covariates, strata, and repsonse from the new data set. \item For ordinary predictions only the covariates are needed. \item If newdata is not present we assume that this is the ordinary case, and use the value of \code{object\$means} as the default covariate set. This is not ideal statistically since many users view this as an ``average'' survival curve, which it is not. \end{itemize} When grabbing [newdata] we want to use model.frame processing, both to handle missing values correctly and, perhaps more importantly, to correctly map any factor variables between the original fit and the new data. (The new data will often have only one of the original levels represented.) Also, we want to correctly handle data-dependent nonlinear terms such as ns and pspline. However, the simple call found in predict.lm, say, \code{model.frame(Terms, data=newdata, ..} isn't used here for a few reasons. The first is a decision on our part that the user should not have to include unused terms in the newdata: sometimes we don't need the response and sometimes we do. Second, if there are strata, the user may or may not have included strata variables in their data set and we need to act accordingly. The third is that we might have an \code{id} statement in this call, which is another variable to be fetched. At one time we dealt with cluster() terms in the formula, but the coxph routine has already removed those for us. Finally, note that there is no ability to use sparse frailties and newdata together; it is a hard case and so rare as to not be worth it. First, remove unnecessary terms from the orginal model formula. If \code{individual} is false then the repsonse variable can go. The dataClasses and predvars attributes, if present, have elements in the same order as the first dimension of the ``factors'' attribute of the terms. Subscripting the terms argument does not preserve dataClasses or predvars, however. Use the pre and post subscripting factors attribute to determine what elements of them to keep. The predvars component is a call objects with one element for each term in the formula, so y ~ age + ns(height) would lead to a predvars of length 4, element 1 is the call itself, 2 would be y, etc. The dataClasses object is a simple list. <>= if (missing(newdata)) { # If the model has interactions, print out a long warning message. # People may hate it, but I don't see another way to stamp out these # bad curves without backwards-incompatability. # I probably should complain about factors too (but never in a strata # or cluster term). if (any(attr(Terms, "order") > 1) ) warning("the model contains interactions; the default curve based on columm means of the X matrix is almost certainly not useful. Consider adding a newdata argument.") if (length(object$means)) { mf2 <- as.list(object$means) #create a dummy newdata names(mf2) <- names(object$coefficients) mf2 <- as.data.frame(mf2) x2 <- matrix(object$means, 1) } else { # nothing but an offset mf2 <- data.frame(X=0) x2 <- 0 } offset2 <- 0 found.strata <- FALSE } else { if (!is.null(object$frail)) stop("Newdata cannot be used when a model has frailty terms") Terms2 <- Terms if (!individual) Terms2 <- delete.response(Terms) <> } @ For backwards compatability, I allow someone to give an ordinary vector instead of a data frame (when only one curve is required). In this case I also need to verify that the elements have a name. Then turn it into a data frame, like it should have been from the beginning. (Documentation of this ability has been suppressed, however. I'm hoping people forget it ever existed.) <>= if (is.vector(newdata, "numeric")) { if (individual) stop("newdata must be a data frame") if (is.null(names(newdata))) { stop("Newdata argument must be a data frame") } newdata <- data.frame(as.list(newdata), stringsAsFactors=FALSE) } @ Finally get my new model frame mf2. We allow the user to leave out any strata() variables if they so desire, \emph{if} there are no strata by covariate interactions. How does one check if the strata variables are or are not available in the call? My first attempt at this was to wrap the call in a try() construct and see if it failed. This doesn't work. \begin{itemize} \item What if there is no strata variable in newdata, but they do have, by bad luck, a variable of the same name in their main directory? \item It would seem like changing the environment to NULL would be wise, so that we don't find variables anywhere but in the data argument, a sort of sandboxing. Not wise: you then won't find functions like ``log''. \item We don't dare modify the environment of the formula at all. It is needed for the sneaky caller who uses his own function inside the formula, 'mycosine' say, and that function can only be found if we retain the environment. \end{itemize} One way out of this is to evaluate each of the strata terms (there can be more than one) one at a time, in an environment that knows nothing except "list" and a fake definition of "strata", and newdata. Variables that are part of the global environment won't be found. I even watch out for the case of either "strata" or "list" is the name of the stratification variable, which causes my fake strata function to return a function when said variable is not in newdata. The variable found.strata is true if ALL the strata are found, set it to false if any are missing. <>= if (has.strata) { found.strata <- TRUE tempenv <- new.env(, parent=emptyenv()) assign("strata", function(..., na.group, shortlabel, sep) list(...), envir=tempenv) assign("list", list, envir=tempenv) for (svar in stangle$vars) { temp <- try(eval(parse(text=svar), newdata, tempenv), silent=TRUE) if (!is.list(temp) || any(unlist(lapply(temp, class))== "function")) found.strata <- FALSE } if (!found.strata) { ss <- untangle.specials(Terms2, "strata") Terms2 <- Terms2[-ss$terms] } } tcall <- Call[c(1, match(c('id', "na.action"), names(Call), nomatch=0))] tcall$data <- newdata tcall$formula <- Terms2 tcall$xlev <- object$xlevels[match(attr(Terms2,'term.labels'), names(object$xlevels), nomatch=0)] tcall[[1L]] <- quote(stats::model.frame) mf2 <- eval(tcall) @ Now, finally, extract the \code{x2} matrix from the just-created frame. <>= if (has.strata && found.strata) { #pull them off temp <- untangle.specials(Terms2, 'strata') strata2 <- strata(mf2[temp$vars], shortlabel=TRUE) strata2 <- factor(strata2, levels=levels(strata)) if (any(is.na(strata2))) stop("New data set has strata levels not found in the original") # An expression like age:strata(sex) will have temp$vars= "strata(sex)" # and temp$terms = integer(0). This does not work as a subscript if (length(temp$terms) >0) Terms2 <- Terms2[-temp$terms] } else strata2 <- factor(rep(0, nrow(mf2))) if (!robust) cluster <- NULL if (individual) { if (missing(newdata)) stop("The newdata argument must be present when individual=TRUE") if (!missid) { #grab the id variable id2 <- model.extract(mf2, "id") if (is.null(id2)) stop("id=NULL is an invalid argument") } else id2 <- rep(1, nrow(mf2)) x2 <- model.matrix(Terms2, mf2)[,-1, drop=FALSE] #no intercept if (length(x2)==0) stop("Individual survival but no variables") offset2 <- model.offset(mf2) if (length(offset2) ==0) offset2 <- 0 y2 <- model.extract(mf2, 'response') if (attr(y2,'type') != type) stop("Survival type of newdata does not match the fitted model") if (attr(y2, "type") != "counting") stop("Individual=TRUE is only valid for counting process data") y2 <- y2[,1:2, drop=F] #throw away status, it's never used } else if (missing(newdata)) { if (has.strata && strata.interaction) stop ("Models with strata by covariate interaction terms require newdata") offset2 <- 0 if (length(object$means)) { x2 <- matrix(object$means, nrow=1, ncol=ncol(X)) } else { # model with only an offset and no new data: very rare case x2 <- matrix(0.0, nrow=1, ncol=1) # make a dummy x2 } } else { offset2 <- model.offset(mf2) if (length(offset2) >0) offset2 <- offset2 else offset2 <- 0 x2 <- model.matrix(Terms2, mf2)[,-1, drop=FALSE] #no intercept } @ <>= if (individual) { result <- coxsurv.fit(ctype, stype, se.fit, varmat, cluster, Y, X, weights, risk, position, strata, oldid, y2, x2, risk2, strata2, id2) } else { result <- coxsurv.fit(ctype, stype, se.fit, varmat, cluster, Y, X, weights, risk, position, strata, oldid, y2, x2, risk2) if (has.strata && found.strata) { if (is.matrix(result$surv)) { <> } } } @ The final bit of work. If the newdata arg contained strata then the user should not get a matrix of survival curves containing every newdata obs * strata combination, but rather a vector of curves, each one with the appropriate strata. It was faster to compute them all, however, than to use the individual=T logic. So now pick off the bits we want. The names of the curves will be the rownames of the newdata arg, if they exist. <>= nr <- nrow(result$surv) #a vector if newdata had only 1 row indx1 <- split(1:nr, rep(1:length(result$strata), result$strata)) rows <- indx1[as.numeric(strata2)] #the rows for each curve indx2 <- unlist(rows) #index for time, n.risk, n.event, n.censor indx3 <- as.integer(strata2) #index for n and strata for(i in 2:length(rows)) rows[[i]] <- rows[[i]]+ (i-1)*nr #linear subscript indx4 <- unlist(rows) #index for surv and std.err temp <- result$strata[indx3] names(temp) <- row.names(mf2) new <- list(n = result$n[indx3], time= result$time[indx2], n.risk= result$n.risk[indx2], n.event=result$n.event[indx2], n.censor=result$n.censor[indx2], strata = temp, surv= result$surv[indx4], cumhaz = result$cumhaz[indx4]) if (se.fit) new$std.err <- result$std.err[indx4] result <- new @ Finally, the last (somewhat boring) part of the code. First, if given the argument \code{censor=FALSE} we need to remove all the time points from the output at which there was only censoring activity. This action is mostly for backwards compatability with older releases that never returned censoring times. Second, add in the variance and the confidence intervals to the result. The code is nearly identical to that in survfitKM. <>= if (!censor) { kfun <- function(x, keep){ if (is.matrix(x)) x[keep,,drop=F] else if (length(x)==length(keep)) x[keep] else x} keep <- (result$n.event > 0) if (!is.null(result$strata)) { temp <- factor(rep(names(result$strata), result$strata), levels=names(result$strata)) result$strata <- c(table(temp[keep])) } result <- lapply(result, kfun, keep) } result$logse = TRUE # this will migrate further in if (se.fit && conf.type != "none") { ci <- survfit_confint(result$surv, result$std.err, logse=result$logse, conf.type, conf.int) result <- c(result, list(lower=ci$lower, upper=ci$upper, conf.type=conf.type, conf.int=conf.int)) } if (!missing(start.time)) result$start.time <- start.time result$call <- Call class(result) <- c('survfitcox', 'survfit') result @ % % Second part of coxsurv.Rnw, broken in two to make it easier for me % to work with emacs. Now, we're ready to do the main compuation. %' The code has gone through multiple iteration as options and complexity increased. Computations are separate for each strata, and each strata will have a different number of time points in the result. Thus we can't preallocate a matrix. Instead we generate an empty list, %' one per strata, and then populate it with the survival curves. At the end we unlist the individual components one by one. This is memory efficient, the number of curves is usually small enough that the "for" loop is no great cost, and it's easier to see what's going on than C code. The computational exception is a model with thousands of strata, e.g., a matched logistic, but in that case survival curves are useless. (That won't stop some users from trying it though.) First, compute the baseline survival curves for each strata. If the strata was a factor produce output curves in that order, otherwise in sorted order. This fitting routine was set out as a separate function for the sake of the rms package. They want to utilize the computation, but have a diffferent process to create the x and y data. <>= coxsurv.fit <- function(ctype, stype, se.fit, varmat, cluster, y, x, wt, risk, position, strata, oldid, y2, x2, risk2, strata2, id2, unlist=TRUE) { if (missing(strata) || length(strata)==0) strata <- rep(0L, nrow(y)) if (is.factor(strata)) ustrata <- levels(strata) else ustrata <- sort(unique(strata)) nstrata <- length(ustrata) survlist <- vector('list', nstrata) names(survlist) <- ustrata survtype <- if (stype==1) 1 else ctype+1 vartype <- survtype if (is.null(wt)) wt <- rep(1.0, nrow(y)) if (is.null(strata)) strata <- rep(1L, nrow(y)) for (i in 1:nstrata) { indx <- which(strata== ustrata[i]) survlist[[i]] <- agsurv(y[indx,,drop=F], x[indx,,drop=F], wt[indx], risk[indx], survtype, vartype) } <> if (unlist) { if (length(result)==1) { # the no strata case if (se.fit) result[[1]][c("n", "time", "n.risk", "n.event", "n.censor", "surv", "cumhaz", "std.err")] else result[[1]][c("n", "time", "n.risk", "n.event", "n.censor", "surv", "cumhaz")] } else { <> } } else { names(result) <- ustrata result } } @ In an ordinary survival curve object with multiple strata, as produced by \code{survfitKM}, the time, survival and etc components are each a single vector that contains the results for strata 1, followed by strata 2, \ldots. The strata compontent is a vector of integers, one per strata, that gives the number of elements belonging to each stratum. The reason is that each strata will have a different number of observations, so that a matrix form was not viable, and the underlying C routines were not capable of handling lists (the code predates the .Call function by a decade). The underlying computation of \code{survfitcoxph.fit} naturally creates the list form, we unlist it to \code{survfit} form as our last action unless the caller requests otherwise. <>= temp <-list(n = unlist(lapply(result, function(x) x$n), use.names=FALSE), time= unlist(lapply(result, function(x) x$time), use.names=FALSE), n.risk= unlist(lapply(result, function(x) x$n.risk), use.names=FALSE), n.event= unlist(lapply(result, function(x) x$n.event), use.names=FALSE), n.censor=unlist(lapply(result, function(x) x$n.censor), use.names=FALSE), strata = sapply(result, function(x) length(x$time))) names(temp$strata) <- names(result) if ((missing(id2) || is.null(id2)) && nrow(x2)>1) { temp$surv <- t(matrix(unlist(lapply(result, function(x) t(x$surv)), use.names=FALSE), nrow= nrow(x2))) dimnames(temp$surv) <- list(NULL, row.names(x2)) temp$cumhaz <- t(matrix(unlist(lapply(result, function(x) t(x$cumhaz)), use.names=FALSE), nrow= nrow(x2))) if (se.fit) temp$std.err <- t(matrix(unlist(lapply(result, function(x) t(x$std.err)), use.names=FALSE), nrow= nrow(x2))) } else { temp$surv <- unlist(lapply(result, function(x) x$surv), use.names=FALSE) temp$cumhaz <- unlist(lapply(result, function(x) x$cumhaz), use.names=FALSE) if (se.fit) temp$std.err <- unlist(lapply(result, function(x) x$std.err), use.names=FALSE) } temp @ For \code{individual=FALSE} we have a second dimension, namely each of the target covariate sets (if there are multiples). Each of these generates a unique set of survival and variance(survival) values, but all of the same size since each uses all the strata. The final output structure in this case has single vectors for the time, number of events, number censored, and number at risk values since they are common to all the curves, and a matrix of survival and variance estimates, one column for each of the distinct target values. If $\Lambda_0$ is the baseline cumulative hazard from the above calculation, then $r_i \Lambda_0$ is the cumulative hazard for the $i$th new risk score $r_i$. The variance has two parts, the first of which is $r_i^2 H_1$ where $H_1$ is returned from the \code{agsurv} routine, and the second is \begin{align*} H_2(t) =& d'(t) V d(t) \\ %' d(t) = \int_0^t [z- \overline x(s)] d\Lambda(s) \end{align*} $V$ is the variance matrix for $\beta$ from the fitted Cox model, and $d(t)$ is the distance between the target covariate $z$ and the mean of the original data, summed up over the interval from 0 to $t$. Essentially the variance in $\hat \beta$ has a larger influence when prediction is far from the mean. The function below takes the basic curve from the list and multiplies it out to matrix form. <>= expand <- function(fit, x2, varmat, se.fit) { if (survtype==1) surv <- cumprod(fit$surv) else surv <- exp(-fit$cumhaz) if (is.matrix(x2) && nrow(x2) >1) { #more than 1 row in newdata fit$surv <- outer(surv, risk2, '^') dimnames(fit$surv) <- list(NULL, row.names(x2)) if (se.fit) { varh <- matrix(0., nrow=length(fit$varhaz), ncol=nrow(x2)) for (i in 1:nrow(x2)) { dt <- outer(fit$cumhaz, x2[i,], '*') - fit$xbar varh[,i] <- (cumsum(fit$varhaz) + rowSums((dt %*% varmat)* dt))* risk2[i]^2 } fit$std.err <- sqrt(varh) } fit$cumhaz <- outer(fit$cumhaz, risk2, '*') } else { fit$surv <- surv^risk2 if (se.fit) { dt <- outer(fit$cumhaz, c(x2)) - fit$xbar varh <- (cumsum(fit$varhaz) + rowSums((dt %*% varmat)* dt)) * risk2^2 fit$std.err <- sqrt(varh) } fit$cumhaz <- fit$cumhaz * risk2 } fit } @ In the lines just above: I have a matrix \code{dt} with one row per death time and one column per variable. For each row $d_i$ separately we want the quadratic form $d_i V d_i'$. The first matrix product can %' be done for all rows at once: found in the inner parenthesis. Ordinary (not matrix) multiplication followed by rowsums does the rest in one fell swoop. Now, if \code{id2} is missing we can simply apply the \code{expand} function to each strata. For the case with \code{id2} not missing, we create a single survival curve for each unique id (subject). A subject will spend blocks of time with different covariate sets, sometimes even jumping between strata. Retrieve each one and save it into a list, and then sew them together end to end. The \code{n} component is the number of observations in the strata --- but this subject might visit several. We report the first one they were in for printout. The \code{time} component will be cumulative on this subject's scale. %' Counting this is a bit trickier than I first thought. Say that the subject's first interval goes from 1 to 10, with observed time points in that interval at 2, 5, and 7, and a second interval from 12 to 20 with observed time points in the data of 15 and 18. On the subject's time scale things happen at days 1, 4, 6, 12 and 15. The deltas saved below are 2-1, 5-2, 7-5, 3+ 14-12, 17-14. Note the 3+ part, kept in the \code{timeforward} variable. Why all this ``adding up'' nuisance? If the subject spent time in two strata, the second one might be on an internal time scale of `time since entering the strata'. The two intervals in newdata could be 0--10 followed by 0--20. Time for the subject can't go backwards though: the change %` between internal/external time scales is a bit like following someone who was stepping back and forth over the international date line. In the code the \code{indx} variable points to the set of times that the subject was present, for this row of the new data. Note the $>$ on one end and $\le$ on the other. If someone's interval 1 was 0--10 and interval 2 was 10--20, and there happened to be a jump in the baseline survival curve at exactly time 10 (someone else died), that jump is counted only in the first interval. <>= if (missing(id2) || is.null(id2)) result <- lapply(survlist, expand, x2, varmat, se.fit) else { onecurve <- function(slist, x2, y2, strata2, risk2, se.fit) { ntarget <- nrow(x2) #number of different time intervals surv <- vector('list', ntarget) n.event <- n.risk <- n.censor <- varh1 <- varh2 <- time <- surv hazard <- vector('list', ntarget) stemp <- as.integer(strata2) timeforward <- 0 for (i in 1:ntarget) { slist <- survlist[[stemp[i]]] indx <- which(slist$time > y2[i,1] & slist$time <= y2[i,2]) if (length(indx)==0) { timeforward <- timeforward + y2[i,2] - y2[i,1] # No deaths or censors in user interval. Possible # user error, but not uncommon at the tail of the curve. } else { time[[i]] <- diff(c(y2[i,1], slist$time[indx])) #time increments time[[i]][1] <- time[[i]][1] + timeforward timeforward <- y2[i,2] - max(slist$time[indx]) hazard[[i]] <- slist$hazard[indx]*risk2[i] if (survtype==1) surv[[i]] <- slist$surv[indx]^risk2[i] n.event[[i]] <- slist$n.event[indx] n.risk[[i]] <- slist$n.risk[indx] n.censor[[i]]<- slist$n.censor[indx] dt <- outer(slist$cumhaz[indx], x2[i,]) - slist$xbar[indx,,drop=F] varh1[[i]] <- slist$varhaz[indx] *risk2[i]^2 varh2[[i]] <- rowSums((dt %*% varmat)* dt) * risk2[i]^2 } } cumhaz <- cumsum(unlist(hazard)) if (survtype==1) surv <- cumprod(unlist(surv)) #increments (K-M) else surv <- exp(-cumhaz) if (se.fit) list(n=as.vector(table(strata)[stemp[1]]), time=cumsum(unlist(time)), n.risk = unlist(n.risk), n.event= unlist(n.event), n.censor= unlist(n.censor), surv = surv, cumhaz= cumhaz, std.err = sqrt(cumsum(unlist(varh1)) + unlist(varh2))) else list(n=as.vector(table(strata)[stemp[1]]), time=cumsum(unlist(time)), n.risk = unlist(n.risk), n.event= unlist(n.event), n.censor= unlist(n.censor), surv = surv, cumhaz= cumhaz) } if (all(id2 ==id2[1])) { result <- list(onecurve(survlist, x2, y2, strata2, risk2, se.fit)) } else { uid <- unique(id2) result <- vector('list', length=length(uid)) for (i in 1:length(uid)) { indx <- which(id2==uid[i]) result[[i]] <- onecurve(survlist, x2[indx,,drop=FALSE], y2[indx,,drop=FALSE], strata2[indx], risk2[indx], se.fit) } names(result) <- uid } } @ Next is the code for the \code{agsurv} function, which actually does the work. The estimates of survival are the Kalbfleisch-Prentice (KP), Breslow, and Efron. Each has an increment at each unique death time. First a bit of notation: $Y_i(t)$ is 1 if bservation $i$ is ``at risk'' at time $t$ and 0 otherwise. For a simple surivival (\code{ncol(y)==2}) a subject is at risk until the time of censoring or death (first column of \code{y}). For (start, stop] data (\code{ncol(y)==3}) a subject becomes a part of the risk set at start+0 and stays through stop. $dN_i(t)$ will be 1 if subject $i$ had an event at time $t$. The risk score for each subject is $r_i = \exp(X_i \beta)$. The Breslow increment at time $t$ is $\sum w_i dN_i(t) / \sum w_i r_i Y_i(t)$, the number of events at time $t$ over the number at risk at time $t$. The final survival is \code{exp(-cumsum(increment))}. The Kalbfleish-Prentice increment is a multiplicative term $z$ which is the solution to the equation $$ \sum w_i r_i Y_i(t) = \sum dN_i(t) w_i \frac{r_i}{1- z(t)^{r_i}} $$ The left hand side is the weighted number at risk at time $t$, the right hand side is a sum over the tied events at that time. If there is only one event the equation has a closed form solution. If not, and knowing the solution must lie between 0 and 1, we do 35 steps of bisection to get a solution within 1e-8. An alternative is to use the -log of the Breslow estimate as a starting estimate, which is faster but requires a more sophisticated iteration logic. The final curve is $\prod_t z(t)^{r_c}$ where $r_c$ is the risk score for the target subject. The Efron estimate can be viewed as a modified Breslow estimate under the assumption that tied deaths are not really tied -- we just don't know the %' order. So if there are 3 subjects who die at some time $t$ we will have three psuedo-terms for $t$, $t+\epsilon$, and $t+ 2\epsilon$. All 3 subjects are present for the denominator of the first term, 2/3 of each for the second, and 1/3 for the third terms denominator. All contribute 1/3 of the weight to each numerator (1/3 chance they were the one to die there). The formulas will require $\sum w_i dN_i(t)$, $\sum w_ir_i dN_i(t)$, and $\sum w_i X_i dN_i(t)$, i.e., the sums only over the deaths. For simple survival data the risk sum $\sum w_i r_i Y_i(t)$ for all the unique death times $t$ is fast to compute as a cumulative sum, starting at the longest followup time and summing towards the shortest. There are two algorithms for (start, stop] data. \begin{itemize} \item Do a separate sum at each death time. The problem is for very large data sets. For each death time the selection \code{(start=t)} is $O(n)$ and can take more time then all the remaining calculations together. \item Use the difference of two cumulative sums, one ordered by start time and one ordered by stop time. This is $O(2n)$ for the intial sums. The problem here is potential round off error if the sums get large. This issue is mostly precluded by subtracting means first, and avoiding intervals that don't overlap an event time. \end{itemize} We compute the extended number still at risk --- all whose stop time is $\ge$ each unique death time --- in the vector \code{xin}. From this we have to subtract all those who haven't actually entered yet %' found in \code{xout}. Remember that (3,20] enters at time 3+. The total at risk at any time is the difference between them. Output is only for the stop times; a call to approx is used to reconcile the two time sets. The \code{irisk} vector is for the printout, it is a sum of weighted counts rather than weighted risk scores. <>= agsurv <- function(y, x, wt, risk, survtype, vartype) { nvar <- ncol(as.matrix(x)) status <- y[,ncol(y)] dtime <- y[,ncol(y) -1] death <- (status==1) time <- sort(unique(dtime)) nevent <- as.vector(rowsum(wt*death, dtime)) ncens <- as.vector(rowsum(wt*(!death), dtime)) wrisk <- wt*risk rcumsum <- function(x) rev(cumsum(rev(x))) # sum from last to first nrisk <- rcumsum(rowsum(wrisk, dtime)) irisk <- rcumsum(rowsum(wt, dtime)) if (ncol(y) ==2) { temp2 <- rowsum(wrisk*x, dtime) xsum <- apply(temp2, 2, rcumsum) } else { delta <- min(diff(time))/2 etime <- c(sort(unique(y[,1])), max(y[,1])+delta) #unique entry times indx <- approx(etime, 1:length(etime), time, method='constant', rule=2, f=1)$y esum <- rcumsum(rowsum(wrisk, y[,1])) #not yet entered nrisk <- nrisk - c(esum,0)[indx] irisk <- irisk - c(rcumsum(rowsum(wt, y[,1])),0)[indx] xout <- apply(rowsum(wrisk*x, y[,1]), 2, rcumsum) #not yet entered xin <- apply(rowsum(wrisk*x, dtime), 2, rcumsum) # dtime or alive xsum <- xin - (rbind(xout,0))[indx,,drop=F] } ndeath <- rowsum(status, dtime) #unweighted death count @ The KP estimate requires a short C routine to do the iteration efficiently, and the Efron estimate needs a second C routine to efficiently compute the partial sums. <>= ntime <- length(time) if (survtype ==1) { #Kalbfleisch-Prentice indx <- (which(status==1))[order(dtime[status==1])] #deaths km <- .C(Cagsurv4, as.integer(ndeath), as.double(risk[indx]), as.double(wt[indx]), as.integer(ntime), as.double(nrisk), inc = double(ntime)) } if (survtype==3 || vartype==3) { # Efron approx xsum2 <- rowsum((wrisk*death) *x, dtime) erisk <- rowsum(wrisk*death, dtime) #risk score sums at each death tsum <- .C(Cagsurv5, as.integer(length(nevent)), as.integer(nvar), as.integer(ndeath), as.double(nrisk), as.double(erisk), as.double(xsum), as.double(xsum2), sum1 = double(length(nevent)), sum2 = double(length(nevent)), xbar = matrix(0., length(nevent), nvar)) } haz <- switch(survtype, nevent/nrisk, nevent/nrisk, nevent* tsum$sum1) varhaz <- switch(vartype, nevent/(nrisk * ifelse(nevent>=nrisk, nrisk, nrisk-nevent)), nevent/nrisk^2, nevent* tsum$sum2) xbar <- switch(vartype, (xsum/nrisk)*haz, (xsum/nrisk)*haz, nevent * tsum$xbar) result <- list(n= nrow(y), time=time, n.event=nevent, n.risk=irisk, n.censor=ncens, hazard=haz, cumhaz=cumsum(haz), varhaz=varhaz, ndeath=ndeath, xbar=apply(matrix(xbar, ncol=nvar),2, cumsum)) if (survtype==1) result$surv <- km$inc result } @ The arguments to this function are the number of unique times n, which is the length of the vectors ndeath (number at each time), denom, and the returned vector km. The risk and wt vectors contain individual values for the subjects with an event. Their length will be equal to sum(ndeath). <>= #include "survS.h" #include "survproto.h" void agsurv4(Sint *ndeath, double *risk, double *wt, Sint *sn, double *denom, double *km) { int i,j,k, l; int n; /* number of unique death times */ double sumt, guess, inc; n = *sn; j =0; for (i=0; i>= #include "survS.h" void agsurv5(Sint *n2, Sint *nvar2, Sint *dd, double *x1, double *x2, double *xsum, double *xsum2, double *sum1, double *sum2, double *xbar) { double temp; int i,j, k, kk; double d; int n, nvar; n = n2[0]; nvar = nvar2[0]; for (i=0; i< n; i++) { d = dd[i]; if (d==1){ temp = 1/x1[i]; sum1[i] = temp; sum2[i] = temp*temp; for (k=0; k< nvar; k++) xbar[i+ n*k] = xsum[i + n*k] * temp*temp; } else { temp = 1/x1[i]; for (j=0; j>= survfit.coxphms <- function(formula, newdata, se.fit=TRUE, conf.int=.95, individual=FALSE, stype=2, ctype, conf.type=c("log", "log-log", "plain", "none", "logit", "arcsin"), censor=TRUE, start.time, id, influence=FALSE, na.action=na.pass, type, p0=NULL, ...) { Call <- match.call() Call[[1]] <- as.name("survfit") #nicer output for the user object <- formula #'formula' because it has to match survfit se.fit <- FALSE #still to do if (missing(newdata)) stop("multi-state survival requires a newdata argument") if (!missing(id)) stop("using a covariate path is not supported for multi-state") temp <- object$stratum_map["(Baseline)",] baselinecoef <- rbind(temp, coef= 1.0) if (any(duplicated(temp))) { # We have shared hazards # Find rows of cmap with "ph(a:b)" type labels to find out which # ones have proportionality rname <- rownames(object$cmap) phbase <- grepl("ph(", rname, fixed=TRUE) for (i in which(phbase)) { ctemp <- object$cmap[i,] index <- which(ctemp >0) baselinecoef[2, index] <- exp(object$coef[ctemp[index]]) } } else phbase <- rep(FALSE, nrow(object$cmap)) # process options, set up Y and the model frame, deal with start.time <> <> istate <- model.extract(mf, "istate") if (!missing(start.time)) { if (!is.numeric(start.time) || length(start.time) !=1 || !is.finite(start.time)) stop("start.time must be a single numeric value") toss <- which(Y[,ncol(Y)-1] <= start.time) if (length(toss)) { n <- nrow(Y) if (length(toss)==n) stop("start.time has removed all observations") Y <- Y[-toss,,drop=FALSE] X <- X[-toss,,drop=FALSE] weights <- weights[-toss] oldid <- oldid[-toss] istate <- istate[-toss] } } # expansion of the X matrix with stacker, set up shared hazards <> # risk scores, mf2, and x2 <> <> <> <> cifit$call <- Call class(cifit) <- c("survfitms", "survfit") cifit } @ The third line \code{as.name('survfit')} causes the printout to say `survfit' instead of `survfit.coxph'. %' Notice that setup is almost completely shared with survival for single state models. The major change is that we use survfitCI (non-Cox) to do all the legwork wrt the tabulation values (number at risk, etc.), while for the computation proper it is easier to make use of the same expanded data set that coxph used for a multi-state fit. <>= # Rebuild istate using the survcheck routine mcheck <- survcheck2(Y, oldid, istate) transitions <- mcheck$transitions if (is.null(istate)) istate <- mcheck$istate if (!identical(object$states, mcheck$states)) stop("failed to rebuild the data set") # Let the survfitCI routine do the work of creating the # overall counts (n.risk, etc). The rest of this code then # replaces the surv and hazard components. if (missing(start.time)) start.time <- min(Y[,2], 0) # If the data has absorbing states (ones with no transitions out), then # remove those rows first since they won't be in the final output. t2 <- transitions[, is.na(match(colnames(transitions), "(censored)")), drop=FALSE] absorb <- row.names(t2)[rowSums(t2)==0] if (is.null(weights)) weights <- rep(1.0, nrow(Y)) if (is.null(strata)) tempstrat <- rep(1L, nrow(Y)) else tempstrat <- strata if (length(absorb)) droprow <- istate %in% absorb else droprow <- FALSE # Let survfitCI fill in the n, number at risk, number of events, etc. portions # We will replace the pstate and cumhaz estimate with correct ones. if (any(droprow)) { j <- which(!droprow) cifit <- survfitCI(as.factor(tempstrat[j]), Y[j,], weights[j], id =oldid[j], istate= istate[j], se.fit=FALSE, start.time=start.time, p0=p0) } else cifit <- survfitCI(as.factor(tempstrat), Y, weights, id= oldid, istate = istate, se.fit=FALSE, start.time=start.time, p0=p0) # For computing the actual estimates it is easier to work with an # expanded data set. # Replicate actions found in the coxph-multi-X chunk, cluster <- model.extract(mf, "cluster") xstack <- stacker(object$cmap, object$stratum_map, as.integer(istate), X, Y, as.integer(strata), states= object$states) if (length(position) >0) position <- position[xstack$rindex] # id was required by coxph X <- xstack$X Y <- xstack$Y strata <- strata[xstack$rindex] # strat in the model, other than transitions transition <- xstack$transition istrat <- xstack$strata if (length(offset)) offset <- offset[xstack$rindex] if (length(weights)) weights <- weights[xstack$rindex] if (length(cluster)) cluster <- cluster[xstack$rindex] oldid <- oldid[xstack$rindex] if (robust & length(cluster)==0) cluster <- oldid @ The survfit.coxph-setup3 chunk, shared with single state Cox models, has created an mf2 model frame and an x2 matrix. For multi-state, we ignore any strata variables in mf2. Create a matrix of risk scores, number of subjects by number of transitions. Different transitions often have different coefficients, so there is a risk score vector per transition. <>= if (has.strata && !is.null(mf2[[stangle$vars]])){ mf2 <- mf2[is.na(match(names(mf2), stangle$vars))] mf2 <- unique(mf2) x2 <- unique(x2) } temp <- coef(object, matrix=TRUE)[!phbase,,drop=FALSE] # ignore missing coefs risk2 <- exp(x2 %*% ifelse(is.na(temp), 0, temp) - xcenter) @ At this point we have several parts to keep straight. The data set has been expanded into a new X and Y. \begin{itemize} \item \code{strata} contains any strata that were specified by the user in the original fit. We do completely separate computations for each stratum: the time scale starts over, nrisk, etc. Each has a separate call to the multihaz function. \item \code{transtion} contains the transition to which each observation applies \item \code{istrat} comes from the xstack routine, and marks each strata * basline hazard combination. \item \code{baselinecoef} maps from baseline hazards to transitions. It has one column per transition, which hazard it points to, and a multiplier. Most multipliers will be 1. \item \code{hfill} is constructed below. It contains the row/column to which each column of baselinecoef is mapped, within the H matrix used to compute P(state). \end{itemize} The coxph routine fits all strata and transitions at once, since the loglik is a sum over strata. This routine does each stratum separately. <>= # make the expansion map. # The H matrices we will need are nstate by nstate, at each time, with # elements that are non-zero only for observed transtions. states <- object$states nstate <- length(states) notcens <- (colnames(object$transitions) != "(censored)") trmat <- object$transitions[, notcens, drop=FALSE] from <- row(trmat)[trmat>0] from <- match(rownames(trmat), states)[from] # actual row of H to <- col(trmat)[trmat>0] to <- match(colnames(trmat), states)[to] # actual col of H hfill <- cbind(from, to) if (individual) { stop("time dependent survival curves are not supported for multistate") } ny <- ncol(Y) if (is.null(strata)) { fit <- multihaz(Y, X, position, weights, risk, istrat, ctype, stype, baselinecoef, hfill, x2, risk2, varmat, nstate, se.fit, cifit$p0, cifit$time) cifit$pstate <- fit$pstate cifit$cumhaz <- fit$cumhaz } else { if (is.factor(strata)) ustrata <- levels(strata) else ustrata <- sort(unique(strata)) nstrata <- length(cifit$strata) itemp <- rep(1:nstrata, cifit$strata) timelist <- split(cifit$time, itemp) ustrata <- names(cifit$strata) tfit <- vector("list", nstrata) for (i in 1:nstrata) { indx <- which(strata== ustrata[i]) # divides the data tfit[[i]] <- multihaz(Y[indx,,drop=F], X[indx,,drop=F], position[indx], weights[indx], risk[indx], istrat[indx], ctype, stype, baselinecoef, hfill, x2, risk2, varmat, nstate, se.fit, cifit$p0[i,], timelist[[i]]) } # do.call(rbind) doesn't work for arrays, it loses a dimension ntime <- length(cifit$time) cifit$pstate <- array(0., dim=c(ntime, dim(tfit[[1]]$pstate)[2:3])) cifit$cumhaz <- array(0., dim=c(ntime, dim(tfit[[1]]$cumhaz)[2:3])) rtemp <- split(seq(along=cifit$time), itemp) for (i in 1:nstrata) { cifit$pstate[rtemp[[i]],,] <- tfit[[i]]$pstate cifit$cumhaz[rtemp[[i]],,] <- tfit[[i]]$cumhaz } } cifit$newdata <- mf2 @ Finally, a routine that does all the actual work. \begin{itemize} \item The first 5 variables are for the data set that the Cox model was built on: y, x, position, risk score, istrat. Position is a flag for each obs. Is it the first of a connected string such as (10, 12) (12,19) (19,21), the last of such a string, both, or neither. 1*first + 2*last. This affects whether an obs is labeled as censored or not, nothing else. \item x2 and risk2 are the covariates and risk scores for the predicted values. These do not involve any ph(a:b) coefficients. \item baselinecoef and hfill control mapping from fittes hazards to transitions and probabilities \item p0 will be NULL if the user did not specifiy it. \item vmat is only needed for standard errors \item utime is the set of time points desired \end{itemize} <>= # Compute the hazard and survival functions multihaz <- function(y, x, position, weight, risk, istrat, ctype, stype, bcoef, hfill, x2, risk2, vmat, nstate, se.fit, p0, utime) { if (ncol(y) ==2) { sort1 <- seq.int(0, nrow(y)-1L) # sort order for a constant y <- cbind(-1.0, y) # add a start.time column, -1 in case # there is an event at time 0 } else sort1 <- order(istrat, y[,1]) -1L sort2 <- order(istrat, y[,2]) -1L ntime <- length(utime) # this returns all of the counts we might desire. storage.mode(weight) <- "double" #failsafe # for Surv(time, status), position is 2 (last) for all obs if (length(position)==0) position <- rep(2L, nrow(y)) fit <- .Call(Ccoxsurv2, utime, y, weight, sort1, sort2, position, istrat, x, risk) cn <- fit$count # 1-3 = at risk, 4-6 = events, 7-8 = censored events # 9-10 = censored, 11-12 = Efron, 13-15 = entry if (ctype ==1) { denom1 <- ifelse(cn[,4]==0, 1, cn[,3]) denom2 <- ifelse(cn[,4]==0, 1, cn[,3]^2) } else { denom1 <- ifelse(cn[,4]==0, 1, cn[,11]) denom2 <- ifelse(cn[,4]==0, 1, cn[,12]) } temp <- matrix(cn[,5] / denom1, ncol = fit$ntrans) hazard <- temp[,bcoef[1,]] * rep(bcoef[2,], each=nrow(temp)) if (se.fit) { temp <- matrix(cn[,5] / denom2, ncol = fit$ntrans) varhaz <- temp[,bcoef[1,]] * rep(bcoef[2,]^2, each=nrow(temp)) } # Expand the result, one "hazard set" for each row of x2 nx2 <- nrow(x2) h2 <- array(0, dim=c(nrow(hazard), nx2, ncol(hazard))) if (se.fit) v2 <- h2 S <- double(nstate) # survival at the current time S2 <- array(0, dim=c(nrow(hazard), nx2, nstate)) H <- matrix(0, nstate, nstate) if (stype==2) { H[hfill] <- colMeans(hazard) diag(H) <- diag(H) -rowSums(H) esetup <- survexpmsetup(H) } for (i in 1:nx2) { h2[,i,] <- apply(hazard %*% diag(risk2[i,]), 2, cumsum) if (se.fit) { d1 <- fit$xbar - rep(x[i,], each=nrow(fit$xbar)) d2 <- apply(d1*hazard, 2, cumsum) d3 <- rowSums((d2%*% vmat) * d2) # v2[jj,] <- (apply(varhaz[jj,],2, cumsum) + d3) * (risk2[i])^2 } S <- p0 for (j in 1:ntime) { H[,] <- 0.0 H[hfill] <- hazard[j,] *risk2[i,] if (stype==1) { diag(H) <- pmax(0, 1.0 - rowSums(H)) S <- as.vector(S %*% H) # don't keep any names } else { diag(H) <- 0.0 - rowSums(H) #S <- as.vector(S %*% expm(H)) # dgeMatrix issue S <- as.vector(S %*% survexpm(H, 1, esetup)) } S2[j,i,] <- S } } rval <- list(time=utime, xgrp=rep(1:nx2, each=nrow(hazard)), pstate=S2, cumhaz=h2) if (se.fit) rval$varhaz <- v2 rval } @ \section{The Fine-Gray model} For competing risks with ending states 1, 2, \ldots $k$, the Fine-Gray approach turns these into a set of simple 2-state Cox models: \begin{itemize} \item (not yet in state 1) $\longrightarrow$ state 1 \item (not yet in state 2) $\longrightarrow$ state 2 \item \ldots \end{itemize} Each of these is now a simple Cox model, assuming that we are willing to make a proportional hazards assumption. There is one added complication: when estimating the first model, one wants to use the data set that would have occured if the subjects being followed for state 1 had not had an artificial censoring, that is, had continued to be followed for event 1 even after event 2 occured. Sometimes this can be filled in directly, e.g., if we knew the enrollment dates for each subject along with the date that follow-up for the study was terminated, and there was no lost to follow-up (only administrative censoring.) An example is the mgus2 data set, where follow-up for death continued after the occurence of plasma cell malignancy. In practice what is done is to estimate the overall censoring distribution and give subjects artificial follow-up. The function below creates a data set that can then be used with coxph. <>= finegray <- function(formula, data, weights, subset, na.action= na.pass, etype, prefix="fg", count="", id, timefix=TRUE) { Call <- match.call() indx <- match(c("formula", "data", "weights", "subset", "id"), names(Call), nomatch=0) if (indx[1] ==0) stop("A formula argument is required") temp <- Call[c(1,indx)] # only keep the arguments we wanted temp$na.action <- na.action temp[[1L]] <- quote(stats::model.frame) # change the function called special <- c("strata", "cluster") temp$formula <- if(missing(data)) terms(formula, special) else terms(formula, special, data=data) mf <- eval(temp, parent.frame()) if (nrow(mf) ==0) stop("No (non-missing) observations") Terms <- terms(mf) Y <- model.extract(mf, "response") if (!inherits(Y, "Surv")) stop("Response must be a survival object") type <- attr(Y, "type") if (type!='mright' && type!='mcounting') stop("Fine-Gray model requires a multi-state survival") nY <- ncol(Y) states <- attr(Y, "states") if (timefix) Y <- aeqSurv(Y) strats <- attr(Terms, "specials")$strata if (length(strats)) { stemp <- untangle.specials(Terms, 'strata', 1) if (length(stemp$vars)==1) strata <- mf[[stemp$vars]] else strata <- survival::strata(mf[,stemp$vars], shortlabel=TRUE) istrat <- as.numeric(strata) mf[stemp$vars] <- NULL } else istrat <- rep(1, nrow(mf)) id <- model.extract(mf, "id") if (!is.null(id)) mf["(id)"] <- NULL # don't leave it in result user.weights <- model.weights(mf) if (is.null(user.weights)) user.weights <- rep(1.0, nrow(mf)) cluster<- attr(Terms, "specials")$cluster if (length(cluster)) { stop("a cluster() term is not valid") } # If there is start-stop data, then there needs to be an id # also check that this is indeed a competing risks form of data. # Mark the first and last obs of each subject, as we need it later. # Observations may not be in time order within a subject delay <- FALSE # is there delayed entry? if (type=="mcounting") { if (is.null(id)) stop("(start, stop] data requires a subject id") else { index <- order(id, Y[,2]) # by time within id sorty <- Y[index,] first <- which(!duplicated(id[index])) last <- c(first[-1] -1, length(id)) if (any(sorty[-last, 3] != 0)) stop("a subject has a transition before their last time point") delta <- c(sorty[-1,1], 0) - sorty[,2] if (any(delta[-last] !=0)) stop("a subject has gaps in time") if (any(Y[first,1] > min(Y[,2]))) delay <- TRUE temp1 <- temp2 <- rep(FALSE, nrow(mf)) temp1[index[first]] <- TRUE temp2[index[last]] <- TRUE first <- temp1 #used later last <- temp2 } } else last <- rep(TRUE, nrow(mf)) if (missing(etype)) enum <- 1 #generate a data set for which endpoint? else { index <- match(etype, states) if (any(is.na(index))) stop ("etype argument has a state that is not in the data") enum <- index[1] if (length(index) > 1) warning("only the first endpoint was used") } # make sure count, if present is syntactically valid if (!missing(count)) count <- make.names(count) else count <- NULL oname <- paste0(prefix, c("start", "stop", "status", "wt")) <> <> } @ The censoring and truncation distributions are \begin{align*} G(t) &= \prod_{s \le t} \left(1 - \frac{c(s)}{r_c(s)} \right ) \\ H(t) &= \prod_{s > t} \left(1 - \frac{e(s)}{r_e(s)} \right ) \end{align*} where $c(t)$ is the number of subjects censored at time $t$, $e(t)$ is the number who enter at time $t$, and $r$ is the size of the relevant risk set. These are equations 5 and 6 of Geskus (Biometrics 2011). Note that both $G$ and $H$ are right continuous functions. For tied times the assumption is that event $<$ censor $<$ entry. For $G$ we use a modified Kapan-Meier where any events at censoring time $t$ are removed from the risk set just before time $t$. To avoid issues with times that are nearly identical (but not quite) we first convert to an integer time scale, and then move events backwards by .2. Since this is a competing risks data set any non-censored observation for a subject is their last, so this time shift does not goof up the alignment of start, stop data. For the truncation distribution it is the subjects with times at or before time $t$ that are in the risk set $r_e(t)$ for truncation at (or before) $t$. $H$ can be calculated using an ordinary KM on the reverse time scale. When there is (start,stop) data and hence multiple observations per subject, calculation of $G$ needs use a status that is 1 only for the \emph{last} row row of a censored subject. <>= if (ncol(Y) ==2) { temp <- min(Y[,1], na.rm=TRUE) if (temp >0) zero <- 0 else zero <- 2*temp -1 # a value less than any observed y Y <- cbind(zero, Y) # add a start column } utime <- sort(unique(c(Y[,1:2]))) # all the unique times newtime <- matrix(findInterval(Y[,1:2], utime), ncol=2) status <- Y[,3] newtime[status !=0, 2] <- newtime[status !=0,2] - .2 Gsurv <- survfit(Surv(newtime[,1], newtime[,2], last & status==0) ~ istrat, se.fit=FALSE) @ The calculation for $H$ is also done on the integer scale. Otherwise we will someday be clobbered by times that differ only in round off error. The only nuisance is the status variable, which is 1 for the first row of each subject, since the data set may not be in sorted order. The offset of .2 used above is not needed, but due to the underlying integer scale it doesn't harm anything either. Reversal of the time scale leads to a left continuous function which we fix up later. <>= if (delay) Hsurv <- survfit(Surv(-newtime[,2], -newtime[,1], first) ~ istrat, se.fit =FALSE) @ Consider the following data set: \begin{itemize} \item Events of type 1 at times 1, 4, 5, 10 \item Events of type 2 at times 2, 5, 8 \item Censors at times 3, 4, 4, 6, 8, 9, 12 \end{itemize} The censoring distribution will have the following shape: \begin{center} \begin{tabular}{rcccccc} interval& (0,3]& (3,4] & (4,6] & (6,8] & (8,12] & 12+\\ C(t) & 1 &11/12 & (11/12)(8/10) & (11/15)(5/6)& (11/15)(5/6)(3/4)& 0 \\ & 1.0000 & .9167 & .7333 & .6111 & .4583 \end{tabular} \end{center} Notice that the event at time 4 is not counted in the risk set at time 4, so the jump is 8/10 rather than 8/11. Likewise at time 8 the risk set has 4 instead of 5: censors occur after deaths. When creating the data set for event type 1, subjects who have an event of type 2 get extended out using this censoring distribution. The event at time 2, for instance, appears as a censored observation with time dependent weights of $G(t)$. The type 2 event at time 5 has weight 1 up through time 5, then weights of $G(t)/C(5)$ for the remainder. This means a weight of 1 over (5,6], 5/6 over (6,8], (5/6)(3/4) over (9,12] and etc. Though there are 6 unique censoring intervals, in the created data set for event type 1 we only need to know case weights at times 1, 4, 5, and 10; the information from the (4,6] and (6,8] intervals will never be used. To create a minimal sized data set we can leave those intervals out. $G(t)$ only drops to zero if the largest time(s) are censored observations, so by definition no events lie in an interval with $G(t)=0$. If there is delayed entry, then the set of intervals is larger due to a merge with the jumps in Hsurv. The truncation distribution Hsurv ($H$) will become 0 at the first entry time; it is a left continuous function whereas Gsurv ($G$) is right continuous. We can slide $H$ one point to the left and merge them at the jump points. <>= status <- Y[, 3] # Do computations separately for each stratum stratfun <- function(i) { keep <- (istrat ==i) times <- sort(unique(Y[keep & status == enum, 2])) #unique event times if (length(times)==0) return(NULL) #no events in this stratum tdata <- mf[keep, -1, drop=FALSE] maxtime <- max(Y[keep, 2]) Gtemp <- Gsurv[i] if (delay) { Htemp <- Hsurv[i] dtime <- rev(-Htemp$time[Htemp$n.event > 0]) dprob <- c(rev(Htemp$surv[Htemp$n.event > 0])[-1], 1) ctime <- Gtemp$time[Gtemp$n.event > 0] cprob <- c(1, Gtemp$surv[Gtemp$n.event > 0]) temp <- sort(unique(c(dtime, ctime))) # these will all be integers index1 <- findInterval(temp, dtime) index2 <- findInterval(temp, ctime) ctime <- utime[temp] cprob <- dprob[index1] * cprob[index2+1] # G(t)H(t), eq 11 Geskus } else { ctime <- utime[Gtemp$time[Gtemp$n.event > 0]] cprob <- Gtemp$surv[Gtemp$n.event > 0] } ct2 <- c(ctime, maxtime) cp2 <- c(1.0, cprob) index <- findInterval(times, ct2, left.open=TRUE) index <- sort(unique(index)) # the intervals that were actually seen # times before the first ctime get index 0, those between 1 and 2 get 1 ckeep <- rep(FALSE, length(ct2)) ckeep[index] <- TRUE expand <- (Y[keep, 3] !=0 & Y[keep,3] != enum & last[keep]) #which rows to expand split <- .Call(Cfinegray, Y[keep,1], Y[keep,2], ct2, cp2, expand, c(TRUE, ckeep)) tdata <- tdata[split$row,,drop=FALSE] tstat <- ifelse((status[keep])[split$row]== enum, 1, 0) tdata[[oname[1]]] <- split$start tdata[[oname[2]]] <- split$end tdata[[oname[3]]] <- tstat tdata[[oname[4]]] <- split$wt * user.weights[split$row] if (!is.null(count)) tdata[[count]] <- split$add tdata } if (max(istrat) ==1) result <- stratfun(1) else { tlist <- lapply(1:max(istrat), stratfun) result <- do.call("rbind", tlist) } rownames(result) <- NULL #remove all the odd labels that R adds attr(result, "event") <- states[enum] result @ \subsection{The predict method} The \code{predict.coxph} function produces various types of predicted values from a Cox model. The arguments are \begin{description} \item [object] The result of a call to \code{coxph}. \item [newdata] Optionally, a new data set for which prediction is desired. If this is absent predictions are for the observations used fit the model. \item[type] The type of prediction \begin{itemize} \item lp = the linear predictor for each observation \item risk = the risk score $exp(lp)$ for each observation \item expected = the expected number of events \item survival = predicted survival = exp(-expected) \item terms = a matrix with one row per subject and one column for each term in the model. \end{itemize} \item[se.fit] Whether or not to return standard errors of the predictions. \item[na.action] What to do with missing values \emph{if} there is new data. \item[terms] The terms that are desired. This option is almost never used, so rarely in fact that it's hard to justify keeping it. \item[collapse] An optional vector of subject identifiers, over which to sum or `collapse' the results \item[reference] the reference context for centering the results \item[\ldots] All predict methods need to have a \ldots argument; we make no use of it however. \end{description} %\subsection{Setup} The first task of the routine is to reconsruct necessary data elements that were not saved as a part of the \code{coxph} fit. We will need the following components: \begin{itemize} \item for type=`expected' residuals we need the orignal survival y. This %'` is saved in coxph objects by default so will only need to be fetched in the highly unusual case that a user specfied \code{y=FALSE} in the orignal call. \item for any call with either newdata, standard errors, or type='terms' the original $X$ matrix, weights, strata, and offset. When checking for the existence of a saved $X$ matrix we can't %' use \code{object\$x} since that will also match the \code{xlevels} component. \item the new data matrix, if any \end{itemize} <>= predict.coxph <- function(object, newdata, type=c("lp", "risk", "expected", "terms", "survival"), se.fit=FALSE, na.action=na.pass, terms=names(object$assign), collapse, reference=c("strata", "sample", "zero"), ...) { <> <> if (type=="expected") { <> } else { <> <> } <> } @ We start of course with basic argument checking. Then retrieve the model parameters: does it have a strata statement, offset, etc. The \code{Terms2} object is a model statement without the strata or cluster terms, appropriate for recreating the matrix of covariates $X$. For type=expected the response variable needs to be kept, if not we remove it as well since the user's newdata might not contain one. %' The type= survival is treated the same as type expected. <>= if (!inherits(object, 'coxph')) stop("Primary argument much be a coxph object") Call <- match.call() type <-match.arg(type) if (type=="survival") { survival <- TRUE type <- "expected" #this is to stop lots of "or" statements } else survival <- FALSE n <- object$n Terms <- object$terms if (!missing(terms)) { if (is.numeric(terms)) { if (any(terms != floor(terms) | terms > length(object$assign) | terms <1)) stop("Invalid terms argument") } else if (any(is.na(match(terms, names(object$assign))))) stop("a name given in the terms argument not found in the model") } # I will never need the cluster argument, if present delete it. # Terms2 are terms I need for the newdata (if present), y is only # needed there if type == 'expected' if (length(attr(Terms, 'specials')$cluster)) { temp <- untangle.specials(Terms, 'cluster', 1) Terms <- object$terms[-temp$terms] } else Terms <- object$terms if (type != 'expected') Terms2 <- delete.response(Terms) else Terms2 <- Terms has.strata <- !is.null(attr(Terms, 'specials')$strata) has.offset <- !is.null(attr(Terms, 'offset')) has.weights <- any(names(object$call) == 'weights') na.action.used <- object$na.action n <- length(object$residuals) if (missing(reference) && type=="terms") reference <- "sample" else reference <- match.arg(reference) @ The next task of the routine is to reconsruct necessary data elements that were not saved as a part of the \code{coxph} fit. We will need the following components: \begin{itemize} \item for type=`expected' residuals we need the orignal survival y. This %'` is saved in coxph objects by default so will only need to be fetched in the highly unusual case that a user specfied \code{y=FALSE} in the orignal call. We also need the strata in this case. Grabbing it is the same amount of work as grabbing X, so gets lumped with that case in the code. \item for any call with either standard errors, reference strata, or type=`terms' the original $X$ matrix, weights, strata, and offset. When checking for the existence of a saved $X$ matrix we can't %' use \code{object\$x} since that will also match the \code{xlevels} component. \item the new data matrix, if present, along with offset and strata. \end{itemize} For the case that none of the above are needed, we can use the \code{linear.predictors} component of the fit. The variable \code{use.x} signals this case, which takes up almost none of the code but is common in usage. The check below that nrow(mf)==n is to avoid data sets that change under our feet. A fit was based on data set ``x'', and when we reconstruct the data frame it is a different size! This means someone changed the data between the model fit and the extraction of residuals. One other non-obvious case is that coxph treats the model \code{age:strata(grp)} as though it were \code{age:strata(grp) + strata(grp)}. The untangle.specials function will return \code{vars= strata(grp), terms=integer(0)}; the first shows a strata to extract and the second that there is nothing to remove from the terms structure. <>= have.mf <- FALSE if (type == "expected") { y <- object[['y']] if (is.null(y)) { # very rare case mf <- stats::model.frame(object) y <- model.extract(mf, 'response') have.mf <- TRUE #for the logic a few lines below, avoid double work } } # This will be needed if there are strata, and is cheap to compute strat.term <- untangle.specials(Terms, "strata") if (se.fit || type=='terms' || (!missing(newdata) && type=="expected") || (has.strata && (reference=="strata") || type=="expected")) { use.x <- TRUE if (is.null(object[['x']]) || has.weights || has.offset || (has.strata && is.null(object$strata))) { # I need the original model frame if (!have.mf) mf <- stats::model.frame(object) if (nrow(mf) != n) stop("Data is not the same size as it was in the original fit") x <- model.matrix(object, data=mf) if (has.strata) { if (!is.null(object$strata)) oldstrat <- object$strata else { if (length(strat.term$vars)==1) oldstrat <- mf[[strat.term$vars]] else oldstrat <- strata(mf[,strat.term$vars], shortlabel=TRUE) } } else oldstrat <- rep(0L, n) weights <- model.weights(mf) if (is.null(weights)) weights <- rep(1.0, n) offset <- model.offset(mf) if (is.null(offset)) offset <- 0 } else { x <- object[['x']] if (has.strata) oldstrat <- object$strata else oldstrat <- rep(0L, n) weights <- rep(1.,n) offset <- 0 } } else { # I won't need strata in this case either if (has.strata) { stemp <- untangle.specials(Terms, 'strata', 1) Terms2 <- Terms2[-stemp$terms] has.strata <- FALSE #remaining routine never needs to look } oldstrat <- rep(0L, n) offset <- 0 use.x <- FALSE } @ Now grab data from the new data set. We want to use model.frame processing, in order to correctly expand factors and such. We don't need weights, however, and don't want to make the user include them in their new dataset. Thus we build the call up the way it is done in coxph itself, but only keeping the newdata argument. Note that terms2 may have fewer variables than the original model: no cluster and if type!= expected no response. If the original model had a strata, but newdata does not, we need to remove the strata from xlev to stop a spurious warning message. <>= if (!missing(newdata)) { use.x <- TRUE #we do use an X matrix later tcall <- Call[c(1, match(c("newdata", "collapse"), names(Call), nomatch=0))] names(tcall)[2] <- 'data' #rename newdata to data tcall$formula <- Terms2 #version with no response tcall$na.action <- na.action #always present, since there is a default tcall[[1L]] <- quote(stats::model.frame) # change the function called if (!is.null(attr(Terms, "specials")$strata) && !has.strata) { temp.lev <- object$xlevels temp.lev[[strat.term$vars]] <- NULL tcall$xlev <- temp.lev } else tcall$xlev <- object$xlevels mf2 <- eval(tcall, parent.frame()) collapse <- model.extract(mf2, "collapse") n2 <- nrow(mf2) if (has.strata) { if (length(strat.term$vars)==1) newstrat <- mf2[[strat.term$vars]] else newstrat <- strata(mf2[,strat.term$vars], shortlabel=TRUE) if (any(is.na(match(newstrat, oldstrat)))) stop("New data has a strata not found in the original model") else newstrat <- factor(newstrat, levels=levels(oldstrat)) #give it all if (length(strat.term$terms)) newx <- model.matrix(Terms2[-strat.term$terms], mf2, contr=object$contrasts)[,-1,drop=FALSE] else newx <- model.matrix(Terms2, mf2, contr=object$contrasts)[,-1,drop=FALSE] } else { newx <- model.matrix(Terms2, mf2, contr=object$contrasts)[,-1,drop=FALSE] newstrat <- rep(0L, nrow(mf2)) } newoffset <- model.offset(mf2) if (is.null(newoffset)) newoffset <- 0 if (type== 'expected') { newy <- model.response(mf2) if (attr(newy, 'type') != attr(y, 'type')) stop("New data has a different survival type than the model") } na.action.used <- attr(mf2, 'na.action') } else n2 <- n @ %\subsection{Expected hazard} When we do not need standard errors the computation of expected hazard is very simple since the martingale residual is defined as status - expected. The 0/1 status is saved as the last column of $y$. <>= if (missing(newdata)) pred <- y[,ncol(y)] - object$residuals if (!missing(newdata) || se.fit) { <> } if (survival) { #it actually was type= survival, do one more step if (se.fit) se <- se * exp(-pred) pred <- exp(-pred) # probablility of being in state 0 } @ The more general case makes use of the [agsurv] routine to calculate a survival curve for each strata. The routine is defined in the section on individual Cox survival curves. The code here closely matches that. The routine only returns values at the death times, so we need approx to get a complete index. One non-obvious, but careful choice is to use the residuals for the predicted value instead of the compuation below, whenever operating on the original data set. This is a consequence of the Efron approx. When someone in a new data set has exactly the same time as one of the death times in the original data set, the code below implicitly makes them the ``last'' death in the set of tied times. The Efron approx puts a tie somewhere in the middle of the pack. This is way too hard to work out in the code below, but thankfully the original Cox model already did it. However, it does mean that a different answer will arise if you set newdata = the original coxph data set. Standard errors have the same issue, but 1. they are hardly used and 2. the original coxph doesn't do that calculation. So we do what's easiest. <>= ustrata <- unique(oldstrat) risk <- exp(object$linear.predictors) x <- x - rep(object$means, each=nrow(x)) #subtract from each column if (missing(newdata)) #se.fit must be true se <- double(n) else { pred <- se <- double(nrow(mf2)) newx <- newx - rep(object$means, each=nrow(newx)) newrisk <- c(exp(newx %*% object$coef) + newoffset) } survtype<- ifelse(object$method=='efron', 3,2) for (i in ustrata) { indx <- which(oldstrat == i) afit <- agsurv(y[indx,,drop=F], x[indx,,drop=F], weights[indx], risk[indx], survtype, survtype) afit.n <- length(afit$time) if (missing(newdata)) { # In this case we need se.fit, nothing else j1 <- approx(afit$time, 1:afit.n, y[indx,1], method='constant', f=0, yleft=0, yright=afit.n)$y chaz <- c(0, afit$cumhaz)[j1 +1] varh <- c(0, cumsum(afit$varhaz))[j1 +1] xbar <- rbind(0, afit$xbar)[j1+1,,drop=F] if (ncol(y)==2) { dt <- (chaz * x[indx,]) - xbar se[indx] <- sqrt(varh + rowSums((dt %*% object$var) *dt)) * risk[indx] } else { j2 <- approx(afit$time, 1:afit.n, y[indx,2], method='constant', f=0, yleft=0, yright=afit.n)$y chaz2 <- c(0, afit$cumhaz)[j2 +1] varh2 <- c(0, cumsum(afit$varhaz))[j2 +1] xbar2 <- rbind(0, afit$xbar)[j2+1,,drop=F] dt <- (chaz * x[indx,]) - xbar v1 <- varh + rowSums((dt %*% object$var) *dt) dt2 <- (chaz2 * x[indx,]) - xbar2 v2 <- varh2 + rowSums((dt2 %*% object$var) *dt2) se[indx] <- sqrt(v2-v1)* risk[indx] } } else { #there is new data use.x <- TRUE indx2 <- which(newstrat == i) j1 <- approx(afit$time, 1:afit.n, newy[indx2,1], method='constant', f=0, yleft=0, yright=afit.n)$y chaz <-c(0, afit$cumhaz)[j1+1] pred[indx2] <- chaz * newrisk[indx2] if (se.fit) { varh <- c(0, cumsum(afit$varhaz))[j1+1] xbar <- rbind(0, afit$xbar)[j1+1,,drop=F] } if (ncol(y)==2) { if (se.fit) { dt <- (chaz * newx[indx2,]) - xbar se[indx2] <- sqrt(varh + rowSums((dt %*% object$var) *dt)) * newrisk[indx2] } } else { j2 <- approx(afit$time, 1:afit.n, newy[indx2,2], method='constant', f=0, yleft=0, yright=afit.n)$y chaz2 <- approx(-afit$time, afit$cumhaz, -newy[indx2,2], method="constant", rule=2, f=0)$y chaz2 <-c(0, afit$cumhaz)[j2+1] pred[indx2] <- (chaz2 - chaz) * newrisk[indx2] if (se.fit) { varh2 <- c(0, cumsum(afit$varhaz))[j1+1] xbar2 <- rbind(0, afit$xbar)[j1+1,,drop=F] dt <- (chaz * newx[indx2,]) - xbar dt2 <- (chaz2 * newx[indx2,]) - xbar2 v2 <- varh2 + rowSums((dt2 %*% object$var) *dt2) v1 <- varh + rowSums((dt %*% object$var) *dt) se[indx2] <- sqrt(v2-v1)* risk[indx2] } } } } @ %\subsection{Linear predictor, risk, and terms} For these three options what is returned is a \emph{relative} prediction which compares each observation to the average for the data set. Partly this is practical. Say for instance that a treatment covariate was coded as 0=control and 1=treatment. If the model were refit using a new coding of 3=control 4=treatment, the results of the Cox model would be exactly the same with respect to coefficients, variance, tests, etc. The raw linear predictor $X\beta$ however would change, increasing by a value of $3\beta$. The relative predictor \begin{equation} \eta_i = X_i\beta - (1/n)\sum_j X_j\beta \label{eq:eta} \end{equation} will stay the same. The second reason for doing this is that the Cox model is a relative risks model rather than an absolute risks model, and thus relative predictions are almost certainly what the user was thinking of. When the fit was for a stratified Cox model more care is needed. For instance assume that we had a fit that was stratified by sex with covaritate $x$, and a second data set were created where for the females $x$ is replaced by $x+3$. The Cox model results will be unchanged for the two models, but the `normalized' linear predictors $(x - \overline x)'\beta$ %` will not be the same. This reflects a more fundamental issue that the for a stratified Cox model relative risks are well defined only \emph{within} a stratum, i.e. for subject pairs that share a common baseline hazard. The example above is artificial, but the problem arises naturally whenever the model includes a strata by covariate interaction. So for a stratified Cox model the predictions should be forced to sum to zero within each stratum, or equivalently be made relative to the weighted mean of the stratum. Unfortunately, this important issue was not realized until late in 2009 when a puzzling query was sent to the author involving the results from such an interaction. Note that this issue did not arise with type='expected', which has a natural scaling. An offset variable, if specified, is treated like any other covariate with respect to centering. The logic for this choice is not as compelling, but it seemed the best that I could do. Note that offsets play no role whatever in predicted terms, only in the lp and risk. Start with the simple ones <>= if (is.null(object$coefficients)) coef<-numeric(0) else { # Replace any NA coefs with 0, to stop NA in the linear predictor coef <- ifelse(is.na(object$coefficients), 0, object$coefficients) } if (missing(newdata)) { offset <- offset - mean(offset) if (has.strata && reference=="strata") { # We can't use as.integer(oldstrat) as an index, if oldstrat is # a factor variable with unrepresented levels as.integer could # give 1,2,5 for instance. xmeans <- rowsum(x*weights, oldstrat)/c(rowsum(weights, oldstrat)) newx <- x - xmeans[match(oldstrat,row.names(xmeans)),] } else if (use.x) { if (reference == "zero") newx <- x else newx <- x - rep(object$means, each=nrow(x)) } } else { offset <- newoffset - mean(offset) if (has.strata && reference=="strata") { xmeans <- rowsum(x*weights, oldstrat)/c(rowsum(weights, oldstrat)) newx <- newx - xmeans[match(newstrat, row.names(xmeans)),] } else if (reference!= "zero") newx <- newx - rep(object$means, each=nrow(newx)) } if (type=='lp' || type=='risk') { if (use.x) pred <- drop(newx %*% coef) + offset else pred <- object$linear.predictors if (se.fit) se <- sqrt(rowSums((newx %*% object$var) *newx)) if (type=='risk') { pred <- exp(pred) if (se.fit) se <- se * sqrt(pred) # standard Taylor series approx } } @ The type=terms residuals are a bit more work. In Splus this code used the Build.terms function, which was essentially the code from predict.lm extracted out as a separate function. As of March 2010 (today) a check of the Splus function and the R code for predict.lm revealed no important differences. A lot of the bookkeeping in both is to work around any possible NA coefficients resulting from a singularity. The basic formula is to \begin{enumerate} \item If the model has an intercept, then sweep the column means out of the X matrix. We've already done this. \item For each term separately, get the list of coefficients that belong to that term; call this list \code{tt}. \item Restrict $X$, $\beta$ and $V$ (the variance matrix) to that subset, then the linear predictor is $X\beta$ with variance matrix $X V X'$. The standard errors are the square root of the diagonal of this latter matrix. This can be computed, as colSums((X %*% V) * X)). \end{enumerate} Note that the \code{assign} component of a coxph object is the same as that found in Splus models (a list), most R models retain a numeric vector which contains the same information but it is not as easily used. The first first part of predict.lm in R rebuilds the list form as its \code{asgn} variable. I can skip this part since it is already done. <>= else if (type=='terms') { asgn <- object$assign nterms<-length(asgn) pred<-matrix(ncol=nterms,nrow=NROW(newx)) dimnames(pred) <- list(rownames(newx), names(asgn)) if (se.fit) se <- pred for (i in 1:nterms) { tt <- asgn[[i]] tt <- tt[!is.na(object$coefficients[tt])] xtt <- newx[,tt, drop=F] pred[,i] <- xtt %*% object$coefficient[tt] if (se.fit) se[,i] <- sqrt(rowSums((xtt %*% object$var[tt,tt]) *xtt)) } pred <- pred[,terms, drop=F] if (se.fit) se <- se[,terms, drop=F] attr(pred, 'constant') <- sum(object$coefficients*object$means, na.rm=T) } @ To finish up we need to first expand out any missings in the result based on the na.action, and optionally collapse the results within a subject. What should we do about the standard errors when collapse is specified? We assume that the individual pieces are independent and thus var(sum) = sum(variances). The statistical justification of this is quite solid for the linear predictor, risk and terms type of prediction due to independent increments in a martingale. For expecteds the individual terms are positively correlated so the se will be too small. One solution would be to refuse to return an se in this case, but the the bias should usually be small, and besides it would be unkind to the user. Prediction of type='terms' is expected to always return a matrix, or the R termplot() function gets unhappy. <>= if (type != 'terms') { pred <- drop(pred) if (se.fit) se <- drop(se) } if (!is.null(na.action.used)) { pred <- napredict(na.action.used, pred) if (is.matrix(pred)) n <- nrow(pred) else n <- length(pred) if(se.fit) se <- napredict(na.action.used, se) } if (!missing(collapse) && !is.null(collapse)) { if (length(collapse) != n2) stop("Collapse vector is the wrong length") pred <- rowsum(pred, collapse) # in R, rowsum is a matrix, always if (se.fit) se <- sqrt(rowsum(se^2, collapse)) if (type != 'terms') { pred <- drop(pred) if (se.fit) se <- drop(se) } } if (se.fit) list(fit=pred, se.fit=se) else pred @ \section{Concordance} \subsection{Main routine} The concordance statistic is the most used measure of goodness-of-fit in survival models. In general let $y_i$ and $x_i$ be observed and predicted data values. A pair of obervations $i$, $j$ is considered condordant if either $y_i > y_j, x_i > x_j$ or $y_i < y_j, x_i < x_j$. The concordance is the fraction of concordant pairs. For a Cox model remember that the predicted survival $\hat y$ is longer if the risk score $X\beta$ is lower, so we have to flip the definition and count ``discordant'' pairs, this is done at the end of the routine. One wrinkle is what to do with ties in either $y$ or $x$. Such pairs can be ignored in the count (treated as incomparable), treated as discordant, or given a score of 1/2. \begin{itemize} \item Kendall's $\tau$-a scores ties as 0. \item Kendall's $\tau$-b and the Goodman-Kruskal $\gamma$ ignore ties in either $y$ or $x$. \item Somers' $d$ treats ties in $y$ as incomparable, pairs that are tied in $x$ (but not $y$) score as 1/2. The AUC from logistic regression is equal to Somers' $d$. \end{itemize} All three of the above range from -1 to 1, the concordance is $(d +1)/2$. For survival data any pairs which cannot be ranked with certainty are considered incomparable. For instance $y_i$ is censored at time 10 and $y_j$ is an event (or censor) at time 20. Subject $i$ may or may not survive longer than subject $j$. Note that if $y_i$ is censored at time 10 and $y_j$ is an event at time 10 then $y_i > y_j$. Observations that are in different strata are also incomparable, since the Cox model only compares within strata. The program creates 4 variables, which are the number of concordant pairs, discordant, tied on time, and tied on $x$ but not on time. The default concordance is based on the Somers'/AUC definition, but all 4 values are reported back so that a user can recreate Kendall's or Goodmans values if desired. Here is the main routine. <>= concordance <- function(object, ...) UseMethod("concordance") concordance.formula <- function(object, data, weights, subset, na.action, cluster, ymin, ymax, timewt=c("n", "S", "S/G", "n/G", "n/G2", "I"), influence=0, ranks=FALSE, reverse=FALSE, timefix=TRUE, keepstrata=10, ...) { Call <- match.call() # save a copy of of the call, as documentation timewt <- match.arg(timewt) if (missing(ymin)) ymin <- NULL if (missing(ymax)) ymax <- NULL index <- match(c("data", "weights", "subset", "na.action", "cluster"), names(Call), nomatch=0) temp <- Call[c(1, index)] temp[[1L]] <- quote(stats::model.frame) special <- c("strata", "cluster") temp$formula <- if(missing(data)) terms(object, special) else terms(object, special, data=data) mf <- eval(temp, parent.frame()) # model frame if (nrow(mf) ==0) stop("No (non-missing) observations") Terms <- terms(mf) Y <- model.response(mf) if (inherits(Y, "Surv")) { if (timefix) Y <- aeqSurv(Y) } else { if (is.factor(Y) && (is.ordered(Y) || length(levels(Y))==2)) Y <- Surv(as.numeric(Y)) else if (is.numeric(Y) && is.vector(Y)) Y <- Surv(Y) else stop("left hand side of the formula must be a numeric vector, survival object, or an orderable factor") if (timefix) Y <- aeqSurv(Y) } n <- nrow(Y) wt <- model.weights(mf) offset<- attr(Terms, "offset") if (length(offset)>0) stop("Offset terms not allowed") stemp <- untangle.specials(Terms, "strata") if (length(stemp$vars)) { if (length(stemp$vars)==1) strat <- mf[[stemp$vars]] else strat <- strata(mf[,stemp$vars], shortlabel=TRUE) Terms <- Terms[-stemp$terms] } else strat <- NULL # if "cluster" was an argument, use it, otherwise grab it from the model group <- model.extract(mf, "cluster") cluster<- attr(Terms, "specials")$cluster if (length(cluster)) { tempc <- untangle.specials(Terms, 'cluster', 1:10) ord <- attr(Terms, 'order')[tempc$terms] if (any(ord>1)) stop ("Cluster can not be used in an interaction") cluster <- strata(mf[,tempc$vars], shortlabel=TRUE) #allow multiples Terms <- Terms[-tempc$terms] # toss it away } if (length(group)) cluster <- group x <- model.matrix(Terms, mf)[,-1, drop=FALSE] #remove the intercept if (ncol(x) > 1) stop("Only one predictor variable allowed") if (!is.null(ymin) & (length(ymin)> 1 || !is.numeric(ymin))) stop("ymin must be a single number") if (!is.null(ymax) & (length(ymax)> 1 || !is.numeric(ymax))) stop("ymax must be a single number") if (!is.logical(reverse)) stop ("the reverse argument must be TRUE/FALSE") fit <- concordancefit(Y, x, strat, wt, ymin, ymax, timewt, cluster, influence, ranks, reverse, keepstrata=keepstrata) na.action <- attr(mf, "na.action") if (length(na.action)) fit$na.action <- na.action fit$call <- Call class(fit) <- 'concordance' fit } print.concordance <- function(x, digits= max(1L, getOption("digits") - 3L), ...) { if(!is.null(cl <- x$call)) { cat("Call:\n") dput(cl) cat("\n") } omit <- x$na.action if(length(omit)) cat("n=", x$n, " (", naprint(omit), ")\n", sep = "") else cat("n=", x$n, "\n") if (length(x$concordance) > 1) { # result of a call with multiple fits tmat <- cbind(concordance= x$concordance, se=sqrt(diag(x$var))) print(round(tmat, digits=digits), ...) cat("\n") } else cat("Concordance= ", format(x$concordance, digits=digits), " se= ", format(sqrt(x$var), digits=digits), '\n', sep='') if (!is.matrix(x$count) || nrow(x$count < 11)) print(round(x$count,2)) invisible(x) } <> <> @ The concordancefit function is broken out separately, since it is called by all of the methods. It is also called directly by the the \code{coxph} routine. If $y$ is not a survival quantity, then all of the options for the \code{timewt} parameter lead to the same result. <>= concordancefit <- function(y, x, strata, weights, ymin=NULL, ymax=NULL, timewt=c("n", "S", "S/G", "n/G", "n/G2", "I"), cluster, influence=0, ranks=FALSE, reverse=FALSE, timefix=TRUE, keepstrata=10, robustse =TRUE) { # The coxph program may occassionally fail, and this will kill the C # routine further below. So check for it. if (any(is.na(x)) || any(is.na(y))) return(NULL) timewt <- match.arg(timewt) if (!robustse) {ranks <- FALSE; influence =0;} # these should only occur if something other package calls this routine if (!is.Surv(y)) { if (is.factor(y) && (is.ordered(y) || length(levels(y))==2)) y <- Surv(as.numeric(y)) else if (is.numeric(y) && is.vector(y)) y <- Surv(y) else stop("left hand side of the formula must be a numeric vector, survival object, or an orderable factor") if (timefix) y <- aeqSurv(y) } n <- length(y) if (length(x) != n) stop("x and y are not the same length") if (missing(strata) || length(strata)==0) strata <- rep(1L, n) if (length(strata) != n) stop("y and strata are not the same length") if (missing(weights) || length(weights)==0) weights <- rep(1.0, n) else if (length(weights) != n) stop("y and weights are not the same length") type <- attr(y, "type") if (type %in% c("left", "interval")) stop("left or interval censored data is not supported") if (type %in% c("mright", "mcounting")) stop("multiple state survival is not supported") nstrat <- length(unique(strata)) if (!is.logical(keepstrata)) { if (!is.numeric(keepstrata)) stop("keepstrat argument must be logical or numeric") else keepstrata <- (nstrat <= keepstrata) } if (timewt %in% c("n", "I") && nstrat > 10 && !keepstrata) { # Special trickery for matched case-control data, where the # number of strata is huge, n per strata is small, and compute # time becomes excessive. Make the data all one strata, but over # disjoint time intervals stemp <- as.numeric(as.factor(strata)) -1 if (ncol(y) ==3) { delta <- 2+ max(y[,2]) - min(y[,1]) y[,1] <- y[,1] + stemp*delta y[,2] <- y[,2] + stemp*delta } else { delta <- max(y[,1]) +2 m1 <- rep(-1L, nrow(y)) y <- Surv(m1 + stemp*delta, y[,1] + stemp*delta, y[,2]) } strata <- rep(1L, n) nstrat <- 1 } # This routine is called once per stratum docount <- function(y, risk, wts, timeopt= 'n', timefix) { n <- length(risk) # this next line is mostly invoked in stratified logistic, where # only 1 event per stratum occurs. All time weightings are the same # don't waste time even if the user asked for something different if (sum(y[,ncol(y)]) <2) timeopt <- 'n' sfit <- survfit(y~1, weights=wts, se.fit=FALSE, timefix=timefix) etime <- sfit$time[sfit$n.event > 0] esurv <- sfit$surv[sfit$n.event > 0] if (length(etime)==0) { # the special case of a stratum with no events (it happens) # No need to do any more work return(list(count= rep(0.0, 6), influence=matrix(0.0, n, 5), resid=NULL)) } if (timeopt %in% c("S/G", "n/G", "n/G2")) { temp <- y temp[,ncol(temp)] <- 1- temp[,ncol(temp)] # switch event/censor gfit <- survfit(temp~1, weights=wts, se.fit=FALSE, timefix=timefix) # G has the exact same time values as S gsurv <- c(1, gfit$surv) # We want G(t-) gsurv <- gsurv[which(sfit$n.event > 0)] } npair <- (sfit$n.risk- sfit$n.event)[sfit$n.event>0] temp <- ifelse(esurv==0, 0, esurv/npair) # avoid 0/0 timewt <- switch(timeopt, "S" = sum(wts)*temp, "S/G" = sum(wts)* temp/ gsurv, "n" = rep(1.0, length(npair)), "n/G" = 1/gsurv, "n/G2"= 1/gsurv^2, "I" = rep(1.0, length(esurv)) ) if (!is.null(ymin)) timewt[etime < ymin] <- 0 if (!is.null(ymax)) timewt[etime > ymax] <- 0 timewt <- ifelse(is.finite(timewt), timewt, 0) # 0 at risk case # order the data: reverse time, censors before deaths if (ncol(y)==2) { sort.stop <- order(-y[,1], y[,2], risk) -1L } else { sort.stop <- order(-y[,2], y[,3], risk) -1L #order by endpoint sort.start <- order(-y[,1]) -1L } # match each prediction score to the unique set of scores # (to deal with ties) utemp <- match(risk, sort(unique(risk))) bindex <- btree(max(utemp))[utemp] storage.mode(y) <- "double" # just in case y is integer storage.mode(wts) <- "double" if (robustse) { if (ncol(y) ==2) fit <- .Call(Cconcordance3, y, bindex, wts, rev(timewt), sort.stop, ranks) else fit <- .Call(Cconcordance4, y, bindex, wts, rev(timewt), sort.start, sort.stop, ranks) # The C routine gives back an influence matrix which has columns for # concordant, discordant, tied on x but not y, tied on y, and tied # on both x and y. dimnames(fit$influence) <- list(NULL, c("concordant", "discordant", "tied.x", "tied.y", "tied.xy")) if (ranks) { if (ncol(y)==2) dtime <- y[y[,2]==1, 1] else dtime <- y[y[,3]==1, 2] temp <- data.frame(time= sort(dtime), fit$resid) names(temp) <- c("time", "rank", "timewt", "casewt", "variance") fit$resid <- temp[temp[,3] > 0,] # don't return zeros } } else { if (ncol(y) ==2) fit <- .Call(Cconcordance5, y, bindex, wts, rev(timewt), sort.stop) else fit <- .Call(Cconcordance6, y, bindex, wts, rev(timewt), sort.start, sort.stop) } fit } if (nstrat < 2) { fit <- docount(y, x, weights, timewt, timefix=timefix) count2 <- fit$count[1:5] vcox <- fit$count[6] fit$count <- fit$count[1:5] if (robustse) imat <- fit$influence if (ranks) resid <- fit$resid } else { strata <- as.factor(strata) ustrat <- levels(strata)[table(strata) >0] #some strata may have 0 obs tfit <- lapply(ustrat, function(i) { keep <- which(strata== i) docount(y[keep,,drop=F], x[keep], weights[keep], timewt, timefix=timefix) }) temp <- t(sapply(tfit, function(x) x$count)) fit <- list(count = temp[,1:5]) count2 <- colSums(fit$count) if (!keepstrata) fit$count <- count2 vcox <- sum(temp[,6]) if (robustse) { imat <- do.call("rbind", lapply(tfit, function(x) x$influence)) # put it back into data order index <- match(1:n, (1:n)[order(strata)]) imat <- imat[index,] if (ranks) { nr <- lapply(tfit, function(x) nrow(x$resid)) resid <- do.call("rbind", lapply(tfit, function(x) x$resid)) resid$strata <- rep(ustrat, nr) } } } npair <- sum(count2[1:3]) if (!keepstrata && is.matrix(fit$count)) fit$count <- colSums(fit$count) somer <- (count2[1] - count2[2])/npair if (robustse) { dfbeta <- weights*((imat[,1]- imat[,2])/npair - (somer/npair)* rowSums(imat[,1:3])) if (!missing(cluster) && length(cluster)>0) { dfbeta <- tapply(dfbeta, cluster, sum) dfbeta <- ifelse(is.na(dfbeta),0, dfbeta) # if cluster is a factor } var.somer <- sum(dfbeta^2) rval <- list(concordance = (somer+1)/2, count=fit$count, n=n, var = var.somer/4, cvar=vcox/(4*npair^2)) } else rval <- list(concordance = (somer+1)/2, count=fit$count, n=n, cvar=vcox/(4*npair^2)) if (is.matrix(rval$count)) colnames(rval$count) <- c("concordant", "discordant", "tied.x", "tied.y", "tied.xy") else names(rval$count) <- c("concordant", "discordant", "tied.x", "tied.y", "tied.xy") if (influence == 1 || influence==3) rval$dfbeta <- dfbeta/2 if (influence >=2) rval$influence <- imat if (ranks) rval$ranks <- resid if (reverse) { # flip concordant/discordant values but not the labels rval$concordance <- 1- rval$concordance if (!is.null(rval$dfbeta)) rval$dfbeta <- -rval$dfbeta if (!is.null(rval$influence)) { rval$influence <- rval$influence[,c(2,1,3,4,5)] colnames(rval$influence) <- colnames(rval$influence)[c(2,1,3,4,5)] } if (is.matrix(rval$count)) { rval$count <- rval$count[, c(2,1,3,4,5)] colnames(rval$count) <- colnames(rval$count)[c(2,1,3,4,5)] } else { rval$count <- rval$count[c(2,1,3,4,5)] names(rval$count) <- names(rval$count)[c(2,1,3,4,5)] } if (ranks) rval$ranks$rank <- -rval$ranks$rank } rval } @ \subsection{Methods} Methods are defined for lm, survfit, and coxph objects. Detection of strata, weights, or clustering is the main nuisance, since those are not passed back as part of coxph or survreg objects. Glm and lm objects have the model frame by default, but that can be turned off by a user. This routine gets the X, Y, and other portions from the result of a particular fit object. <>= cord.getdata <- function(object, newdata=NULL, cluster=NULL, need.wt, timefix=TRUE) { # For coxph object, don't reconstruct the model frame unless we must. # This will occur if weights, strata, or cluster are needed, or if # there is a newdata argument. Of course, if the model frame is # already present, then use it! Terms <- terms(object) specials <- attr(Terms, "specials") if (!is.null(specials$tt)) stop("cannot yet handle models with tt terms") if (!is.null(newdata)) { mf <- model.frame(object, data=newdata) y <- model.response(mf) if (!is.Surv(y)) { if (is.numeric(y) && is.vector(y)) y <- Surv(y) else stop("left hand side of the formula must be a numeric vector or a survival object") } if (timefix) y <- aeqSurv(y) rval <- list(y= y, x= predict(object, newdata)) # the type of prediction does not matter, as long as it is a # monotone transform of the linear predictor } else { mf <- object$model y <- object$y if (is.null(y)) { if (is.null(mf)) mf <- model.frame(object) y <- model.response(mf) } if (!is.Surv(y)) { y <- Surv(y) if (timefix) y <- aeqSurv(y) } # survival models will have already called timefix x <- object$linear.predictors # used by most if (is.null(x)) x <- object$fitted.values # used by lm if (is.null(x)) {object$na.action <- NULL; x <- predict(object)} rval <- list(y = y, x= x) } if (need.wt) { if (is.null(mf)) mf <- model.frame(object) rval$weights <- model.weights(mf) } if (!is.null(specials$strata)) { if (is.null(mf)) mf <- model.frame(object) stemp <- untangle.specials(Terms, 'strata', 1) if (length(stemp$vars)==1) rval$strata <- mf[[stemp$vars]] else rval$strata <- strata(mf[,stemp$vars], shortlabel=TRUE) } if (is.null(cluster)) { if (!is.null(specials$cluster)) { if (is.null(mf)) mf <- model.frame(object) tempc <- untangle.specials(Terms, 'cluster', 1:10) ord <- attr(Terms, 'order')[tempc$terms] rval$cluster <- strata(mf[,tempc$vars], shortlabel=TRUE) } else if (!is.null(object$call$cluster)) { if (is.null(mf)) mf <- model.frame(object) rval$cluster <- model.extract(mf, "cluster") } } else rval$cluster <- cluster rval } @ The methods themselves, which are near clones of each other. There is one portion of these that is not very clear. I use the trick from nearly all calls to model.frame to deal with arguments that might be there or might not, such as newdata. Construct a call by hand by first subsetting this call as Call[...], then replace the first element with the name of what I really want to call -- quote(cord.work) --, add any other args I want, and finally execute it with eval(). The problem is that this doesn't work; the routine can't find cord.work since it is not an exported function. A simple call to cord.work is okay, since function calls inherit from the survival namespace, but cfun isn't a function call, it is an expression. There are 3 possible solutions \begin{itemize} \item bad: change eval(cfun, parent.frame()) to eval(cfun, evironment(coxph)), or any other function from the survival library which has namespace::survival as its environment. If the user calls concordance with ymax=zed, say, we might not be able to find 'zed'. Especially if they had called concordance from within a function. We need the call chain. \item okay: use cfun[[1]] <- cord.work, which makes a copy of the entire cord.work function and stuffs it in. The function isn't too long, so this is okay. If cord.work fails, the label on its error message won't be as nice since it won't have ``cord.work'' in it. \item speculative: make a function and invoke it. This creates a new function in the survival namespace, but evaluates it in the current context. Using parent.frame() is important so that I don't accidentally pick up 'nfit' say, if the user had used a variable of that name as one of their arguments. \\ temp <- function(){} \\ body(temp, environment(coxph)) <- cfun\\ rval <- eval(temp(), parent.frame()) \end{itemize} <>= concordance.lm <- function(object, ..., newdata, cluster, ymin, ymax, influence=0, ranks=FALSE, timefix=TRUE, keepstrata=10) { Call <- match.call() fits <- list(object, ...) nfit <- length(fits) fname <- as.character(Call) # like deparse(substitute()) but works for ... fname <- fname[1 + 1:nfit] notok <- sapply(fits, function(x) !inherits(x, "lm")) if (any(notok)) { # a common error is to mistype an arg, "ramk=TRUE" for instance, # and it ends up in the ... list # try for a nice message in this case: the name of the arg if it # has one other than "object", fname otherwise indx <- which(notok) id2 <- names(Call)[indx+1] temp <- ifelse(id2 %in% c("","object"), fname, id2) stop(temp, " argument is not an appropriate fit object") } cargs <- c("ymin", "ymax","influence", "ranks", "keepstrata") cfun <- Call[c(1, match(cargs, names(Call), nomatch=0))] cfun[[1]] <- cord.work # or quote(survival:::cord.work) cfun$fname <- fname if (missing(newdata)) newdata <- NULL if (missing(cluster)) cluster <- NULL need.wt <- any(sapply(fits, function(x) !is.null(x$call$weights))) cfun$data <- lapply(fits, cord.getdata, newdata=newdata, cluster=cluster, need.wt=need.wt, timefix=timefix) rval <- eval(cfun, parent.frame()) rval$call <- Call rval } concordance.survreg <- function(object, ..., newdata, cluster, ymin, ymax, timewt=c("n", "S", "S/G", "n/G", "n/G2", "I"), influence=0, ranks=FALSE, timefix=FALSE, keepstrata=10) { Call <- match.call() fits <- list(object, ...) nfit <- length(fits) fname <- as.character(Call) # like deparse(substitute()) but works for ... fname <- fname[1 + 1:nfit] notok <- sapply(fits, function(x) !inherits(x, "survreg")) if (any(notok)) { # a common error is to mistype an arg, "ramk=TRUE" for instance, # and it ends up in the ... list # try for a nice message in this case: the name of the arg if it # has one other than "object", fname otherwise indx <- which(notok) id2 <- names(Call)[indx+1] temp <- ifelse(id2 %in% c("","object"), fname, id2) stop(temp, " argument is not an appropriate fit object") } cargs <- c("ymin", "ymax","influence", "ranks", "timewt", "keepstrata") cfun <- Call[c(1, match(cargs, names(Call), nomatch=0))] cfun[[1]] <- cord.work cfun$fname <- fname if (missing(newdata)) newdata <- NULL if (missing(cluster)) cluster <- NULL need.wt <- any(sapply(fits, function(x) !is.null(x$call$weights))) cfun$data <- lapply(fits, cord.getdata, newdata=newdata, cluster=cluster, need.wt=need.wt, timefix=timefix) rval <- eval(cfun, parent.frame()) rval$call <- Call rval } concordance.coxph <- function(object, ..., newdata, cluster, ymin, ymax, timewt=c("n", "S", "S/G", "n/G", "n/G2", "I"), influence=0, ranks=FALSE, timefix=FALSE, keepstrata=10) { Call <- match.call() fits <- list(object, ...) nfit <- length(fits) fname <- as.character(Call) # like deparse(substitute()) but works for ... fname <- fname[1 + 1:nfit] notok <- sapply(fits, function(x) !inherits(x, "coxph")) if (any(notok)) { # a common error is to mistype an arg, "ramk=TRUE" for instance, # and it ends up in the ... list # try for a nice message in this case: the name of the arg if it # has one other than "object", fname otherwise indx <- which(notok) id2 <- names(Call)[indx+1] temp <- ifelse(id2 %in% c("","object"), fname, id2) stop(temp, " argument is not an appropriate fit object") } # the cargs trick is a nice one, but it only copies over arguments that # are present. If 'ranks' was not specified, the default of FALSE is # not set. We keep it in the arg list only to match the documentation. cargs <- c("ymin", "ymax","influence", "ranks", "timewt", "keepstrata") cfun <- Call[c(1, match(cargs, names(Call), nomatch=0))] cfun[[1]] <- cord.work # a copy of the function cfun$fname <- fname cfun$reverse <- TRUE if (missing(newdata)) newdata <- NULL if (missing(cluster)) cluster <- NULL need.wt <- any(sapply(fits, function(x) !is.null(x$call$weights))) cfun$data <- lapply(fits, cord.getdata, newdata=newdata, cluster=cluster, need.wt=need.wt, timefix=timefix) rval <- eval(cfun, parent.frame()) rval$call <- Call rval } @ The next routine does all of the actual work for a set of models. Note that because of the call-through trick (fargs) exactly and only those arguments that are passed in are passed through to concordancefit. Default argument values for that function are found there. The default value for inflence found below is used in this routine, so it is important that they match. <>= cord.work <- function(data, timewt, ymin, ymax, influence=0, ranks=FALSE, reverse, fname, keepstrata) { Call <- match.call() fargs <- c("timewt", "ymin", "ymax", "influence", "ranks", "reverse", "keepstrata") fcall <- Call[c(1, match(fargs, names(Call), nomatch=0))] fcall[[1L]] <- concordancefit nfit <- length(data) if (nfit==1) { dd <- data[[1]] fcall$y <- dd$y fcall$x <- dd$x fcall$strata <- dd$strata fcall$weights <- dd$weights fcall$cluster <- dd$cluster rval <- eval(fcall, parent.frame()) } else { # Check that all of the models used the same data set, in the same # order, to the best of our abilities n <- length(data[[1]]$x) for (i in 2:nfit) { if (length(data[[i]]$x) != n) stop("all models must have the same sample size") if (!identical(data[[1]]$y, data[[i]]$y)) warning("models do not have the same response vector") if (!identical(data[[1]]$weights, data[[i]]$weights)) stop("all models must have the same weight vector") } if (influence==2) fcall$influence <-3 else fcall$influence <- 1 flist <- lapply(data, function(d) { temp <- fcall temp$y <- d$y temp$x <- d$x temp$strata <- d$strata temp$weights <- d$weights temp$cluster <- d$cluster eval(temp, parent.frame()) }) for (i in 2:nfit) { if (length(flist[[1]]$dfbeta) != length(flist[[i]]$dfbeta)) stop("models must have identical clustering") } count = do.call(rbind, lapply(flist, function(x) { if (is.matrix(x$count)) colSums(x$count) else x$count})) concordance <- sapply(flist, function(x) x$concordance) dfbeta <- sapply(flist, function(x) x$dfbeta) names(concordance) <- fname rownames(count) <- fname wt <- data[[1]]$weights if (is.null(wt)) vmat <- crossprod(dfbeta) else vmat <- t(wt * dfbeta) %*% dfbeta rval <- list(concordance=concordance, count=count, n=flist[[1]]$n, var=vmat, cvar= sapply(flist, function(x) x$cvar)) if (influence==1) rval$dfbeta <- dfbeta else if (influence ==2) { temp <- unlist(lapply(flist, function(x) x$influence)) rval$influence <- array(temp, dim=c(dim(flist[[1]]$influence), nfit)) } if (ranks) { temp <- lapply(flist, function(x) x$ranks) rdat <- data.frame(fit= rep(fname, sapply(temp, nrow)), do.call(rbind, temp)) row.names(rdat) <- NULL rval$ranks <- rdat } } class(rval) <- "concordance" rval } @ Last, a few miscellaneous methods <>= coef.concordance <- function(object, ...) object$concordance vcov.concordance <- function(object, ...) object$var @ The C routine returns an influence matrix with one row per subject $i$, and columns giving the partial with respect to $w_i$ for the number of concordant, discordant, tied on $x$ and ties on $y$ pairs. Somers' $d$ is $(C-D)/m$ where $m= C + D + T$ is the total number of %' comparable pairs, which does not count the tied-on-y column. For any given subject or cluster $k$ (for grouped jackknife) the IJ estimate of the variance is \begin{align*} V &\ \sum_k \left(\frac{\partial d}{\partial w_k}\right)^2 \\ \frac{\partial d}{\partial w_k} &= \frac{1}{m} \left[\frac{\partial{C-D}}{\partial w_k} - d \frac{\partial C+D+T}{\partial w_k} \right] \\ \end{align*} The C code looks a lot like a Cox model: walk forward through time, keep track of the risk sets, and add something to the totals at each death. What needs to be summed is the rank of the event subject's $x$ value, as compared to the value for all others at risk at this time point. For notational simplicity let $Y_j(t_i)$ be an indicator that subject $j$ is at risk at event time $t_i$, and $Y^*_j(t_i)$ the more restrictive one that subject $j$ is both at risk and not a tied event time. The values we want at time $t_i$ are \begin{align} C_i &= v_i \delta_i w_i \sum_j w_j Y^*_j(t_i) \left[I(x_i < x_j) \right] \label{C} \\ D_i &= v_i \delta_i w_i \sum_j w_j Y^*_j(t_i) \left[I(x_i > x_j)\right] \label{D} \\ T_i &= v_i \delta_i w_i \sum_j w_j Y^*_j(t_i) \left[I(x_i = x_j) \right] \label{T} \\ \end{align} In the above $v$ is an optional time weight, which we will discuss later. The normal concordance definition has $v=1$. $C$, $D$, and $T$ are the number of concordant, discordant, and tied pairs, respectively, and $m= C+D+T$ will be the total number of concordant pairs. Somers' $d$ is $(C-D)/m$ and the concordance is $(d+1)/2 = (C + T/2)/m$. The primary compuational question is how to do this efficiently, i.e., better than a naive algorithm that loops across all $n(n-1)/2$ possible pairs. There are two key ideas. \begin{enumerate} \item Rearrange the counting so that we do it by death times. For each death we count the number of other subjects in the risk set whose score is higher, lower, or tied and add it into the totals. This neatly solves the question of time-dependent covariates. \item Counting the number with higher, lower, and tied $x$ can be done in $O(\log_2 n)$ time if the $x$ data is kept in a binary tree. \end{enumerate} \begin{figure} \myfig{balance} \caption{A balanced tree of 13 nodes.} \label{treefig} \end{figure} Figure \ref{treefig} shows a balanced binary tree containing 13 risk scores. For each node the left child and all its descendants have a smaller value than the parent, the right child and all its descendents have a larger value. Each node in figure \ref{treefig} is also annotated with the total weight of observations in that node and the weight for itself plus all its children (not shown on graph). Assume that the tree shown represents all of the subjects still alive at the time a particular subject ``Smith'' expires, and that Smith has the risk score of 19 in the tree. The concordant pairs are those with a risk score $>19$, i.e., both $\hat y=x$ and $y$ are larger, discordant are $<19$, and we have no ties. The totals can be found by \begin{enumerate} \item Initialize the counts for discordant, concordant and tied to the values from the left children, right children, and ties at this node, respectively, which will be $(C,D,T) = (1,1,0)$. \item Walk up the tree, and at each step add the (parent + left child) or (parent + right child) to either D or C, depending on what part of the tree has not yet been totaled. At the next node (8) $D= D+4$, and at the top node $C=C + 6$. \end{enumerate} There are 5 concordant and 7 discordant pairs. This takes a little less than $\log_2(n)$ steps on average, as compared to an average of $n/2$ for the naive method. The difference can matter when $n$ is large since this traversal must be done for each event. The classic way to store trees is as a linked list. There are several algorithms for adding and subtracting nodes from a tree while maintaining the balance (red-black trees, AA trees, etc) but we take a different approach. Since we need to deal with case weights in the model and we know all the risk score at the outset, the full set of risk scores is organised into a tree at the beginning, updating the sums of weights at each node as observations are added or removed from the risk set. If we internally index the nodes of the tree as 1 for the top, 2--3 for the next horizontal row, 4--7 for the next, \ldots then the parent-child traversal becomes particularly easy. The parent of node $i$ is $i/2$ (integer arithmetic) and the children of node $i$ are $2i$ and $2i +1$. In C code the indices start at 0 of course. The following bit of code arranges data into such a tree. <>= btree <- function(n) { tfun <- function(n, id, power) { if (n==1L) id else if (n==2L) c(2L *id + 1L, id) else if (n==3L) c(2L*id + 1L, id, 2L*id +2L) else { nleft <- if (n== power*2L) power else min(power-1L, n-power%/%2L) c(tfun(nleft, 2L *id + 1L, power%/%2), id, tfun(n-(nleft+1L), 2L*id +2L, power%/%2)) } } tfun(as.integer(n), 0L, as.integer(2^(floor(logb(n-1,2))))) } @ Referring again to figure \ref{treefig}, \code{btree(13)} yields the vector \code{7 3 8 1 9 4 10 0 11 5 12 2 6} meaning that the smallest element will be in position 8 of the tree, the next smallest in position 4, etc, and using indexing that starts at 0 since the results will be passed to a C routine. The code just above takes care to do all arithmetic as integer. This actually made almost no difference in the compute time, but it was an interesting exercise to find that out. The next question is how to compute a variance for the result. One approach is to compute an infinitesimal jackknife (IJ) estimate, for which we need derivatives with respect to the weights. Looking back at equation \eqref{C} we have \begin{align} C &= \sum_i w_i \delta_i \sum_j Y^*_j(t_i) w_j I(x_i < x_j) \nonumber\\ % \frac{\partial C}{\partial w_k} &= % (v_k/m_k)\delta_k \sum_j Y^*_{j}(t_k) I(x_k < x_j) + % \sum_i (v_i/m_i) w_i Y^*_k(t_i) I(x_i < x_k) \label{partialC} \end{align} A given subject's weight appears multiple times, once when they are an event ($w_i \delta_i)$, and then as part of the risk set for other's events. I avoided this for some time because it looked like an $O(nd)$ process to separately update each subject's influence for each risk set they inhabit, but David Watson pointed out a path forward. The solution is to keep two trees. Tree 1 contains all of the subjects at risk. We traverse it when each subject is added in, updating the tree, and traverse it again at each death, pulling off values to update our sums. The second tree holds only the deaths and is updated at each death; it is read out twice per subject, once just after they enter the risk set and once when they leave. The basic algorithm is to move through an outer and inner loop. The outer loop moves across unique times, the inner for all obs that share a death time. We progress from largest to smallest time. Dealing with tied deaths is a bit subtle. \begin{itemize} \item All of the tied deaths need to be added to the event tree before subtracting the tree values from the ``initial'' influence matrix, since none of the tied subjects are in the comparison set for each other. \item Changes to the overall concordance/discordance counts need to be done for all the ties before adding them into the main tree, for the same reason. \item The Cox model variance just below has to be added up sequentially, one terms after each addition to the main tree. \end{itemize} Thus the inner loop must be repeated at least twice. A second variance computation treats the data as a Cox model. Create zero-centered scores for all subjects in the risk set: \begin{align} z_i(t) &= \sum_{j \in R(t)} w_j \sign(x_i - x_j) \nonumber \\ D-C &= \sum_i \delta_i z_i(t_i) \label{zcord} \end{align} At any event time $\sum w_i z_i =0$. Equation \eqref{zcord} is the score equation for a Cox model with time-dependent covariate $z$. When two subjects have an event at the same time, this formulation treats each of them as being in the other's risk set whereas the concordance treats them as incomparable --- how can they be the same? The trick is that $D-C$ does not change: the tied pairs add equally to $D$ and $C$. Under the null hypothesis that the risk score is not related to outcome, each term in \eqref{zcord} is a random selection from the $z$ scores in the risk set, and the variance of the addition is the variance of $z$, the sum of these over deaths is the Cox model information matrix, which is also the variance of the score statistic. The mean of $z$ is always zero, so we need to keep track of $\sum w_i z^2$. How can we do this efficiently? First note that $z_i$ can be written as sum(weights for smaller x) - sum(weights for larger x), and in fact the weighted mean for any slice of $x$, $a < x < b$, is exactly the same: mean = sum(weights for x values below the range) - sum(weights above the range). The second trick is to use an ANOVA decomposition of the variance of $z$ into within-slice and between-slice sums of squares, where the 3 slices are the $z$ scores at a given $x$ value (node of the tree), weights for score below that cutpoint, and above. Assume that a new observation $k$ has just been added to the tree. This will add $w_k$ to all the $z$ values above, and to the weighted mean of all those above, $-w_k$ to the values and means below, and 0 to the values and means of any tied observations. Thus none of the current `within' SS change. Let $s_a$, $s_b$ and $s_0$ be the current sum of weights above, below, and at the node of the tree. The mean for the above group was $(s_b + s_0)$ with between SS contribution of $s_a (s_b + s_0)^2$. The below mean was $-(s_a + s_0)$ with between SS contribution of $s_b(s_a + s_0)^2$. The change to the between SS from adding the new subject is $$ s_a\left( (s_b+s_0 + w_k)^2 - (s_b + s_0)^2 \right) = s_a (2w_k (s_b + s_0) + w_k^2) $$ while the change in between SS for the below group is $s_b(2w_k(s_a + s_0) + w_k^2)$, and there is no change for the prior observations in the middle group. Last we add $w_kz_k^2 = w_k(s_b- s_a)^2$ to the sum for the new observation. Putting all this together the change is $$ w_k \left(s_a (w_k + (s_b + s_c)) + s_b(w_k + (s_a + s_c)) + (s_a-s_b)^2 \right) $$ We can now define the C-routine that does the bulk of the work. First we give the outline shell of the code and then discuss the parts one by one. This routine is for ordinary survival data, and will be called once per stratum. Input variables are \begin{description} \item[n] the number of observations \item[y] matrix containing the time and status, data is sorted by descending time, with censorings precedint deaths. \item[x] the tree node at which this observation's risk score resides %' \item[wt] case weight for the observation \end{description} The routine will return list with three components: \begin{itemize} \item count, a vector containing the weighted number of concordant, discordant, tied on $x$ but not $y$, and tied on y pairs. The weight for a pair is $w_iw_j$. \item resid, a three column matrix with one row per event, containing the score residual at that event, its variance, and the sum of weights. The score residual is a rescaled $z_i$ so as to lie between 0 and 1: $(1+ z/\sum(w))/2$. The concordance is then a weighted sum of the residuals. \item influence, a matrix with one row per observation and 4 columns, giving that observation's first derivative with respect to the count vector. \end{itemize} <>= #include "survS.h" #include "survproto.h" <> SEXP concordance3(SEXP y, SEXP x2, SEXP wt2, SEXP timewt2, SEXP sortstop, SEXP doresid2) { int i, j, k, ii, jj, kk, j2; int n, ntree, nevent; double *time, *status; int xsave; /* sum of weights for a node (nwt), sum of weights for the node and ** all of its children (twt), then the same again for the subset of ** deaths */ double *nwt, *twt, *dnwt, *dtwt; double z2; /* sum of z^2 values */ int ndeath; /* total number of deaths at this point */ int utime; /* number of unique event times seen so far */ double dwt, dwt2; /* sum of weights for deaths and deaths tied on x */ double wsum[3]; /* the sum of weights that are > current, <, or equal */ double temp, adjtimewt; /* the second accounts for npair and timewt*/ SEXP rlist, count2, imat2, resid2; double *count, *imat[5], *resid[4]; double *wt, *timewt; int *x, *sort2; int doresid; static const char *outnames1[]={"count", "influence", "resid", ""}, *outnames2[]={"count", "influence", ""}; n = nrows(y); doresid = asLogical(doresid2); x = INTEGER(x2); wt = REAL(wt2); timewt = REAL(timewt2); sort2 = INTEGER(sortstop); time = REAL(y); status = time + n; /* if there are tied predictors, the total size of the tree will be < n */ ntree =0; nevent =0; for (i=0; i= ntree) ntree = x[i] +1; nevent += status[i]; } nwt = (double *) R_alloc(4*ntree, sizeof(double)); twt = nwt + ntree; dnwt = twt + ntree; dtwt = dnwt + ntree; for (i=0; i< 4*ntree; i++) nwt[i] =0.0; if (doresid) PROTECT(rlist = mkNamed(VECSXP, outnames1)); else PROTECT(rlist = mkNamed(VECSXP, outnames2)); count2 = SET_VECTOR_ELT(rlist, 0, allocVector(REALSXP, 6)); count = REAL(count2); for (i=0; i<6; i++) count[i]=0.0; imat2 = SET_VECTOR_ELT(rlist, 1, allocMatrix(REALSXP, n, 5)); for (i=0; i<5; i++) { imat[i] = REAL(imat2) + i*n; for (j=0; j> UNPROTECT(1); return(rlist); } @ The key part of our computation is to update the vectors of weights. We don't actually pass the risk score values $r$ into the routine, %' it is enough for each observation to point to the appropriate tree node. The tree contains the weights for everyone whose survival is larger than the time currently under review, so starts with all weights equal to zero. For any pair of observations $i,j$ we need to add $w_iw_j$ to the appropriate count, $w_j$ to subject $i$'s row of the leverage matrix and $w_i$ to subject $j$'s row. We use two trees to do this efficiently, one with all the observations to date, one with the events to date. Starting at the largest time (which is sorted last), walk through the tree. \begin{itemize} \item If the current observation is a censoring time, in order: \begin{itemize} \item Subtract event tree information from the influence matrix \item Update the Cox variance \item Add them into the main tree \end{itemize} \item If the current observation is a death, care for all deaths tied at this time point. Each pass covers all the deaths. \begin{itemize} \item Pass 1: In any order \begin{itemize} \item Add up the total number of deaths \item Update the tied.y count and tied.xy count \\ tied.xy subtotals reset each time x changes \item Count concordant, discordant, tied.x counts, both total and for the observation's influence \item Add the subject to the event tree \item Compute the first 3 columns of the residuals. \end{itemize} \item Finish up the tied.xy influence, for the last unique x in this set. \item Pass 2: \begin{itemize} \item Subtract the event tree information from the influence matrix \item Add the tied.y part of the influence for each obs \item Increment the Cox variance \item Add the subject into the main tree \end{itemize} \end{itemize} \item When all the subjects have been added to the tree, then add the final death tree's data for to the influence matrix. \end{itemize} For concordant, discordant, and tied.x there are three readouts: the total tree before any additions, the death tree after the addition of the tied events, and the death tree at the very end. Increments to the Cox variance occur just before each addition to the total tree, and are saved out after each batch of events. The above discussion counts up all pairs that are not tied on the response $y$. Though not used in the concordance the routine counts up tied.y pairs as well, with a separate count for those that are tied on both $x$ and $y$. The algorithm for this part is simpler since the data is sorted by $y$. Say that there were 5 obs tied at some time point with weights of $w_1$ to $w_5$. The total count for ties involves all 5-choose-2 pairs and can be written as $$ w_1 w_2 + (w_1 + w_2)w_3 + (w_1 + w_2 + w_3)w_4 + (w_1 + w_2 + w_3 + w_4)w_5 $$ which immediately suggests a simple summation algorithm as we go through the loop. In the below \code{dwt} contains the running sum 0, $w_1$, $w_1 + w_2$, etc and we add \code{w[i]*dwt} to the total just before incrementing the sum. The influence for observation 1 is $w_2 + w_3 + w_4 + w_5$, which can be done at the end as \code{dwt - wt[i]}. The temporary accumulator \code{dwt} is reset to 0 with each new $y$ value. To compute ties on both $x$ and $y$ the data set is sorted by $x$ within $y$, and we use the same algorithm, but reset \code{dwt2} to zero whenever either $x$ or $y$ changes. <>= z2 =0; utime=0; for (i=0; i>= void walkup(double *nwt, double* twt, int index, double sums[3], int ntree) { int i, j, parent; for (i=0; i<3; i++) sums[i] = 0.0; sums[2] = nwt[index]; /* tied on x */ j = 2*index +2; /* right child */ if (j < ntree) sums[0] += twt[j]; if (j <=ntree) sums[1]+= twt[j-1]; /*left child */ while(index > 0) { /* for as long as I have a parent... */ parent = (index-1)/2; if (index%2 == 1) sums[0] += twt[parent] - twt[index]; /* left child */ else sums[1] += twt[parent] - twt[index]; /* I am a right child */ index = parent; } } void addin(double *nwt, double *twt, int index, double wt) { nwt[index] += wt; while (index >0) { twt[index] += wt; index = (index-1)/2; } twt[0] += wt; } @ The code for [start, stop) data is almost identical, the primary call simply has one more index. As in the agreg routines there are two sort indices, the first indexes the data by stop time, longest to earliest, and the second by start time. The [[y]] variable now has three columns. <>= SEXP concordance4(SEXP y, SEXP x2, SEXP wt2, SEXP timewt2, SEXP sortstart, SEXP sortstop, SEXP doresid2) { int i, j, k, ii, jj, kk, i2, j2; int n, ntree, nevent; double *time1, *time2, *status; int xsave; /* sum of weights for a node (nwt), sum of weights for the node and ** all of its children (twt), then the same again for the subset of ** deaths */ double *nwt, *twt, *dnwt, *dtwt; double z2; /* sum of z^2 values */ int ndeath; /* total number of deaths at this point */ int utime; /* number of unique event times seen so far */ double dwt; /* weighted number of deaths at this point */ double dwt2; /* tied on both x and y */ double wsum[3]; /* the sum of weights that are > current, <, or equal */ double temp, adjtimewt; /* the second accounts for npair and timewt*/ SEXP rlist, count2, imat2, resid2; double *count, *imat[5], *resid[4]; double *wt, *timewt; int *x, *sort2, *sort1; int doresid; static const char *outnames1[]={"count", "influence", "resid", ""}, *outnames2[]={"count", "influence", ""}; n = nrows(y); doresid = asLogical(doresid2); x = INTEGER(x2); wt = REAL(wt2); timewt = REAL(timewt2); sort2 = INTEGER(sortstop); sort1 = INTEGER(sortstart); time1 = REAL(y); time2 = time1 + n; status = time2 + n; /* if there are tied predictors, the total size of the tree will be < n */ ntree =0; nevent =0; for (i=0; i= ntree) ntree = x[i] +1; nevent += status[i]; } /* ** nwt and twt are the node weight and total =node + all children for the ** tree holding all subjects. dnwt and dtwt are the same for the tree ** holding all the events */ nwt = (double *) R_alloc(4*ntree, sizeof(double)); twt = nwt + ntree; dnwt = twt + ntree; dtwt = dnwt + ntree; for (i=0; i< 4*ntree; i++) nwt[i] =0.0; if (doresid) PROTECT(rlist = mkNamed(VECSXP, outnames1)); else PROTECT(rlist = mkNamed(VECSXP, outnames2)); count2 = SET_VECTOR_ELT(rlist, 0, allocVector(REALSXP, 6)); count = REAL(count2); for (i=0; i<6; i++) count[i]=0.0; imat2 = SET_VECTOR_ELT(rlist, 1, allocMatrix(REALSXP, n, 5)); for (i=0; i<5; i++) { imat[i] = REAL(imat2) + i*n; for (j=0; j> UNPROTECT(1); return(rlist); } @ As we move from the longest time to the shortest observations are added into the tree of weights whenever we encounter their stop time. This is just as before. Weights now also need to be removed from the tree whenever we encounter an observation's start time. %' It is convenient ``catch up'' on this second task whenever we encounter a death. <>= z2 =0; utime=0; i2 =0; /* i2 tracks the start times */ for (i=0; i= time2[ii]); i2++) { jj = sort1[i2]; /* influence */ walkup(dnwt, dtwt, x[jj], wsum, ntree); imat[0][jj] += wsum[1]; imat[1][jj] += wsum[0]; imat[2][jj] += wsum[2]; addin(nwt, twt, x[jj], -wt[jj]); /*remove from main tree */ /* Cox variance */ walkup(nwt, twt, x[jj], wsum, ntree); z2 -= wt[jj]*(wsum[0]*(wt[jj] + 2*(wsum[1] + wsum[2])) + wsum[1]*(wt[jj] + 2*(wsum[0] + wsum[2])) + (wsum[0]-wsum[1])*(wsum[0]-wsum[1])); } ndeath=0; dwt=0; dwt2 =0; xsave=x[ii]; j2= i; adjtimewt = timewt[utime++]; /* pass 1 */ for (j=i; j>= survexp <- function(formula, data, weights, subset, na.action, rmap, times, method=c("ederer", "hakulinen", "conditional", "individual.h", "individual.s"), cohort=TRUE, conditional=FALSE, ratetable=survival::survexp.us, scale=1, se.fit, model=FALSE, x=FALSE, y=FALSE) { <> <> <> <> } @ The first few lines are standard. Keep a copy of the call, then manufacture a call to [[model.frame]] that contains only the arguments relevant to that function. <>= Call <- match.call() # keep the first element (the call), and the following selected arguments indx <- match(c('formula', 'data', 'weights', 'subset', 'na.action'), names(Call), nomatch=0) if (indx[1] ==0) stop("A formula argument is required") tform <- Call[c(1,indx)] # only keep the arguments we wanted tform[[1L]] <- quote(stats::model.frame) # change the function called Terms <- if(missing(data)) terms(formula, 'ratetable') else terms(formula, 'ratetable',data=data) @ The function works with two data sets, the user's data on an actual set of %' subjects and the reference ratetable. This leads to a particular nuisance, that the variable names in the data set may not match those in the ratetable. For instance the United States overall death rate table [[survexp.us]] expects 3 variables, as shown by [[summary(survexp.us)]] \begin{itemize} \item age = age in days for each subject at the start of follow-up \item sex = sex of the subject, ``male'' or ``female'' (the routine accepts any unique abbreviation and is case insensitive) \item year = date of the start of follow-up \end{itemize} Up until the most recent revision, the formula contained any necessary mapping between the variables in the data set and the ratetable. For instance \begin{verbatim} survexp( ~ sex + ratetable(age=age*365.25, sex=sex, year=entry.dt), data=mydata, ratetable=survexp.us) \end{verbatim} In this case the user's data set has a variable `age' containing age in years, along with sex and an entry date. This had to be changed for two reasons. The primary one is that the data in a [[ratetable]] call had to be converted into a matrix in order to ``pass through'' the model.frame logic. With the recent updates to coxph so that it remembers factor codings correctly in new data sets, it is advantageous to keep factors as factors. The second is that a coxph model with a large number of covariates induces a very long ratetable clause; at about 40 variable it caused one of the R internal routines to fail due to a long expression. A third reason, perhaps the most pressing in reality, is that I've always %' felt that the prior code was confusing since it used the same term 'ratetable' for two different tasks. The new process adds the [[rmap]] argument, an example would be [[rmap=list(age =age*365.25, year=entry.dt)]]. Any variables in the ratetable that are not found in [[rmap]] are assumed to not need a mapping, this would be [[sex]] in the above example. For backwards compatability we allow the old style argument, converting it into the new style. The [[rmap]] argument needs to be examined without evaluating it; we then add the appropriate extra variables into a temporary formula so that the model frame has all that is required. The ratetable variables then can be retrieved from the model frame. The [[pyears]] routine uses the same rmap argument; this segment of the code is given its own name so that it can be included there as well. <>= rate <- attr(Terms, "specials")$ratetable if(length(rate) > 1) stop("Can have only 1 ratetable() call in a formula") <> mf <- eval(tform, parent.frame()) @ <>= if(length(rate) == 1) { if (!missing(rmap)) stop("The ratetable() call in a formula is depreciated") stemp <- untangle.specials(Terms, 'ratetable') rcall <- as.call(parse(text=stemp$var)[[1]]) # as a call object rcall[[1]] <- as.name('list') # make it a call to list(.. Terms <- Terms[-stemp$terms] # remove from the formula } else if (!missing(rmap)) { rcall <- substitute(rmap) if (!is.call(rcall) || rcall[[1]] != as.name('list')) stop ("Invalid rcall argument") } else rcall <- NULL # A ratetable, but no rcall argument # Check that there are no illegal names in rcall, then expand it # to include all the names in the ratetable if (is.ratetable(ratetable)) { varlist <- names(dimnames(ratetable)) if (is.null(varlist)) varlist <- attr(ratetable, "dimid") # older style } else if(inherits(ratetable, "coxph") && !inherits(ratetable, "coxphms")) { ## Remove "log" and such things, to get just the list of # variable names varlist <- all.vars(delete.response(ratetable$terms)) } else stop("Invalid rate table") temp <- match(names(rcall)[-1], varlist) # 2,3,... are the argument names if (any(is.na(temp))) stop("Variable not found in the ratetable:", (names(rcall))[is.na(temp)]) if (any(!(varlist %in% names(rcall)))) { to.add <- varlist[!(varlist %in% names(rcall))] temp1 <- paste(text=paste(to.add, to.add, sep='='), collapse=',') if (is.null(rcall)) rcall <- parse(text=paste("list(", temp1, ")"))[[1]] else { temp2 <- deparse(rcall) rcall <- parse(text=paste("c(", temp2, ",list(", temp1, "))"))[[1]] } } @ The formula below is used only in the call to [[model.frame]] to ensure that the frame has both the formula and the ratetable variables. We don't want to modify the original formula, since we use it to create the $X$ matrix and the response variable. The non-obvious bit of code is the addition of an environment to the formula. The [[model.matrix]] routine has a non-standard evaluation - it uses the frame of the formula, rather than the parent.frame() argument below, along with the [[data]] to look up variables. If a formula is long enough deparse() will give two lines, hence the extra paste call to re-collapse it into one. <>= # Create a temporary formula, used only in the call to model.frame newvar <- all.vars(rcall) if (length(newvar) > 0) { temp <- paste(paste(deparse(Terms), collapse=""), paste(newvar, collapse='+'), sep='+') tform$formula <- as.formula(temp, environment(Terms)) } @ If the user data has 0 rows, e.g. from a mistaken [[subset]] statement that eliminated all subjects, we need to stop early. Otherwise the .C code fails in a nasty way. <>= n <- nrow(mf) if (n==0) stop("Data set has 0 rows") if (!missing(se.fit) && se.fit) warning("se.fit value ignored") weights <- model.extract(mf, 'weights') if (length(weights) ==0) weights <- rep(1.0, n) if (class(ratetable)=='ratetable' && any(weights !=1)) warning("weights ignored") if (any(attr(Terms, 'order') >1)) stop("Survexp cannot have interaction terms") if (!missing(times)) { if (any(times<0)) stop("Invalid time point requested") if (length(times) >1 ) if (any(diff(times)<0)) stop("Times must be in increasing order") } @ If a response variable was given, we only need the times and not the status. To be correct, computations need to be done for each of the times given in the [[times]] argument as well as for each of the unique y values. This ends up as the vector [[newtime]]. If a [[times]] argument was given we will subset down to only those values at the end. For a population rate table and the Ederer method the times argument is required. <>= Y <- model.extract(mf, 'response') no.Y <- is.null(Y) if (no.Y) { if (missing(times)) { if (is.ratetable(ratetable)) stop("either a times argument or a response is needed") } else newtime <- times } else { if (is.matrix(Y)) { if (is.Surv(Y) && attr(Y, 'type')=='right') Y <- Y[,1] else stop("Illegal response value") } if (any(Y<0)) stop ("Negative follow up time") # if (missing(npoints)) temp <- unique(Y) # else temp <- seq(min(Y), max(Y), length=npoints) temp <- unique(Y) if (missing(times)) newtime <- sort(temp) else newtime <- sort(unique(c(times, temp[temp>= ovars <- attr(Terms, 'term.labels') # rdata contains the variables matching the ratetable rdata <- data.frame(eval(rcall, mf), stringsAsFactors=TRUE) if (is.ratetable(ratetable)) { israte <- TRUE if (no.Y) { Y <- rep(max(times), n) } rtemp <- match.ratetable(rdata, ratetable) R <- rtemp$R } else if (inherits(ratetable, 'coxph')) { israte <- FALSE Terms <- ratetable$terms # if (!is.null(attr(Terms, 'offset'))) # stop("Cannot deal with models that contain an offset") # strats <- attr(Terms, "specials")$strata # if (length(strats)) # stop("survexp cannot handle stratified Cox models") # if (any(names(mf[,rate]) != attr(ratetable$terms, 'term.labels'))) stop("Unable to match new data to old formula") } else if (inherits(ratetable, "coxphms")) stop("survexp not defined for multi-state coxph models") else stop("Invalid ratetable") @ Now for some calculation. If cohort is false, then any covariates on the right hand side (other than the rate table) are irrelevant, the function returns a vector of expected values rather than survival curves. <>= if (substring(method, 1, 10) == "individual") { #individual survival if (no.Y) stop("for individual survival an observation time must be given") if (israte) temp <- survexp.fit (1:n, R, Y, max(Y), TRUE, ratetable) else { rmatch <- match(names(data), names(rdata)) if (any(is.na(rmatch))) rdata <- cbind(rdata, data[,is.na(rmatch)]) temp <- survexp.cfit(1:n, rdata, Y, 'individual', ratetable) } if (method == "individual.s") xx <- temp$surv else xx <- -log(temp$surv) names(xx) <- row.names(mf) na.action <- attr(mf, "na.action") if (length(na.action)) return(naresid(na.action, xx)) else return(xx) } @ Now for the more commonly used case: returning a survival curve. First see if there are any grouping variables. The results of the [[tcut]] function are often used in person-years analysis, which is somewhat related to expected survival. However tcut results aren't relevant here and we put in a check for the %' confused user. The strata command creates a single factor incorporating all the variables. <>= if (length(ovars)==0) X <- rep(1,n) #no categories else { odim <- length(ovars) for (i in 1:odim) { temp <- mf[[ovars[i]]] ctemp <- class(temp) if (!is.null(ctemp) && ctemp=='tcut') stop("Can't use tcut variables in expected survival") } X <- strata(mf[ovars]) } #do the work if (israte) temp <- survexp.fit(as.numeric(X), R, Y, newtime, method=="conditional", ratetable) else { temp <- survexp.cfit(as.numeric(X), rdata, Y, method, ratetable, weights) newtime <- temp$time } @ Now we need to package up the curves properly All the results can be returned as a single matrix of survivals with a common vector of times. If there was a times argument we need to subset to selected rows of the computation. <>= if (missing(times)) { n.risk <- temp$n surv <- temp$surv } else { if (israte) keep <- match(times, newtime) else { # The result is from a Cox model, and it's list of # times won't match the list requested in the user's call # Interpolate the step function, giving survival of 1 # for requested points that precede the Cox fit's # first downward step. The code is like summary.survfit. n <- length(temp$time) keep <- approx(temp$time, 1:n, xout=times, yleft=0, method='constant', f=0, rule=2)$y } if (is.matrix(temp$surv)) { surv <- (rbind(1,temp$surv))[keep+1,,drop=FALSE] n.risk <- temp$n[pmax(1,keep),,drop=FALSE] } else { surv <- (c(1,temp$surv))[keep+1] n.risk <- temp$n[pmax(1,keep)] } newtime <- times } newtime <- newtime/scale if (is.matrix(surv)) { dimnames(surv) <- list(NULL, levels(X)) out <- list(call=Call, surv= drop(surv), n.risk=drop(n.risk), time=newtime) } else { out <- list(call=Call, surv=c(surv), n.risk=c(n.risk), time=newtime) } @ Last do the standard things: add the model, x, or y components to the output if the user asked for them. (For this particular routine I can't think of %' a reason they every would.) Copy across summary information from the rate table computation if present, and add the method and class to the output. <>= if (model) out$model <- mf else { if (x) out$x <- X if (y) out$y <- Y } if (israte && !is.null(rtemp$summ)) out$summ <- rtemp$summ if (no.Y) out$method <- 'Ederer' else if (conditional) out$method <- 'conditional' else out$method <- 'cohort' class(out) <- c('survexp', 'survfit') out @ \subsection{Parsing the covariates list} For a multi-state Cox model we allow a list of formulas to take the place of the \code{formula} argument. The first element of the list is the default formula, later elements are of the form \code{transitions ~ formula/options}, where the left hand side denotes one or more transitions, and the right hand side is used to augment the basic formula wrt those transitions. Step 1 is to break the formula into parts. There will be a list of left sides, a list of right sides, and a list of options. From this we can create a single ``pseudo formula'' that is used to drive the model.frame process, which ensures that all of the variables we need will be found in the model frame. Further processing has to wait until after the model frame has been constructed, i.e., if a left side referred to state ``deathh'' that might be a real state or a typing mistake, we can't know until the data is in hand. Should we walk the parse tree of the formula, or convert it to character and use string manipulations? The latter looks promising until you see a fragment like this: \code{entry:death ~ age/sex + ns(weight/height, df=4) / common} Walking the parse tree is a bit more subtle, but we then can take advantage of all the knowledge built into the R parser. A formula is a 3 element list of ``~'', leftside, rightside, or 2 elements if it has only a right hand side. Legal ones for coxph have both left and right. <>= parsecovar1 <- function(flist, statedata) { if (any(sapply(flist, function(x) !inherits(x, "formula")))) stop("an element of the formula list is not a formula") if (any(sapply(flist, length) != 3)) stop("all formulas must have a left and right side") # split the formulas into a right hand and left hand side lhs <- lapply(flist, function(x) x[-3]) # keep the ~ rhs <- lapply(flist, function(x) x[[3]]) # don't keep the ~ rhs <- parse_rightside(rhs) <> list(rhs = rhs, lhs= lterm) } @ \begin{figure} \includegraphics{figures/fig1.pdf} \caption{The parse tree for the formula \code{1:3 +2:3 ~ strata(sex)/(age + trt) + ns(weight/ht, df=4) / common + shared}} \label{figparse} \end{figure} Figure \ref{figparse} shows the parse tree for a complex formula. The following function splits the formula at the rightmost slash, ignoring the inside of any function or parenthesised phrase. Recursive functions like this are almost impossible to read, but luckily it is short. The formula recurrs on the left and right side of +*: and \%in\%, and on binary - (but not on unary -). <>= rightslash <- function(x) { if (class(x) != 'call') return(x) else { if (x[[1]] == as.name('/')) return(list(x[[2]], x[[3]])) else if (x[[1]]==as.name('+') || (x[[1]]==as.name('-') && length(x)==3)|| x[[1]]==as.name('*') || x[[1]]==as.name(':') || x[[1]]==as.name('%in%')) { temp <- rightslash(x[[3]]) if (is.list(temp)) { x[[3]] <- temp[[1]] return(list(x, temp[[2]])) } else { temp <- rightslash(x[[2]]) if (is.list(temp)) { x[[2]] <- temp[[2]] return(list(temp[[1]], x)) } else return(x) } } else return(x) } } @ There are 4 possble options of common, shared, and init. The first 2 appear just as words, the last should have a set of values attached which become the \code{ival} vector. There will, of course, one day be a user with a variable named \code{common} who wants a nested term \code{x/common}. Since we don't look inside parenthesis they will be able to use \code{1:3 ~ (x/common)}. <>= parse_rightside <- function(rhs) { parts <- lapply(rhs, rightslash) new <- lapply(parts, function(opt) { tform <- ~ x # a skeleton, "x" will be replaced if (!is.list(opt)) { # no options for this line tform[[2]] <- opt list(formula = tform, ival = NULL, common = FALSE, shared = FALSE) } else{ # treat the option list as though it were a formula temp <- ~ x temp[[2]] <- opt[[2]] optterms <- terms(temp) ff <- rownames(attr(optterms, "factors")) index <- match(ff, c("common", "shared", "init")) if (any(is.na(index))) stop("option not recognized in a covariates formula: ", paste(ff[is.na(index)], collapse=", ")) common <- any(index==1) shared <- any(index==2) if (any(index==3)) { optatt <- attributes(optterms) j <- optatt$variables[1 + which(index==3)] j[[1]] <- as.name("list") ival <- unlist(eval(j, parent.frame())) } else ival <- NULL tform[[2]] <- opt[[1]] list(formula= tform, ival= ival, common= common, shared=shared) } }) new } @ The left hand side of each formula specifies the set of transitions to which the covariates apply, and is more complex. Say instance that we had 7 states and the following statedata data set. \begin{center} \begin{tabular}{cccc} state & A& N& death \\ \hline A-N- & 0& 0 & 0\\ A+N- & 1& 0 & 0\\ A-N1 & 0& 1 & 0\\ A+N1 & 1& 1 & 0\\ A-N2 & 0& 2 & 0\\ A+N2 & 1& 2 & 0\\ Death& NA & NA& 1 \end{tabular} \end{center} Here are some valid transitions \begin{enumerate} \item 0:state('A+N+'), any transition to the A+N+ state \item state('A-N-'):death(0), a transition from A-N-, but not to death \item A(0):A(1), any of the 4 changes that start with A=0 and end with A=1 \item N(0):N(1,2) + N(1):N(2), an upward change of N \item 'A-N-':c('A-N+','A+N-'); if there is no variable then the overall state is assumed \item 1:3 + 2:3; we can refer to states by number, and we can have multiples \end{enumerate} <>= # deal with the left hand side of the formula # the next routine cuts at '+' signs pcut <- function(form) { if (length(form)==3) { if (form[[1]] == '+') c(pcut(form[[2]]), pcut(form[[3]])) else if (form[[1]] == '~') pcut(form[[2]]) else list(form) } else list(form) } lcut <- lapply(lhs, function(x) pcut(x[[2]])) @ We now have one list per formula, each list is either a single term or a list of terms (case 4 above). To make evaluation easier, create functions that append their name to a list of values. I have not yet found a way to do this without eval(parse()), which always seems clumsy. A use for the labels without an argument will arise later, hence the double environments. Repeating the list above, this is what we want to end with \begin{itemize} \item a list with one element per formula in the covariates list \item each element is a list, with one element per term: multiple a:b terms are allowed separated by + signs \item each of these level 3 elements is a list with two elements ``left'' and ``right'', for the two sides of the : operator \item left and right will be one of 3 forms: a simple vector, a one element list containing the stateid, or a two element list containing the stateid and the values. Any word that doesn't match one of the column names of statedata ends up as a vector. \end{itemize} <>= env1 <- new.env(parent= parent.frame(2)) env2 <- new.env(parent= env1) if (missing(statedata)) { assign("state", function(...) list(stateid= "state", values=c(...)), env1) assign("state", list(stateid="state")) } else { for (i in statedata) { assign(i, eval(list(stateid=i)), env2) tfun <- eval(parse(text=paste0("function(...) list(stateid='" , i, "', values=c(...))"))) assign(i, tfun, env1) } } lterm <- lapply(lcut, function(x) { lapply(x, function(z) { if (length(z)==1) { temp <- eval(z, envir= env2) if (is.list(temp) && names(temp)[[1]] =="stateid") temp else temp } else if (length(z) ==3 && z[[1]]==':') list(left=eval(z[[2]], envir=env2), right=eval(z[[3]], envir=env2)) else stop("invalid term: ", deparse(z)) }) }) @ The second call, which builds tmap, the terms map. Arguments are the results from the first pass, the statedata data frame, the default formula, the terms structure from the full formula, and the transitions count. One nuisance is that the terms function sometimes inverts things. For example in the formula \code{terms(~ x1 + x1:iage + x2 + x2:iage)} the label for the second of these becomes \code{iage:x2}. I'm guessing it is because the variable first appear in the order x1, iage, x2 and labels make use of that order. But when we look at the formula fragment \code{~ x2 + x2:iage} the terms will be in the other order. A way out of this is to use the simple \code{termmatch} function below, which keys off of the factors attribute instead of the names. <>= termmatch <- function(f1, f2) { # look for f1 in f2, each the factors attribute of a terms object if (length(f1)==0) return(NULL) # a formula with only ~1 irow <- match(rownames(f1), rownames(f2)) if (any(is.na(irow))) stop ("termmatch failure 1") hashfun <- function(j) sum(ifelse(j==0, 0, 2^(seq(along.with=j)))) hash1 <- apply(f1, 2, hashfun) hash2 <- apply(f2[irow,,drop=FALSE], 2, hashfun) index <- match(hash1, hash2) if (any(is.na(index))) stop("termmatch failure 2") index } parsecovar2 <- function(covar1, statedata, dformula, Terms, transitions,states) { if (is.null(statedata)) statedata <- data.frame(state = states, stringsAsFactors=FALSE) else { if (is.null(statedata$state)) stop("the statedata data set must contain a variable 'state'") indx1 <- match(states, statedata$state, nomatch=0) if (any(indx1==0)) stop("statedata does not contain all the possible states: ", states[indx1==0]) statedata <- statedata[indx1,] # put it in order } # Statedata might have rows for states that are not in the data set, # for instance if the coxph call had used a subset argument. Any of # those were eliminated above. # Likewise, the formula list might have rules for transitions that are # not present. Don't worry about it at this stage. allterm <- attr(Terms, 'factors') nterm <- ncol(allterm) # create a map for every transition, even ones that are not used. # at the end we will thin it out # It has an extra first row for intercept (baseline) # Fill it in with the default formula nstate <- length(states) tmap <- array(0, dim=c(nterm+1, nstate, nstate)) dmap <- array(seq_len(length(tmap)), dim=c(nterm+1, nstate, nstate)) #unique values dterm <- termmatch(attr(terms(dformula), "factors"), allterm) dterm <- c(1L, 1L+ dterm) # add intercept tmap[dterm,,] <- dmap[dterm,,] inits <- NULL if (!is.null(covar1)) { <> } <> } @ Now go through the formulas one by one. The left hand side tells us which state:state transitions to fill in, the right hand side tells the variables. The code block below goes through lhs element(s) for a single formula. That element is itself a list which has an entry for each term, and that entry can have left and right portions. <>= state1 <- state2 <- NULL for (x in lhs) { # x is one term if (!is.list(x) || is.null(x$left)) stop("term found without a ':' ", x) # left of the colon if (!is.list(x$left) && length(x$left) ==1 && x$left==0) temp1 <- 1:nrow(statedata) else if (is.numeric(x$left)) { temp1 <- as.integer(x$left) if (any(temp1 != x$left)) stop("non-integer state number") if (any(temp1 <1 | temp1> nstate)) stop("numeric state is out of range") } else if (is.list(x$left) && names(x$left)[1] == "stateid"){ if (is.null(x$left$value)) stop("state variable with no list of values: ",x$left$stateid) else { if (any(k= is.na(match(x$left$stateid, names(statedata))))) stop(x$left$stateid[k], ": state variable not found") zz <- statedata[[x$left$stateid]] if (any(k= is.na(match(x$left$value, zz)))) stop(x$left$value[k], ": state value not found") temp1 <- which(zz %in% x$left$value) } } else { k <- match(x$left, statedata$state) if (any(is.na(k))) stop(x$left[is.na(k)], ": state not found") temp1 <- which(statedata$state %in% x$left) } # right of colon if (!is.list(x$right) && length(x$right) ==1 && x$right ==0) temp2 <- 1:nrow(statedata) else if (is.numeric(x$right)) { temp2 <- as.integer(x$right) if (any(temp2 != x$right)) stop("non-integer state number") if (any(temp2 <1 | temp2> nstate)) stop("numeric state is out of range") } else if (is.list(x$right) && names(x$right)[1] == "stateid") { if (is.null(x$right$value)) stop("state variable with no list of values: ",x$right$stateid) else { if (any(k= is.na(match(x$right$stateid, names(statedata))))) stop(x$right$stateid[k], ": state variable not found") zz <- statedata[[x$right$stateid]] if (any(k= is.na(match(x$right$value, zz)))) stop(x$right$value[k], ": state value not found") temp2 <- which(zz %in% x$right$value) } } else { k <- match(x$right, statedata$state) if (any(is.na(k))) stop(x$right[k], ": state not found") temp2 <- which(statedata$state %in% x$right) } state1 <- c(state1, rep(temp1, length(temp2))) state2 <- c(state2, rep(temp2, each=length(temp1))) } @ At the end it has created to vectors state1 and state2 listing all the pairs of states that are indicated. The init clause (initial values) are gathered but not checked: we don't yet know how many columns a term will expand into. tmap is a 3 way array: term, state1, state2 containing coefficient numbers and zeros. <>= for (i in 1:length(covar1$rhs)) { rhs <- covar1$rhs[[i]] lhs <- covar1$lhs[[i]] # one rhs and one lhs per formula <> npair <- length(state1) # number of state:state pairs for this line # update tmap for this set of transitions # first, what variables are mentioned, and check for errors rterm <- terms(rhs$formula) rindex <- 1L + termmatch(attr(rterm, "factors"), allterm) # the update.formula function is good at identifying changes # formulas that start with "- x" have to be pasted on carefully temp <- substring(deparse(rhs$formula, width.cutoff=500), 2) if (substring(temp, 1,1) == '-') dummy <- formula(paste("~ .", temp)) else dummy <- formula(paste("~. +", temp)) rindex1 <- termmatch(attr(terms(dformula), "factors"), allterm) rindex2 <- termmatch(attr(terms(update(dformula, dummy)), "factors"), allterm) dropped <- 1L + rindex1[is.na(match(rindex1, rindex2))] # remember the intercept if (length(dropped) >0) { for (k in 1:npair) tmap[dropped, state1[k], state2[k]] <- 0 } # grab initial values if (length(rhs$ival)) inits <- c(inits, list(term=rindex, state1=state1, state2= state2, init= rhs$ival)) # adding -1 to the front is a trick, to check if there is a "+1" term dummy <- ~ -1 + x dummy[[2]][[3]] <- rhs$formula if (attr(terms(dummy), "intercept") ==1) rindex <- c(1L, rindex) # an update of "- sex" won't generate anything to add # dmap is simply an indexed set of unique values to pull from, so that # no number is used twice if (length(rindex) > 0) { # rindex = things to add if (rhs$common) { j <- dmap[rindex, state1[1], state2[1]] for(k in 1:npair) tmap[rindex, state1[k], state2[k]] <- j } else { for (k in 1:npair) tmap[rindex, state1[k], state2[k]] <- dmap[rindex, state1[k], state2[k]] } } # Deal with the shared argument, using - for a separate coef if (rhs$shared && npair>1) { j <- dmap[1, state1[1], state2[1]] for (k in 2:npair) tmap[1, state1[k], state2[k]] <- -j } } @ Fold the 3-dimensional tmap into a matrix with terms as rows and one column for each transition that actually occured. <>= i <- match("(censored)", colnames(transitions), nomatch=0) if (i==0) t2 <- transitions else t2 <- transitions[,-i, drop=FALSE] # transitions to 'censor' don't count indx1 <- match(rownames(t2), states) indx2 <- match(colnames(t2), states) tmap2 <- matrix(0L, nrow= 1+nterm, ncol= sum(t2>0)) trow <- row(t2)[t2>0] tcol <- col(t2)[t2>0] for (i in 1:nrow(tmap2)) { for (j in 1:ncol(tmap2)) tmap2[i,j] <- tmap[i, indx1[trow[j]], indx2[tcol[j]]] } # Remember which hazards had ph # tmap2[1,] is the 'intercept' row # If the hazard for colum 6 is proportional to the hazard for column 2, # the tmap2[1,2] = tmap[1,6], and phbaseline[6] =2 temp <- tmap2[1,] tmap2[1,] <- match(abs(tmap2[1,]), unique(abs(temp))) phbaseline <- ifelse(temp<0, tmap2[1,], 0) if (nrow(tmap2) > 1) tmap2[-1,] <- match(tmap2[-1,], unique(c(0L, tmap2[-1,]))) -1L dimnames(tmap2) <- list(c("(Baseline)", colnames(allterm)), paste(indx1[trow], indx2[tcol], sep=':')) # mapid gives the from,to for each realized state list(tmap = tmap2, inits=inits, mapid= cbind(from=indx1[trow], to=indx2[tcol]), phbaseline = phbaseline) @ Last is a helper routine that converts tmap, which has one row per term, into cmap, which has one row per coefficient. Both have one column per transition. It uses the assign attribute of the X matrix along with the column names. Consider the model \code{~ x1 + strata(x2) + factor(x3)} where x3 has 4 levels. The Xassign vector will be 1, 3, 3, 3, since it refers to terms and there are 3 columns of X for term number 3. If there were an intercept the first column of X would be a 1 and Xassign would be 0, 1, 3, 3, 3. Let's say that there were 3 transitions and tmap looks like this: \begin{tabular}{rccc} & 1:2 & 1:3 & 2:3 \\ (Baseline) & 1 & 2 & 3 \\ x1 & 1 & 4 & 4 \\ strata(x2) & 2 & 5 & 6 \\ factor(x3) & 3 & 3 & 7 \end{tabular} The cmap matrix will ignore rows 1 and 3 since they do not correspond to coefficients in the model. <>= parsecovar3 <- function(tmap, Xcol, Xassign, phbaseline=NULL) { # sometime X will have an intercept, sometimes not; cmap never does hasintercept <- (Xassign[1] ==0) ptemp <- phbaseline[phbaseline >0] nph.coef <- length(ptemp) nph.row <- length(unique(ptemp)) cmap <- matrix(0L, length(Xcol) + nph.row - hasintercept, ncol(tmap)) uterm <- unique(Xassign[Xassign != 0]) # terms that will have coefficients xcount <- table(factor(Xassign, levels=1:max(Xassign))) mult <- 1+ max(xcount) # temporary scaling ii <- 0 for (i in uterm) { k <- seq_len(xcount[i]) for (j in 1:ncol(tmap)) cmap[ii+k, j] <- if(tmap[i+1,j]==0) 0 else tmap[i+1,j]*mult +k ii <- ii + max(k) } if (nph.row > 0) { i <- length(Xcol)- hasintercept # non-ph rows in cmap j <- cbind(i+ match(ptemp, unique(ptemp)), which(phbaseline>0)) cmap[j] <- max(cmap) + seq(along.with =ptemp) newname <- paste0("ph(",colnames(tmap)[unique(ptemp)], ")") } else newname <- NULL # renumber coefs as 1, 2, 3, ... cmap[,] <- match(cmap, sort(unique(c(0L, cmap)))) -1L colnames(cmap) <- colnames(tmap) if (hasintercept) rownames(cmap) <- c(Xcol[-1], newname) else rownames(cmap) <- c(Xcol, newname) cmap } @ \section{Person years} The person years routine and the expected survival code are the two parts of the survival package that make use of external rate tables, of which the United States mortality tables \code{survexp.us} and \code{survexp.usr} are examples contained in the package. The arguments for pyears are \begin{description} \item[formula] The model formula. The right hand side consists of grouping variables and is essentially identical to [[survfit]], the result of the model will be a table of results with dimensions determined from the right hand variables. The formula can include an optional [[ratetable]] directive; but this style has been superseded by the [[rmap]] argument. \item [data, weights, subset, na.action] as usual \item[rmap] an optional mapping for rate table variables, see more below. \item[ratetable] the population rate table to use as a reference. This can either be a ratetable object or a previously fitted Cox model \item[scale] Scale the resulting output times, e.g., 365.25 to turn days into years. \item[expect] Should the output table include the expected number of events, or the expected number of person-years of observation? \item[model, x, y] as usual \item[data.frame] if true the result is returned as a data frame, if false as a set of tables. \end{description} <>= pyears <- function(formula, data, weights, subset, na.action, rmap, ratetable, scale=365.25, expect=c('event', 'pyears'), model=FALSE, x=FALSE, y=FALSE, data.frame=FALSE) { <> <> <> } @ Start out with the standard model processing, which involves making a copy of the input call, but keeping only the arguments we want. We then process the special argument [[rmap]]. This is discussed in the section on the [[survexp]] function so we need not repeat the explantation here. <>= expect <- match.arg(expect) Call <- match.call() # create a call to model.frame() that contains the formula (required) # and any other of the relevant optional arguments # then evaluate it in the proper frame indx <- match(c("formula", "data", "weights", "subset", "na.action"), names(Call), nomatch=0) if (indx[1] ==0) stop("A formula argument is required") tform <- Call[c(1,indx)] # only keep the arguments we wanted tform[[1L]] <- quote(stats::model.frame) # change the function called Terms <- if(missing(data)) terms(formula, 'ratetable') else terms(formula, 'ratetable',data=data) if (any(attr(Terms, 'order') >1)) stop("Pyears cannot have interaction terms") rate <- attr(Terms, "specials")$ratetable if (length(rate) >0 || !missing(rmap) || !missing(ratetable)) { has.ratetable <- TRUE if(length(rate) > 1) stop("Can have only 1 ratetable() call in a formula") if (missing(ratetable)) stop("No rate table specified") <> } else has.ratetable <- FALSE mf <- eval(tform, parent.frame()) Y <- model.extract(mf, 'response') if (is.null(Y)) stop ("Follow-up time must appear in the formula") if (!is.Surv(Y)){ if (any(Y <0)) stop ("Negative follow up time") Y <- as.matrix(Y) if (ncol(Y) >2) stop("Y has too many columns") } else { stype <- attr(Y, 'type') if (stype == 'right') { if (any(Y[,1] <0)) stop("Negative survival time") nzero <- sum(Y[,1]==0 & Y[,2] ==1) if (nzero >0) warning(paste(nzero, "observations with an event and 0 follow-up time,", "any rate calculations are statistically questionable")) } else if (stype != 'counting') stop("Only right-censored and counting process survival types are supported") } n <- nrow(Y) if (is.null(n) || n==0) stop("Data set has 0 observations") weights <- model.extract(mf, 'weights') if (is.null(weights)) weights <- rep(1.0, n) @ The next step is to check out the ratetable. For a population rate table a set of consistency checks is done by the [[match.ratetable]] function, giving a set of sanitized indices [[R]]. This function wants characters turned to factors. For a Cox model [[R]] will be a model matix whose covariates are coded in exactly the same way that variables were coded in the original Cox model. We call the model.matrix.coxph function so as not to have to repeat the steps found there (remove cluster statements, etc). <>= # rdata contains the variables matching the ratetable if (has.ratetable) { rdata <- data.frame(eval(rcall, mf), stringsAsFactors=TRUE) if (is.ratetable(ratetable)) { israte <- TRUE rtemp <- match.ratetable(rdata, ratetable) R <- rtemp$R } else if (inherits(ratetable, 'coxph') && !inherits(ratetable, "coxphms")) { israte <- FALSE Terms <- ratetable$terms if (!is.null(attr(Terms, 'offset'))) stop("Cannot deal with models that contain an offset") strats <- attr(Terms, "specials")$strata if (length(strats)) stop("pyears cannot handle stratified Cox models") if (any(names(mf[,rate]) != attr(ratetable$terms, 'term.labels'))) stop("Unable to match new data to old formula") R <- model.matrix.coxph(ratetable, data=rdata) } else stop("Invalid ratetable") } @ Now we process the non-ratetable variables. Those of class [[tcut]] set up time-dependent classes. For these the cutpoints attribute sets the intervals, if there were 4 cutpoints of 1, 5,6, and 10 the 3 intervals will be 1-5, 5-6 and 6-10, and odims will be 3. All other variables are treated as factors. <>= ovars <- attr(Terms, 'term.labels') if (length(ovars)==0) { # no categories! X <- rep(1,n) ofac <- odim <- odims <- ocut <- 1 } else { odim <- length(ovars) ocut <- NULL odims <- ofac <- double(odim) X <- matrix(0, n, odim) outdname <- vector("list", odim) names(outdname) <- attr(Terms, 'term.labels') for (i in 1:odim) { temp <- mf[[ovars[i]]] if (inherits(temp, 'tcut')) { X[,i] <- temp temp2 <- attr(temp, 'cutpoints') odims[i] <- length(temp2) -1 ocut <- c(ocut, temp2) ofac[i] <- 0 outdname[[i]] <- attr(temp, 'labels') } else { temp2 <- as.factor(temp) X[,i] <- temp2 temp3 <- levels(temp2) odims[i] <- length(temp3) ofac[i] <- 1 outdname[[i]] <- temp3 } } } @ Now do the computations. The code above has separated out the variables into 3 groups: \begin{itemize} \item The variables in the rate table. These determine where we \emph{start} in the rate table with respect to retrieving the relevant death rates. For the US table [[survexp.us]] this will be the date of study entry, age (in days) at study entry, and sex of each subject. \item The variables on the right hand side of the model. These are interpreted almost identically to a call to [[table]], with special treatment for those of class \emph{tcut}. \item The response variable, which tells the number of days of follow-up and optionally the status at the end of follow-up. \end{itemize} Start with the rate table variables. There is an oddity about US rate tables: the entry for age (year=1970, age=55) contains the daily rate for anyone who turns 55 in that year, from their birthday forward for 365 days. So if your birthday is on Oct 2, the 1970 table applies from 2Oct 1970 to 1Oct 1971. The underlying C code wants to make the 1970 rate table apply from 1Jan 1970 to 31Dec 1970. The easiest way to finess this is to fudge everyone's enter-the-study date. If you were born in March but entered in April, make it look like you entered in Febuary; that way you get the first 11 months at the entry year's rates, etc. The birth date is entry date - age in days (based on 1/1/1970). The other aspect of the rate tables is that ``older style'' tables, those that have the factor attribute, contained only decennial data which the C code would interpolate on the fly. The value of [[atts$factor]] was 10 indicating that there are 10 years in the interpolation interval. The newer tables do not do this and the C code is passed a 0/1 for continuous (age and year) versus discrete (sex, race). <>= ocut <-c(ocut,0) #just in case it were of length 0 osize <- prod(odims) if (has.ratetable) { #include expected atts <- attributes(ratetable) datecheck <- function(x) inherits(x, c("Date", "POSIXt", "date", "chron")) cuts <- lapply(attr(ratetable, "cutpoints"), function(x) if (!is.null(x) & datecheck(x)) ratetableDate(x) else x) if (is.null(atts$type)) { #old stlye table rfac <- atts$factor us.special <- (rfac >1) } else { rfac <- 1*(atts$type ==1) us.special <- (atts$type==4) } if (any(us.special)) { #special handling for US pop tables if (sum(us.special) > 1) stop("more than one type=4 in a rate table") # Someone born in June of 1945, say, gets the 1945 US rate until their # next birthday. But the underlying logic of the code would change # them to the 1946 rate on 1/1/1946, which is the cutpoint in the # rate table. We fudge by faking their enrollment date back to their # birth date. # # The cutpoint for year has been converted to days since 1/1/1970 by # the ratetableDate function. (Date objects in R didn't exist when # rate tables were conceived.) if (is.null(atts$dimid)) dimid <- names(atts$dimnames) else dimid <- atts$dimid cols <- match(c("age", "year"), dimid) if (any(is.na(cols))) stop("ratetable does not have expected shape") # The format command works for Dates, use it to get an offset bdate <- as.Date("1970-01-01") + (R[,cols[2]] - R[,cols[1]]) byear <- format(bdate, "%Y") offset <- as.numeric(bdate - as.Date(paste0(byear, "-01-01"))) R[,cols[2]] <- R[,cols[2]] - offset # Doctor up "cutpoints" - only needed for (very) old style rate tables # for which the C code does interpolation on the fly if (any(rfac >1)) { temp <- which(us.special) nyear <- length(cuts[[temp]]) nint <- rfac[temp] #intervals to interpolate over cuts[[temp]] <- round(approx(nint*(1:nyear), cuts[[temp]], nint:(nint*nyear))$y - .0001) } } docount <- is.Surv(Y) temp <- .C(Cpyears1, as.integer(n), as.integer(ncol(Y)), as.integer(is.Surv(Y)), as.double(Y), as.double(weights), as.integer(length(atts$dim)), as.integer(rfac), as.integer(atts$dim), as.double(unlist(cuts)), as.double(ratetable), as.double(R), as.integer(odim), as.integer(ofac), as.integer(odims), as.double(ocut), as.integer(expect=='event'), as.double(X), pyears=double(osize), pn =double(osize), pcount=double(if(docount) osize else 1), pexpect=double(osize), offtable=double(1))[18:22] } else { #no expected docount <- as.integer(ncol(Y) >1) temp <- .C(Cpyears2, as.integer(n), as.integer(ncol(Y)), as.integer(docount), as.double(Y), as.double(weights), as.integer(odim), as.integer(ofac), as.integer(odims), as.double(ocut), as.double(X), pyears=double(osize), pn =double(osize), pcount=double(if (docount) osize else 1), offtable=double(1)) [11:14] } @ Create the output object. <>= has.tcut <- any(sapply(mf, function(x) inherits(x, 'tcut'))) if (data.frame) { # Create a data frame as the output, rather than a set of # rate tables if (length(ovars) ==0) { # no variables on the right hand side keep <- TRUE df <- data.frame(pyears= temp$pyears/scale, n = temp$n) } else { keep <- (temp$pyears >0) # what rows to keep in the output # grab prototype rows from the model frame, this preserves class # (unless it is a tcut variable, then we know what to do) tdata <- lapply(1:length(ovars), function(i) { temp <- mf[[ovars[i]]] if (inherits(temp, "tcut")) { #if levels are numeric, return numeric if (is.numeric(outdname[[i]])) outdname[[i]] else factor(outdname[[i]], outdname[[i]]) # else factor } else temp[match(outdname[[i]], temp)] }) tdata$stringsAsFactors <- FALSE # argument for expand.grid df <- do.call("expand.grid", tdata)[keep,,drop=FALSE] names(df) <- ovars df$pyears <- temp$pyears[keep]/scale df$n <- temp$pn[keep] } row.names(df) <- NULL # toss useless 'creation history' if (has.ratetable) df$expected <- temp$pexpect[keep] if (expect=='pyears') df$expected <- df$expected/scale if (docount) df$event <- temp$pcount[keep] # if any of the predictors were factors, make them factors in the output for (i in 1:length(ovars)){ if (is.factor( mf[[ovars[i]]])) df[[ovars[i]]] <- factor(df[[ovars[i]]], levels( mf[[ovars[i]]])) } out <- list(call=Call, data= df, offtable=temp$offtable/scale, tcut=has.tcut) if (has.ratetable && !is.null(rtemp$summ)) out$summary <- rtemp$summ } else if (prod(odims) ==1) { #don't make it an array out <- list(call=Call, pyears=temp$pyears/scale, n=temp$pn, offtable=temp$offtable/scale, tcut = has.tcut) if (has.ratetable) { out$expected <- temp$pexpect if (expect=='pyears') out$expected <- out$expected/scale if (!is.null(rtemp$summ)) out$summary <- rtemp$summ } if (docount) out$event <- temp$pcount } else { out <- list(call = Call, pyears= array(temp$pyears/scale, dim=odims, dimnames=outdname), n = array(temp$pn, dim=odims, dimnames=outdname), offtable = temp$offtable/scale, tcut=has.tcut) if (has.ratetable) { out$expected <- array(temp$pexpect, dim=odims, dimnames=outdname) if (expect=='pyears') out$expected <- out$expected/scale if (!is.null(rtemp$summ)) out$summary <- rtemp$summ } if (docount) out$event <- array(temp$pcount, dim=odims, dimnames=outdname) } out$observations <- nrow(mf) out$terms <- Terms na.action <- attr(mf, "na.action") if (length(na.action)) out$na.action <- na.action if (model) out$model <- mf else { if (x) out$x <- X if (y) out$y <- Y } class(out) <- 'pyears' out @ \subsection{Print and summary} The print function for pyear gives a very abbreviated printout: just a few lines. It works with pyears objects with or without a data component. <>= print.pyears <- function(x, ...) { if (!is.null(cl<- x$call)) { cat("Call:\n") dput(cl) cat("\n") } if (is.null(x$data)) { if (!is.null(x$event)) cat("Total number of events:", format(sum(x$event)), "\n") cat ( "Total number of person-years tabulated:", format(sum(x$pyears)), "\nTotal number of person-years off table:", format(x$offtable), "\n") } else { if (!is.null(x$data$event)) cat("Total number of events:", format(sum(x$data$event)), "\n") cat ( "Total number of person-years tabulated:", format(sum(x$data$pyears)), "\nTotal number of person-years off table:", format(x$offtable), "\n") } if (!is.null(x$summary)) { cat("Matches to the chosen rate table:\n ", x$summary) } cat("Observations in the data set:", x$observations, "\n") if (!is.null(x$na.action)) cat(" (", naprint(x$na.action), ")\n", sep='') cat("\n") invisible(x) } @ The summary function attempts to create output that looks like a pandoc table, which in turn makes it mesh nicely with Rstudio. Pandoc has 4 types of tables: with and without vertical bars and with single or multiple rows per cell. If the pyears object has only a single dimension then our output will be a simple table with a row or column for each of the output types (see the vertical argument). The result will be a simple table or a ``pipe'' table depending on the vline argument. For two or more dimensions the output follows the usual R strategy for printing an array, but with each ``cell'' containing all of the summaries for that combination of predictors, thus giving either a ``multiline'' or ``grid'' table. The default values of no vertical lines makes the tables appropriate for non-pandoc output such as a terminal session. <>= summary.pyears <- function(object, header=TRUE, call=header, n= TRUE, event=TRUE, pyears=TRUE, expected = TRUE, rate = FALSE, rr = expected, ci.r = FALSE, ci.rr = FALSE, totals=FALSE, legend=TRUE, vline = FALSE, vertical = TRUE, nastring=".", conf.level=0.95, scale= 1, ...) { # Usual checks if (!inherits(object, "pyears")) stop("input must be a pyears object") temp <- c(is.logical(header), is.logical(call), is.logical(n), is.logical(event) , is.logical(pyears), is.logical(expected), is.logical(rate), is.logical(ci.r), is.logical(rr), is.logical(ci.rr), is.logical(vline), is.logical(vertical), is.logical(legend), is.logical(totals)) tname <- c("header", "call", "n", "event", "pyears", "expected", "rate", "ci.r", "rr", "ci.rr", "vline", "vertical", "legend", "totals") if (any(!temp) || length(temp) != 14 || any(is.na(temp))) { stop("the ", paste(tname[!temp], collapse=", "), "argument(s) must be single logical values") } if (!is.numeric(conf.level) || conf.level <=0 || conf.level >=1 | length(conf.level) > 1 || is.na(conf.level) > 1) stop("conf.level must be a single numeric between 0 and 1") if (is.na(scale) || !is.numeric(scale) || length(scale) !=1 || scale <=0) stop("scale must be a value > 0") vname <- attr(terms(object), "term.labels") #variable names if (!is.null(object$data)) { # Extra work: restore the tables which had been unpacked into a df # All of the categories are factors in this case tdata <- object$data[vname] # the conditioning variables dname <- lapply(tdata, function(x) { if (is.factor(x)) levels(x) else sort(unique(x))}) # dimnames dd <- sapply(dname, length) # dim of arrays index <- tapply(tdata[,1], tdata) restore <- c('n', 'event', 'pyears', 'expected') #do these, if present restore <- restore[restore %in% names(object$data)] new <- lapply(object$data[restore], function(x) { temp <- array(0L, dim=dd, dimnames=dname) temp[index] <- x temp} ) object <- c(object, new) } if (is.null(object$expected)) { expected <- FALSE rr <- FALSE ci.rr <- FALSE } if (is.null(object$event)) { event <- FALSE rate <- FALSE ci.r <- FALSE rr <- FALSE ci.rr <- FALSE } # print out the front matter if (call && !is.null(object$call)) { cat("Call: ") dput(object$call) cat("\n") } if (header) { cat("number of observations =", object$observations) if (length(object$omit)) cat(" (", naprint(object$omit), ")\n", sep="") else cat("\n") if (object$offtable > 0) cat(" Total time lost (off table)", format(object$offtable), "\n") cat("\n") } # Add in totals if requested if (totals) { # if the pyear object was based on any time dependent cuts, then # the "n" component cannot be totaled up. tcut <- if (is.null(object$tcut)) TRUE else object$tcut object$n <- pytot(object$n, na=tcut) object$pyears <- pytot(object$pyears) if (event) object$event <- pytot(object$event) if (expected) object$expected <- pytot(object$expected) } dd <- dim(object$n) vname <- attr(terms(object), "term.labels") #variable names <> if (length(dd) ==1) { # 1 dimensional table <> } else { # more than 1 dimension <> } invisible(object) } <> @ <>= # Put the elements to be printed onto a list pname <- (tname[3:6])[c(n, event, pyears, expected)] plist <- object[pname] if (rate) { pname <- c(pname, "rate") plist$r <- scale* object$event/object$pyears } if (ci.r) { pname <- c(pname, "ci.r") plist$ci.r <- cipoisson(object$event, object$pyears, p=conf.level) *scale } if (rr) { pname <- c(pname, "rr") plist$rr <- object$event/object$expected } if (ci.rr) { pname <- c(pname, "ci.rr") plist$ci.rr <- cipoisson(object$event, object$expected, p=conf.level) } rname <- c(n = "N", event="Events", pyears= "Time", expected= "Expected events", rate = "Event rate", ci.r = "CI (rate)", rr= "Obs/Exp", ci.rr= "CI (O/E)") rname <- rname[pname] @ If there is only one dimension to the table we can forgo the top legend and use the object names as one of the margins. If \code{vertical=TRUE} the output types are vertical, otherwise they are horizontal. Format each element of the output separately. <>= cname <- names(object$n) #category names if (vertical) { # The person-years objects list across the top, categories up and down # This makes columns line up in a standard "R" way # The first column label is the variable name, content is the categories plist <- lapply(plist, pformat, nastring, ...) # make it character pcol <- sapply(plist, function(x) nchar(x[1])) #width of each one colwidth <- pmax(pcol, nchar(rname)) +2 for (i in 1:length(plist)) plist[[i]] <- strpad(plist[[i]], colwidth[i]) colwidth <- c(max(nchar(vname), nchar(cname)) +2, colwidth) leftcol <- list(strpad(cname, colwidth[1])) header <- strpad(c(vname, rname), colwidth) } else { # in this case each column will have different types of objects in it # alignment is the nuisance newmat <- pybox(plist, length(plist[[1]]), nastring, ...) colwidth <- pmax(nchar(cname), apply(nchar(newmat), 1, max)) +2 # turn the list sideways plist <- split(newmat, row(newmat)) for (i in 1:length(plist)) plist[[i]] <- strpad(plist[[i]], colwidth[i]) colwidth <- c(max(nchar(vname), nchar(rname)) +2, colwidth) leftcol <- list(strpad(rname, colwidth[1])) header <- strpad(c(vname, cname), colwidth) } # Now print it if (vline) { # use a pipe table cat(paste(header, collapse = "|"), "\n") cat(paste(strpad("-", colwidth, "-"), collapse="|"), "\n") temp <- do.call("paste", c(leftcol, plist, list(sep ="|"))) cat(temp, sep= '\n') } else { cat(paste(header, collapse = " "), "\n") cat(paste(strpad("-", colwidth, "-"), collapse=" "), "\n") temp <- do.call("paste", c(leftcol, plist, list(sep =" "))) cat(temp, sep='\n') } @ When there are more than one category in the pyears object then we use a special layout. Each 'cell' of the printed table has all of the values in it. <>= if (header) { # the header is itself a table width <- max(nchar(rname)) if (vline) { cat('+', strpad('-', width, '-'), "+\n", sep="") cat(paste0('|',strpad(rname, width), '|'), sep='\n') cat('+', strpad('-', width, '-'), "+\n\n", sep="") } else { cat(strpad('-', width, '-'), "\n") cat(strpad(rname, width), sep='\n') cat(strpad('-', width, '-'), "\n\n") } } tname <- vname[1:2] #names for the row and col rowname <- dimnames(object$n)[[1]] colname <- dimnames(object$n)[[2]] if (length(dd) > 2) newmat <- pybox(plist, c(dd[1],dd[2], prod(dd[-(1:2)])), nastring, ...) else newmat <- pybox(plist, dd, nastring, ...) if (length(dd) > 2) { newmat <- pybox(plist, c(dd[1],dd[2], prod(dd[-(1:2)])), nastring, ...) outer.label <- do.call("expand.grid", dimnames(object$n)[-(1:2)]) temp <- names(outer.label) for (i in 1:nrow(outer.label)) { # first the caption, then data cat(paste(":", paste(temp, outer.label[i,], sep="=")), '\n') pyshow(newmat[,,i,], tname, rowname, colname, vline) } } else { newmat <- pybox(plist, dd, nastring, ...) pyshow(newmat, tname, rowname, colname, vline) } @ Here are some character manipulation functions. The stringi package has more elegant versions of the pad function, but we don't need the speed. No one is going to print out thousands of lines. <>= strpad <- function(x, width, pad=' ') { # x = the string(s) to be padded out # width = width of desired string. nc <- nchar(x) added <- width - nc left <- pmax(0, floor(added/2)) # can't add negative space right <- pmax(0, width - (nc + left)) # right will be >= left if (all(right <=0)) { if (length(x) >= length(width)) x # nothing needs to be done else rep(x, length=length(width)) } else { # Each pad could be a different length. # Make a long string from which we can take a portion longpad <- paste(rep(pad, max(right)), collapse='') paste0(substring(longpad, 1, left), x, substring(longpad,1, right)) } } pformat <- function(x, nastring, ...) { # This is only called for single index tables, in vertical mode # Any matrix will be a confidence interval if (is.matrix(x)) ret <- paste(ifelse(is.na(x[,1]), nastring, format(x[,1], ...)), "-", ifelse(is.na(x[,2]), nastring, format(x[,2], ...))) else ret <- ifelse(is.na(x), nastring, format(x, ...)) } @ Create formatted boxes. We want all the decimal points to line up, so the format calls are in 3 parts: integer, real, and confidence interval. If there are confidence intervals, format their values and then paste together the left-right ends. The intermediag form \code{final} is a matrix with one column per statistic. At the end, reformat it as an array whose last dimension is the components. <>= pybox <- function(plist, dd, nastring, ...) { ci <- (substring(names(plist), 1,3) == "ci.") # the CI components int <- sapply(plist, function(x) all(x == floor(x) | is.na(x))) int <- (!ci & int) real<- (!ci & !int) nc <- prod(dd) final <- matrix("", nrow=nc, ncol=length(ci)) if (any(int)) { # integers if (any(sapply(plist[int], length) != nc)) stop("programming length error, notify package author") temp <- unlist(plist[int]) final[,int] <- ifelse(is.na(temp), nastring, format(temp)) } if (any(real)) { # floating point if (any(sapply(plist[real], length) != nc)) stop("programming length error, notify package author") temp <- unlist(plist[real]) final[,real] <- ifelse(is.na(temp), nastring, format(temp, ...)) } if (any(ci)) { if (any(sapply(plist[ci], length) != nc*2)) stop("programming length error, notify package author") temp <- unlist(plist[ci]) temp <- array(ifelse(is.na(temp), nastring, format(temp, ...)), dim=c(nc, 2, sum(ci))) final[,ci] <- paste(temp[,1,], temp[,2,], sep='-') } array(final, dim=c(dd, length(ci))) } @ This function prints out a box table. Each cell contains the full set of statistics that were requested. Most of the work is the creation of the appropriate spacing and special characters to create a valid pandoc table. <>= pyshow <- function(dmat, labels, rowname, colname, vline) { # Every column is the same width, except the first colwidth <- c(max(nchar(rowname), nchar(labels[1])), rep(max(nchar(dmat[1,1,]), nchar(colname)), length(colname))) colwidth[2] <- max(colwidth[2], nchar(labels[2])) ncol <- length(colwidth) dd <- dim(dmat) # vector of length 3, third dim is the statistics rline <- ceiling(dd[3]/2) #which line to put the row label on. if (vline) { # use a grid table cat("+", paste(strpad('-', colwidth, pad='-'), collapse='+'), "+\n", sep='') temp <- rep(' ', ncol); temp[2] <- labels[2] cat("|", paste(strpad(temp, colwidth), collapse="|"), "|\n", sep='') cat("|", paste(strpad(c(labels[1], colname), colwidth), collapse="|"), "|\n", sep='') cat("+", paste(strpad('=', colwidth, pad='='), collapse="+"), "+\n", sep='') for (i in 1:dd[1]) { for (j in 1:dd[3]) { #one printout line per stat if (j==rline) temp <- c(rowname[i], dmat[i,,j]) else temp <- c("", dmat[i,,j]) cat("|", paste(strpad(temp, colwidth), collapse='|'), "|\n", sep='') } cat("+", paste(strpad('-', colwidth, '-'), collapse='+'), "+\n", sep='') } } else { # use a multiline table cat(paste(strpad('-', colwidth, '-'), collapse='-'), "\n") temp <- rep(' ', ncol); temp[2] <- labels[2] cat(paste(strpad(temp, colwidth), collapse=" "), "\n") cat(paste(strpad(c(labels[1], colname), colwidth), collapse=" "), "\n") cat(paste(strpad('-', colwidth, pad='-'), collapse=" "), "\n") for (i in 1:dd[1]) { for (j in 1:dd[3]) { #one printout line per stat if (j==rline) temp <- c(rowname[i], dmat[i,,j]) else temp <- c("", dmat[i,,j]) cat(paste(strpad(temp, colwidth), collapse=' '), "\n") } if (i< dd[1]) cat(" \n") #blank line } cat(paste(strpad('-', colwidth, '-'), collapse='-'), "\n") } } @ This function adds a totals row to the data, for either the first or first and second dimensions. The ``n'' component can't be totaled, so we turn that into NA. <>= pytot <- function(x, na=FALSE) { dd <- dim(x) if (length(dd) ==1) { if (na) array(c(x, NA), dim= length(x) +1, dimnames=list(c(dimnames(x)[[1]], "Total"))) else array(c(x, sum(x)), dim= length(x) +1, dimnames=list(c(dimnames(x)[[1]], "Total"))) } else if (length(dd) ==2) { if (na) new <- rbind(cbind(x, NA), NA) else { new <- rbind(x, colSums(x)) new <- cbind(new, rowSums(new)) } array(new, dim=dim(x) + c(1,1), dimnames=list(c(dimnames(x)[[1]], "Total"), c(dimnames(x)[[2]], "Total"))) } else { # The general case index <- 1:length(dd) if (na) sum1 <- sum2 <- sum3 <- NA else { sum1 <- apply(x, index[-1], sum) # row sums sum2 <- apply(x, index[-2], sum) # col sums sum3 <- apply(x, index[-(1:2)], sum) # total sums } # create a new matrix and then fill it in d2 <- dd d2[1:2] <- dd[1:2] +1 dname <- dimnames(x) dname[[1]] <- c(dname[[1]], "Total") dname[[2]] <- c(dname[[2]], "Total") new <- array(x[1], dim=d2, dimnames=dname) # say dim(x) =(5,8,4); we want new[6,-9,] <- sum1; new[-6,9,] <- sum2 # and new[6,9,] <- sum3 # if dim is longer, we need to add more commas commas <- rep(',', length(dd) -2) eval(parse(text=paste("new[1:dd[1], 1:dd[2]", commas, "] <- x"))) eval(parse(text=paste("new[ d2[1],-d2[2]", commas, "] <- sum1"))) eval(parse(text=paste("new[-d2[1], d2[2]", commas, "] <- sum2"))) eval(parse(text=paste("new[ d2[1], d2[2]", commas, "] <- sum3"))) new } } @ \section{Residuals for survival curves} \subsection{R-code} For all the more complex cases, the variance of a survival curve is based on the infinitesimal jackknife: $$ D_i(t) = \frac{\partial S(t)}{\partial w_i} $$ evaluated at the the observed vector of weights. The variance at a given time is then $D'WD'$ where $D$ is a diagonal matrix of the case weights. When there are multiple states $S$ is replaced by the vector $p(t)$, with one element per state, and the formula gets a bit more complex. The predicted curve from a Cox model is the most complex case. Realizing that we need to return the matrix $D$ to the user, in order to compute the variance of derived quantities like the restricted mean time in state, the code has been changed from a primarily internal focus (compute within the survfit routine) to an external one. The underlying C code is very similar to that in survfitkm.c One major difference in the routines is that this code is designed to return values at a fixed set of time points; it is an error if the user does not provide them. This allows the result to be presented as a matrix or array. Computational differences will be discussed later. The method argument is for debugging. For multi-state it uses either C code or the optimized R method. The double call below is because we want residuals to return a simple matrix, but the pseudo function needs to get back a little bit more. \section{Residuals for survival curves} \subsection{R-code} For all the more complex cases, the variance of a survival curve is based on the infinitesimal jackknife: $$ D_i(t) = \frac{\partial S(t)}{\partial w_i} $$ evaluated at the the observed vector of weights. The variance at a given time is then $D'WD'$ where $D$ is a diagonal matrix of the case weights. When there are multiple states $S$ is replaced by the vector $p(t)$, with one element per state, and the formula gets a bit more complex. The predicted curve from a Cox model is the most complex case. Realizing that we need to return the matrix $D$ to the user, in order to compute the variance of derived quantities like the restricted mean time in state, the code has been changed from a primarily internal focus (compute within the survfit routine) to an external one. The underlying C code is very similar to that in survfitkm.c One major difference in the routines is that this code is designed to return values at a fixed set of time points; it is an error if the user does not provide them. This allows the result to be presented as a matrix or array. Computational differences will be discussed later. The method argument is for debugging. For multi-state it uses either C code or the optimized R method. The double call below is because we want residuals to return a simple matrix, but the pseudo function needs to get back a little bit more. <>= # residuals for a survfit object residuals.survfit <- function(object, times, type= "pstate", collapse, weighted=FALSE, method=1, ...){ if (!inherits(object, "survfit")) stop("argument must be a survfit object") if (missing(times)) stop("the times argument is required") # allow a set of alias temp <- c("pstate", "cumhaz", "sojourn", "survival", "chaz", "rmst", "rmts", "auc") type <- match.arg(casefold(type), temp) itemp <- c(1,2,3,1,2,3,3,3)[match(type, temp)] type <- c("pstate", "cumhaz", "auc")[itemp] if (missing(collapse)) fit <- survresid.fit(object, times, type, weighted=weighted, method= method) else fit <- survresid.fit(object, times, type, collapse= collapse, weighted= weighted, method= method) fit$residuals } survresid.fit <- function(object, times, type= "pstate", collapse, weighted=FALSE, method=1) { survfitms <- inherits(object, "survfitms") coxsurv <- inherits(object, "survfitcox") timefix <- (is.null(object$timefix) || object$timefix) start.time <- object$start.time if (is.null(start.time)) start.time <- min(c(0, object$time)) # check input arguments if (missing(times)) stop ("the times argument is required") else { if (!is.numeric(times)) stop("times must be a numeric vector") times <- sort(unique(times)) if (timefix) times <- aeqSurv(Surv(times))[,1] } # get the data <> if (missing(collapse)) collapse <- (!(is.null(id)) && any(duplicated(id))) if (collapse && is.null(id)) stop("collapse argument requires an id or cluster argument in the survfit call") ny <- ncol(newY) if (collapse && any(X != X[1])) { # If the same id shows up in multiple curves, we just can't deal # with it. temp <- unlist(lapply(split(id, X), unique)) if (any(duplicated(temp))) stop("same id appears in multiple curves, cannot collapse") } timelab <- signif(times, 3) # used for dimnames # What type of survival curve? if (!coxsurv) { stype <- Call$stype if (is.null(stype)) stype <- 1 ctype <- Call$ctype if (is.null(ctype)) ctype <- 1 if (!survfitms) { resid <- rsurvpart1(newY, X, casewt, times, type, stype, ctype, object) if (collapse) { resid <- rowsum(resid, id, reorder=FALSE) dimnames(resid) <- list(id= unique(id), times=timelab) curve <- (as.integer(X))[!duplicated(id)] #which curve for each } else { if (length(id) >0) dimnames(resid) <- list(id=id, times=timelab) curve <- as.integer(X) } } else { # multi-state if (!collapse) { if (length(id >0)) d1name <- id else d1name <- NULL cluster <- d1name curve <- as.integer(X) } else { d1name <- unique(id) cluster <- match(id, d1name) curve <- (as.integer(X))[!duplicated(id)] } resid <- rsurvpart2(newY, X, casewt, istate, times, cluster, type, object, method=method, collapse=collapse) if (type == "cumhaz") { ntemp <- colnames(object$cumhaz) if (length(dim(resid)) ==3) dimnames(resid) <- list(id=d1name, times=timelab, cumhaz= ntemp) else dimnames(resid) <- list(id=d1name, cumhaz=ntemp) } else { ntemp <- object$states if (length(dim(resid)) ==3) dimnames(resid) <- list(id=d1name, times=timelab, state= ntemp) else dimnames(resid) <- list(id=d1name, state= ntemp) } } } else stop("coxph survival curves not yet available") if (weighted && any(casewt !=1)) resid <- resid*casewt list(residuals= resid, curve= curve, id= id, idname=idname) } @ The first part of the work is retrieve the data set. This is done in multiple places in the survival code, all essentially the same. If I gave up (like lm) and forced the model frame to be saved this would be easier of course. <>= Call <- object$call # remember the name of the id variable, if present. # but we don't try to parse it: id= mydata$clinic becomes NULL idname <- Call$id if (is.name(idname)) idname <- as.character(idname) else idname <- NULL # I always need the model frame if (coxsurv) { mf <- model.frame(object) if (is.null(object$y)) Y <- model.response(mf) else Y <- object$y } else { formula <- formula(object) # the chunk below is shared with survfit.formula na.action <- getOption("na.action") if (is.character(na.action)) na.action <- get(na.action) # this is a temporary hack <> # end of shared code } xlev <- levels(X) # Deal with ties if (is.null(Call$timefix) || Call$timefix) newY <- aeqSurv(Y) else newY <- Y @ This code has 3 primary sections: single state survival, multi-state survival, and post-Cox survival. A motivating idea in all of them is to avoid an $O(nd)$ calculation that involves the increment to each subject's leverage at each of the $d$ event times. Since $d$ often grows with $n$ this can get very slow. This routine is designed for the case where the number of time points in the output matrix is modest, so we aim for $O(n)$ processes that repeat for each output time. \subsection{Simple survival} The Nelson-Aalen estimate of cumulative hazard is a simple sum \begin{align} H(t) &= H(t-) + h(t) \nonumber \\ \frac{\partial H(t)}{\partial w_i} &= \frac{\partial H(t-)}{\partial w_i} + [dN_i(t) - Y_i(t)h(t)]/r(t) \nonumber \\ &= \sum_{d_j \le t} dN_i(d_j)/r(d_j) - Y_i(d_j)h(d_j)/r(d_j) \label{NAderiv} \end{align} where $H$ the cumulative hazard, $h$ is the increment to the cumulative hazard, $Y_i$ is 1 when a subject is at risk, and $dN_i$ marks an event for the subject. Our basic strategy for the NA estimate is to use a two stage estimate. First, compute three vectors, each with one element per event time. \begin{itemize} \item term1 = $1/r(d_j)$ is the increment to the derivative for any observation with an event at event time $d_j$ \item term2 = $-h(d_j)/r(d_j)$ is the increment for any observation that is at risk at time $d_j$ \item term3 = cumulative sum of term2 \end{itemize} For any given observation $i$ whose follow-up interval is $(s_i, t_i)$, their derivative at time $z$ is the sum of \begin{itemize} \item term3(min($z$, $t_i$)) - term3(min($z$, $s_i$)) \item term1($t_i$) if $t_i \le z$ and observation $i$ is an event \end{itemize} The Fleming-Harrington estimate of survival is \begin{align*} S(t) &= e^{-H(t)} \\ \partial{S(t)}{\partial w_i} &= -S(t)\partial{H(t)}{\partial w_i} \end{align*} So has exactly the same computation, with a multiplication at the end. <>= rsurvpart1 <- function(Y, X, casewt, times, type, stype, ctype, fit) { ntime <- length(times) etime <- (fit$n.event >0) ny <- ncol(Y) event <- (Y[,ny] >0) status <- Y[,ny] # # Create a list whose first element contains the location of # the death times in curve 1, second element the death times for curve 2, # if (is.null(fit$strata)) { fitrow <- list(which(etime)) } else { temp1 <- cumsum(fit$strata) temp2 <- c(1, temp1+1) fitrow <- lapply(1:length(fit$strata), function(i) { indx <- seq(temp2[i], temp1[i]) indx[etime[indx]] # keep the death times }) } ff <- unlist(fitrow) # for each time x, the index of the last death time which is <=x. # 0 if x is before the first death time in the fit object. # The result is an index to the survival curve matchfun <- function(x, fit, index) { dtime <- fit$time[index] # subset to this curve i2 <- findInterval(x, dtime, left.open=FALSE) c(0, index)[i2 +1] } # output matrix D will have one row per observation, one col for each # reporting time. tindex and yindex have the same dimension as D. # tindex points to the last death time in fit which # is <= the reporting time. (If there is only 1 curve, each col of # tindex will be a repeat of the same value.) tindex <- matrix(0L, nrow(Y), length(times)) for (i in 1:length(fitrow)) { yrow <- which(as.integer(X) ==i) temp <- matchfun(times, fit, fitrow[[i]]) tindex[yrow, ] <- rep(temp, each= length(yrow)) } tindex[,] <- match(tindex, c(0,ff)) -1L # the [,] preserves dimensions # repeat the indexing for Y onto fit$time. Each row of yindex points # to the last row of fit with death time <= Y[,ny] ny <- ncol(Y) yindex <- matrix(0L, nrow(Y), length(times)) event <- (Y[,ny] >0) if (ny==3) startindex <- yindex for (i in 1:length(fitrow)) { yrow <- (as.integer(X) ==i) # rows of Y for this curve temp <- matchfun(Y[yrow,ny-1], fit, fitrow[[i]]) yindex[yrow,] <- rep(temp, ncol(yindex)) if (ny==3) { temp <- matchfun(Y[yrow,1], fit, fitrow[[i]]) startindex[yrow,] <- rep(temp, ncol(yindex)) } } yindex[,] <- match(yindex, c(0,ff)) -1L if (ny==3) { startindex[,] <- match(startindex, c(0,ff)) -1L # no subtractions for report times before subject's entry startindex <- pmin(startindex, tindex) } # Now do the work if (type=="cumhaz" || stype==2) { # result based on hazards if (ctype==1) { <> } else { <> } } else { # not hazard based <> } D } @ The Nelson-Aalen is the simplest case. We don't have to worry about case weights of the data, since that has already been accounted for by the survfit function. <>= death <- (yindex <= tindex & rep(event, ntime)) # an event occured at <= t term1 <- 1/fit$n.risk[ff] term2 <- lapply(fitrow, function(i) fit$n.event[i]/fit$n.risk[i]^2) term3 <- unlist(lapply(term2, cumsum)) sum1 <- c(0, term1)[ifelse(death, 1+yindex, 1)] sum2 <- c(0, term3)[1 + pmin(yindex, tindex)] if (ny==3) sum3 <- c(0, term3)[1 + pmin(startindex, tindex)] if (ny==2) D <- matrix(sum1 - sum2, ncol=ntime) else D <- matrix(sum1 + sum3 - sum2, ncol=ntime) # survival is exp(-H) so the derivative is a simple transform of D if (type== "pstate") D <- -D* c(1,fit$surv[ff])[1+ tindex] else if (type == "auc") { <> } @ The sojourn time is the area under the survival curve. Let $x_j$ be the widths of the rectangles under the curve from event time $d_j$ to $min(d_{j+1}, t)$, zero if $t \le d_j$, or $t-d_m$ if $t$ is after the last event time. \begin{align*} A(0,t) &= \sum_{j=1}^m x_j S(d_j) \\ \frac{\partial A(0,t)}{\partial w_i} &= \sum_{j=1}^m -x_j S(d_j) \frac{\partial H(d_j)}{\partial w_i} \\ &= \sum_{j=1}^m -x_jS(d_j) \sum_{k \le j} \frac{\partial h(d_k)}{\partial w_i} \\ &= \sum_{k=1}^m \frac{\partial h(d_k)}{\partial w_i} \left(\sum_{j\ge k} -x_j S(d_j) \right) \\ &= \sum_{k=1}^m -A(d_k, t) \frac{\partial h(d_k)}{\partial w_i} \end{align*} For an observation at risk over the interval $(a,b)$ we have exactly the same calculus as the cumulative hazard with respect to which $h(d_k)$ terms are counted for the observation, but now they are weighted sums. The weights are different for each output time, so we set them up as a matrix. We need the AUC at each event time $d_k$, and the AUC at the output times. Matrix subscripts are a little used feature of R. If y is a matrix of values and x is a 2 colum matrix containing m (row, col) pairs, the result will be a vector of length m that plucks out the [x[1,1], x[1,2]] value of y, then the [x[2,1], x[2,2]] value of y, etc. They are rarely useful, but very handy in the few cases where they apply. <>= auc1 <- lapply(fitrow, function(i) { if (length(i) <=1) 0 else c(0, cumsum(diff(fit$time[i]) * (fit$surv[i])[-length(i)])) }) # AUC at each event time auc2 <- lapply(fitrow, function(i) { if (length(i) <=1) 0 else { xx <- sort(unique(c(fit$time[i], times))) # all the times yy <- (fit$surv[i])[findInterval(xx, fit$time[i])] auc <- cumsum(c(diff(xx),0) * yy) c(0, auc)[match(times, xx)] }}) # AUC at the output times # Most often this function is called with a single curve, so make that case # faster. (Or I presume so: mapply and do.call may be more efficient than # I think for lists of length 1). if (length(fitrow)==1) { # simple case, most common to ask for auc wtmat <- pmin(outer(auc1[[1]], -auc2[[1]], '+'),0) term1 <- term1 * wtmat term2 <- unlist(term2) * wtmat term3 <- apply(term2, 2, cumsum) } else { #more than one curve, compute weighted cumsum per curve wtmat <- mapply(function(x, y) pmin(outer(x, -y, "+"), 0), auc1, auc2) term1 <- term1 * do.call(rbind, wtmat) temp <- mapply(function(x, y) apply(x*y, 2, cumsum), term2, wtmat) term3 <- do.call(rbind, temp) } sum1 <- sum2 <- matrix(0, nrow(yindex), ntime) if (ny ==3) sum3 <- sum1 for (i in 1:ntime) { sum1[,i] <- c(0, term1[,i])[ifelse(death[,i], 1 + yindex[,i], 1)] sum2[,i] <- c(0, term3[,i])[1 + pmin(yindex[,i], tindex[,i])] if (ny==3) sum3[,i] <- c(0, term3[,i])[1 + pmin(startindex[,i], tindex[,i])] } # Perhaps a bit faster(?), but harder to read. And for AUC people usually only # ask for one time point #sum1 <- rbind(0, term1)[cbind(c(ifelse(death, 1+yindex, 1)), c(col(yindex)))] #sum2 <- rbind(0, term3)[cbind(c(1 + pmin(yindex, tindex)), c(col(yindex)))] #if (ny==3) sum3 <- # rbind(0, term3)[c(cbind(1 + pmin(startindex, tindex)), # c(col(yindex)))] if (ny==2) D <- matrix(sum1 - sum2, ncol=ntime) else D <- matrix(sum1 + sum3 - sum2, ncol=ntime) @ \paragraph{Fleming-Harrington} For the Fleming-Harrington estimator the calculation at a tied time differs slightly. If there were 10 at risk and 3 tied events, the Nelson-Aalen has an increment of 3/10, while the FH has an increment of (1/10 + 1/9 + 1/8). The underlying idea is that the true time values are continuous and we observe ties due to coarsening of the data. The derivative will have 3 terms as well. In this case the needed value cannot be pulled directly from the survfit object. Computationally, the number of distinct times at which a tie occurs is normally quite small and the for loop below will not be too expensive. <>= stop("residuals function still imcomplete, for FH estimate") if (any(casewt != casewt[1])) { # Have to reconstruct the number of obs with an event, the curve only # contains the weighted sum nevent <- unlist(lapply(seq(along.with=levels(X)), function(i) { keep <- which(as.numeric(X) ==i) counts <- table(Y[keep, ny-1], status) as.vector(counts[, ncol(counts)]) })) } else nevent <- fit$n.event n2 <- fit$n.risk risk2 <- 1/fit$n.risk ltemp <- risk2^2 for (i in which(nevent>1)) { # assume not too many ties denom <- fit$n.risk[i] - fit$n.event[i]*(0:(nevent[i]-1))/nevent[i] risk2[i] <- mean(1/denom) # multiplier for the event ltemp[i] <- mean(1/denom^2) n2[i] <- mean(denom) } death <- (yindex <= tindex & rep(event, ntime)) term1 <- risk2[ff] term2 <- lapply(fitrow, function(i) event[i]*ltemp[i]) term3 <- unlist(lapply(term2, cumsum)) sum1 <- c(0, term1)[ifelse(death, 1+yindex, 1)] sum2 <- c(0, term3)[1 + pmin(yindex, tindex)] if (ny==3) sum3 <- c(0, term3)[1 + pmin(startindex, tindex)] if (ny==2) D <- matrix(sum1 - sum2, ncol=ntime) else D <- matrix(sum1 + sum3 - sum2, ncol=ntime) if (type=="pstate") D <- -D* c(0,fit$surv[ff])[1+ tindex] else if (type=="auc") { <> } @ \paragraph{Kaplan-Meier} For the Kaplan-Meier (a special case of the Aalen-Johansen) the underlying algorithm is multiplicative, but we can turn it into an additive algoritm with a slight of hand. \begin{align*} S(t) &= \prod_{d_j\le t} (1- h(d_j)) \\ &= \exp \left(\sum_{d_j\le t} \log(1- h(d_j)) \right) \\ &= \exp \left(\sum_{d_j\le t} \log(r(d_j) - dN(d_j)) - log(r(d_j)) \right) \\ \frac{\partial S(t)}{\partial w_i} &= S(t) \sum_{d_j\le t} \frac{Y_i(d_j) - dN_i(d_j)}{r(d_j) - dN(d_j)} - \frac{Y_i(d_j)}{ r(d_j)} \end{align*} The addend for term2 is now $1/n(n-e)$ where $e$ is the number of events, i.e., the same term as in the Greenwood variance, and term1 is $-1/n(n-e)$. The jumps in the KM curve are just a big larger than jumps in a FH estimate, so it makes sense that these are just a bit larger. <>= death <- (yindex <= tindex & rep(event, ntime)) # dtemp avoids 1/0. (When this occurs the influence is 0, since # the curve has dropped to zero; and this avoids Inf in term1 and term2). dtemp <- ifelse(fit$n.risk==fit$n.event, 0, 1/(fit$n.risk- fit$n.event)) term1 <- dtemp[ff] term2 <- lapply(fitrow, function(i) dtemp[i]*fit$n.event[i]/fit$n.risk[i]) term3 <- unlist(lapply(term2, cumsum)) add1 <- c(0, term1)[ifelse(death, 1+yindex, 1)] add2 <- c(0, term3)[1 + pmin(yindex, tindex)] if (ny==3) add3 <- c(0, term3)[1 + pmin(startindex, tindex)] if (ny==2) D <- matrix(add1 - add2, ncol=ntime) else D <- matrix(add1 + add3 - add2, ncol=ntime) # survival is exp(-H) so the derivative is a simple transform of D if (type== "pstate") D <- -D* c(1,fit$surv[ff])[1+ tindex] else if (type == "auc") { <> } @ \subsection{Multi-state Aalen-Johansen estimate} For multi-state models a correction for ties of similar spirit to the Efron approximation in a Cox model (the ctype=2 argument for \code{survfit}) is difficult: the 'right' answer depends on the study. Thus the ctype argument is not present. Both stype 1 and 2 are feasible, but currently only \code{stype=1} is supported. This makes the code somewhat simpler, but this is more than offset by the multi-state nature. With multiple states we also need to account for influence on the starting state $p(0)$. One thing that can make this code slow is data that has been divided into a very large number of intervals, giving a large number of observations for each cluster. We first deal with that by collapsing adjacent observations. <>= rsurvpart2 <- function(Y, X, casewt, istate, times, cluster, type, fit, method, collapse) { ny <- ncol(Y) ntime <- length(times) nstate <- length(fit$states) # ensure that Y, istate, and fit all use the same set of states states <- fit$states if (!identical(attr(Y, "states"), fit$states)) { map <- match(attr(Y, "states"), fit$states) Y[,ny] <- c(0, map)[1+ Y[,ny]] # 0 = censored attr(Y, "states") <- fit$states } if (is.null(istate)) istate <- rep(1L, nrow(Y)) #everyone starts in s0 else { if (is.character(istate)) istate <- factor(istate) if (is.factor(istate)) { if (!identical(levels(istate), fit$states)) { map <- match(levels(istate), fit$states) if (any(is.na(map))) stop ("invalid levels in istate") istate <- map[istate] } } # istate is numeric, we take what we get and hope it is right } # collapse redundant rows in Y, for efficiency # a redundant row is a censored obs in the middle of a chain of times # if the user wants individial obs, however, we would just have to # expand it again if (ny==3 && collapse & any(duplicated(cluster))) { ord <- order(cluster, X, istate, Y[,1]) cfit <- .Call(Ccollapse, Y, X, istate, cluster, casewt, ord -1L) if (nrow(cfit) < .8*length(X)) { # shrinking the data by 20 percent is worth it temp <- Y[ord,] Y <- cbind(temp[cfit[,1], 1], temp[cfit[2], 2:3]) X <- X[cfit[,1]] istate <- istate[cfit[1,]] cluster <- cluster[cfit[1,]] } } # Compute the initial leverage inf0 <- NULL if (is.null(fit$call$p0) && any(istate != istate[1])) { #p0 was not supplied by the user, and the intitial states vary inf0 <- matrix(0., nrow=nrow(Y), ncol=nstate) i0fun <- function(i, fit, inf0) { # reprise algorithm in survfitCI p0 <- fit$p0 t0 <- fit$time[1] if (ny==2) at.zero <- which(as.numeric(X) ==i) else at.zero <- which(as.numeric(X) ==i & (Y[,1] < t0 & Y[,2] >= t0)) for (j in 1:nstate) { inf0[at.zero, j] <- (ifelse(istate[at.zero]==states[j], 1, 0) - p0[j])/sum(casewt[at.zero]) } inf0 } if (is.null(fit$strata)) inf0 <- i0fun(1, fit, inf0) else for (i in 1:length(levels(X))) inf0 <- i0fun(i, fit[i], inf0) # each iteration fills in some rows } p0 <- fit$p0 # needed for method==1, type != cumhaz fit <- survfit0(fit) # package the initial state into the picture start.time <- fit$time[1] # This next block is identical to the one in rsurvpart1, more comments are # there etime <- (rowSums(fit$n.event) >0) event <- (Y[,ny] >0) # # Create a list whose first element contains the location of # the death times in curve 1, second element for curve 2, etc. # if (is.null(fit$strata)) fitrow <- list(which(etime)) else { temp1 <- cumsum(fit$strata) temp2 <- c(1, temp1+1) fitrow <- lapply(1:length(fit$strata), function(i) { indx <- seq(temp2[i], temp1[i]) indx[etime[indx]] # keep the death times }) } ff <- unlist(fitrow) # for each time x, the index of the last death time which is <=x. # 0 if x is before the first death time matchfun <- function(x, fit, index) { dtime <- fit$time[index] # subset to this curve i2 <- findInterval(x, dtime, left.open=FALSE) c(0, index)[i2 +1] } if (type== "cumhaz") { <> } else { <> } # since we may have done a partial collapse (removing redundant rows), the # parent routine can't collapse the data if (collapse & any(duplicated(cluster))) { if (length(dim(D)) ==2) D <- rowsum(D, cluster, reorder=FALSE) else { #rowsums has to be fooled dd <- dim(D) temp <- rowsum(matrix(D, nrow=dd[1]), cluster) D <- array(temp, dim=c(nrow(temp), dd[2:3])) } } D } @ \paragraph{Nelson-Aalen} The multi-state Nelson-Aalen estimate of the cumulative hazard at time $t$ is a vector with one element for each observed transition pair. If there were $k$ states there are potentially $k(k-1)$ transition pairs, though normally only a small number will occur in a given fit. We ignore transitions from state $j$ to state $j$. Let $r(t)$ be the weighted number at risk at time $t$, in each state. When some subject makes a $j:k$ transition, the $j:k$ transition will have an increment of $w_i/r_j(t)$. This is precisely the same increment as the ordinary Nelson estimate. The only change then is that we loop over the set of possible transitions, creating a large output object. <>= # output matrix D will have one row per observation, one col for each # reporting time. tindex and yindex have the same dimension as D. # tindex points to the last death time in fit which # is <= the reporting time. (If there is only 1 curve, each col of # tindex will be a repeat of the same value.) tindex <- matrix(0L, nrow(Y), length(times)) for (i in 1:length(fitrow)) { yrow <- which(as.integer(X) ==i) temp <- matchfun(times, fit, fitrow[[i]]) tindex[yrow, ] <- rep(temp, each= length(yrow)) } tindex[,] <- match(tindex, c(0,ff)) -1L # the [,] preserves dimensions # repeat the indexing for Y onto fit$time. Each row of yindex points # to the last row of fit with death time <= Y[,ny] ny <- ncol(Y) yindex <- matrix(0L, nrow(Y), length(times)) event <- (Y[,ny] >0) if (ny==3) startindex <- yindex for (i in 1:length(fitrow)) { yrow <- (as.integer(X) ==i) # rows of Y for this curve temp <- matchfun(Y[yrow,ny-1], fit, fitrow[[i]]) yindex[yrow,] <- rep(temp, ncol(yindex)) if (ny==3) { temp <- matchfun(Y[yrow,1], fit, fitrow[[i]]) startindex[yrow,] <- rep(temp, ncol(yindex)) } } yindex[,] <- match(yindex, c(0,ff)) -1L if (ny==3) { startindex[,] <- match(startindex, c(0, ff)) -1L # no subtractions for report times before subject's entry startindex <- pmin(startindex, tindex) } dstate <- Y[,ncol(Y)] istate <- as.integer(istate) ntrans <- ncol(fit$cumhaz) # the number of possible transitions D <- array(0, dim=c(nrow(Y), ntime, ntrans)) scount <- table(istate[dstate!=0], dstate[dstate!=0]) # observed transitions state1 <- row(scount)[scount>0] state2 <- col(scount)[scount>0] temp <- paste(rownames(scount)[state1], colnames(scount)[state2], sep='.') if (!identical(temp, colnames(fit$cumhaz))) stop("setup error") for (k in length(state1)) { e2 <- Y[,ny] == state2[k] add1 <- (yindex <= tindex & rep(e2, ntime)) lsum <- unlist(lapply(fitrow, function(i) cumsum(fit$n.event[i,k]/fit$n.risk[i,k]^2))) term1 <- c(0, 1/fit$n.risk[ff,k])[ifelse(add1, 1+yindex, 1)] term2 <- c(0, lsum)[1+pmin(yindex, tindex)] if (ny==3) term3 <- c(0, lsum)[1 + startindex] if (ny==2) D[,,k] <- matrix(term1 - term2, ncol=ntime) else D[,,k] <- matrix(term1 + term3 - term2, ncol=ntime) } @ \paragraph{Aalen-Johansen} The multi-state AJ estimate is more complex. Let $p(t)$ be the vector of probability in state at time $t$. Then \begin{align} p(t) &= p(t-) [I+ A(t)]\nonumber\\ \frac{\partial p(t)}{\partial w_i} &= \frac{\partial p(t-)}{\partial w_i} [I+ A(t)] + p(t-) \frac{\partial A(t)}{\partial w_i} \nonumber\\ &= U_i(t-) [I+ A(t)] + p(t-) \frac{\partial A(t)}{\partial w_i} \label{ajresidx}\\ \end{align} When we expand the left hand portion of \eqref{ajresidx} to include all observations it becomes simple matrix multiplication, not so with the right hand portion. Each individual subject $i$ has a subject-specific nstate * nstate derivative matrix $dA$, which will be non-zero only for the state (row) $j$ that the subject occupies at time $t-$. The $j$th row of $p(t-) dH$ is added to each subject's derivative. The $A$ matrix at time $t$ has off diagonal elements and derivative \begin{align} A(t)_{jk} &= \frac{\sum_i w_i Y_{ij}(t) dN{ik}(t)} {\sum_i w_iY_{ij}(t)} \\ &= \lambda_{jk}(t) \\ \frac{\partial A(t)}{\partial w_i} &= \frac{dN_{ik}(t) - \lambda_{jk}(t)} {\sum_i w_iY_{ij}(t)} \label{Aderiv} \end{align} This is the standard counting process notation: $Y_{ij}(t)$ is 1 if subject $i$ is in state $j$ and at risk at time $t-$, and $dN_{ik}(t)$ is a transition to state $k$ at time $t$. Each observation at risk appears in at most 1 row of $A(t)$, since they can only be in one state. The diagonal element of $A$ are set so that each row sums to 0. If there are no transitions out of state $j$ at some time point, then that row of $A$ is zero. Since the row sums are constant, the sum of the derivatives for each row must be zero. If we evaluate equation \label{ajresidx} directly there will be $O(nk^2)$ operations at each death time for the matrix product, and another $O(nk)$ to add in the new increment. For a large data set $d$ is often of the same order as $n$, which makes this an expensive calculation. But, this is what the C-code version currently does, because I have code that actually works. <>= if (method==1) { # Compute the result using the direct method, in C code # the routine is called separately for each curve, data in sorted order # is1 <- as.integer(istate) -1L # 0 based subscripts for C if (is.null(inf0)) inf0 <- matrix(0, nrow=nrow(Y), ncol=nstate) if (all(as.integer(X) ==1)) { # only one curve if (ny==2) asort1 <- 0L else asort1 <- order(Y[,1], Y[,2]) -1L asort2 <- order(Y[,ny-1]) -1L tfit <- .Call(Csurvfitresid, Y, asort1, asort2, is1, casewt, p0, inf0, times, start.time, type== "auc") if (ntime==1) { if (type=="auc") D <- tfit[[2]] else D <- tfit[[1]] } else { if (type=="auc") D <- array(tfit[[2]], dim=c(nrow(Y), nstate, ntime)) else D <- array(tfit[[1]], dim=c(nrow(Y), nstate, ntime)) } } else { # one curve at a time ix <- as.numeric(X) # 1, 2, etc if (ntime==1) D <- matrix(0, nrow(Y), nstate) else D <- array(0, dim=c(nrow(Y), nstate, ntime)) for (curve in 1:max(ix)) { j <- which(ix==curve) ytemp <- Y[j,,drop=FALSE] if (ny==2) asort1 <- 0L else asort1 <- order(ytemp[,1], ytemp[,2]) -1L asort2 <- order(ytemp[,ny-1]) -1L # call with a subset of the data j <- which(ix== curve) tfit <- .Call(Csurvfitresid, ytemp, asort1, asort2, is1[j], casewt[j], p0[curve,], inf0[j,], times, start.time, type=="auc") if (ntime==1) { if (type=="auc") D[j,] <- tfit[[2]] else D[j,] <- tfit[[1]] } else { if (type=="auc") D[j,,] <- tfit[[2]] else D[j,,] <- tfit[[1]] } } } # the C code makes time the last dimension, we want it to be second if (ntime > 1) D <- aperm(D, c(1,3,2)) } else { # method 2 <> } @ Can we speed this up? An alternate is to look at the direct expansion. \begin{align} p(t) &= p(0) \prod_{d_j \le t} [I+ A(d_j)] \nonumber \\ \frac{\partial p(t)}{\partial w_i} &= \frac{\partial p(0)}{\partial w_i} \prod_{d_j \le t} [I+ A(d_j)] \\ & + p(0)\sum_{d_j \le t} \left( \prod_{kk$. Let $D(x)$ be the diagonal matrix. \begin{align} T_{01} &= D(p'(0))[I+ A(d_1)] & T_{02} &= T_{01}[I + A(d_2)] & T_{03} &= T_{02} [I + A(d_3)] & \ldots \\ T_{11} &= D(p(d_1)) B(d_1) & T_{12} &= T_{11}[I + A(d_2)] & T_{13} &= T_{12}[I + A(d_3)] & \ldots \\ T_{21} &= 0 & T_{22} &= D(p(d_2)) B(d_2) & T_{23} &= T_{22}[I+ A(d_2)] & \ldots \\ T_{31} &= 0 & T_{32}&=0 & T_{33} &= D(p(d_3)) B(d_3) &\ldots \end{align} (According to the latex guide the above should be nicely spaced, but I get equations that are touching. Why?) If $p(0)$ is a fixed value specified by the user then $p'(0)$ =0. Otherwise $p(0)$ is the emprical distribution of the initial states, just before the first death time $d_1$. Let $n_0$ be the (weighted) count of subjects who are at risk at that time. The $j$th row of $p'(0)$ is defined as the deviative wrt $w_i$ for a subject who starts in state $j$. If no one starts in state $j$ that row of the matrix will be 0, otherwise it contains $(1-p_j(0)$ in the $jth$ element and $p_j(0)/n_0$ elsewhere. Define the matrix $W_{jk} = \sum_{l=1}^j T_{lk}$, with $W_{j0}=0$. Then for someone who enters at time $s$ such that $d_a < s \le d_{a+1}$, is censored or has an event at time $t$ such that $d_b \le t >= Yold <- Y utime <- fit$time[fit$time <= max(times) & etime] # unique death times ndeath <- length(utime) # number of unique event times delta <- diff(c(start.time, utime)) # Expand Y if (ny==2) split <- .Call(Csurvsplit, rep(0., nrow(Y)), Y[,1], times) else split <- .Call(Csurvsplit, Y[,1], Y[,2], times) X <- X[split$row] casewt <- casewt[split$row] istate <- istate[split$row] Y <- cbind(split$start, split$end, ifelse(split$censor, 0, Y[split$row,ny])) ny <- 3 # Create a vector containing the index of each end time into the fit object yindex <- ystart <- double(nrow(Y)) for (i in 1:length(fitrow)) { yrow <- (as.integer(X) ==i) # rows of Y for this curve yindex[yrow] <- matchfun(Y[yrow, 2], fit, fitrow[[i]]) ystart[yrow] <- matchfun(Y[yrow, 1], fit, fitrow[[i]]) } # And one indexing the reporting times into fit tindex <- matrix(0L, nrow=length(fitrow), ncol=ntime) for (i in 1:length(fitrow)) { tindex[i,] <- matchfun(times, fit, fitrow[[i]]) } yindex[,] <- match(yindex, c(0,ff)) -1L tindex[,] <- match(tindex, c(0,ff)) -1L ystart[,] <- pmin(match(ystart, c(0,ff)) -1L, tindex) # Create the array of C matrices cmat <- array(0, dim=c(nstate, nstate, ndeath)) # max(i2) = ndeath, by design Hmat <- cmat # We only care about observations that had a transition; any transitions # after the last reporting time are not relevant transition <- (Y[,ny] !=0 & Y[,ny] != istate & Y[,ny-1] <= max(times)) # obs that had a transition i2 <- match(yindex, sort(unique(yindex))) # which C matrix this obs goes to i2 <- i2[transition] from <- as.numeric(istate[transition]) # from this state to <- Y[transition, ny] # to this state nrisk <- fit$n.risk[cbind(yindex[transition], from)] # number at risk wt <- casewt[transition] for (i in seq(along.with =from)) { j <- c(from[i], to[i]) haz <- wt[i]/nrisk[i] cmat[from[i], j, i2[i]] <- cmat[from[i], j, i2[i]] + c(-haz, haz) } for (i in 1:ndeath) Hmat[,,i] <- cmat[,,i] + diag(nstate) # The transformation matrix H(t) at time t is cmat[,,t] + I # Create the set of W and V matrices. # dindex <- which(etime & fit$time <= max(times)) Wmat <- Vmat <- array(0, dim=c(nstate, nstate, ndeath)) for (i in ndeath:1) { j <- match(dindex[i], tindex, nomatch=0) if (j > 0) { # this death matches one of the reporting times Wmat[,,i] <- diag(nstate) Vmat[,,i] <- matrix(0, nstate, nstate) } else { Wmat[,,i] <- Hmat[,,i+1] %*% Wmat[,,i+1] Vmat[,,i] <- delta[i] + Hmat[,,i+1] %*% Wmat[,,i+1] } } @ The above code has created the Wmat array for all reporting times and for all the curves (if more than one). Each of them reaches forward to the next reporting time. Now work forward in time. <>= iterm <- array(0, dim=c(nstate, nstate, ndeath)) # term in equation itemp <- vtemp <- matrix(0, nstate, nstate) # cumulative sum, temporary isum <- isum2 <- iterm # cumulative sum vsum <- vsum2 <- vterm <- iterm for (i in 1:ndeath) { j <- dindex[i] n0 <- ifelse(fit$n.risk[j,] ==0, 1, fit$n.risk[j,]) # avoid 0/0 iterm[,,i] <- ((fit$pstate[j-1,]/n0) * cmat[,,i]) %*% Wmat[,,i] vterm[,,i] <- ((fit$pstate[j-1,]/n0) * cmat[,,i]) %*% Vmat[,,i] itemp <- itemp + iterm[,,i] vtemp <- vtemp + vterm[,,i] isum[,,i] <- itemp vsum[,,i] <- vtemp j <- match(dindex[i], tindex, nomatch=0) if (j>0) itemp <- vtemp <- matrix(0, nstate, nstate) # reset isum2[,,i] <- itemp vsum2[,,i] <- vtemp } # We want to add isum[state,, entry time] - isum[state,, exit time] for # each subject, and for those with an a:b transition there will be an # additional vector with -1, 1 in the a and b position. i1 <- match(ystart, sort(unique(yindex)), nomatch=0) # start at 0 gives 0 i2 <- match(yindex, sort(unique(yindex))) D <- matrix(0., nrow(Y), nstate) keep <- (Y[,2] <= max(times)) # any intervals after the last reporting time # will have 0 influence for (i in which(keep)) { if (Y[i,3] !=0 && istate[i] != Y[i,3]) { z <- fit$pstate[yindex[i]-1, istate[i]]/fit$n.risk[yindex[i], istate[i]] temp <- double(nstate) temp[istate[i]] = -z temp[Y[i,3]] = z temp <- temp %*% Wmat[,,i2[i]] - isum[istate[i],,i2[i]] if (i1[i] >0) temp <- temp + isum2[istate[i],, i1[i]] D[i,] <- temp } else { if (i1[i] >0) D[i,] = isum2[istate[i],,i1[i]] - isum[istate[i],, i2[i]] else D[i,] = -isum[istate[i],, i2[i]] } } @ By design, each row of $Y$, and hence each row of $D$, corresponds to a unique curve, and also to a unique period in the reporting intervals. (Any Y intervals after the last reporting time will have D=0 for the row.) If there are multiple reporting intervals, create an array with one n by nstate slice for each. If a row lies in the first interval, $D$ currently contains its influence on that interval. It's influence on the second interval is the vector times $\prod H(d_k)$ where $k$ is the set of event times $>$ the first reporting time and $\le$ the second one. <>= Dsave <- D if (!is.null(inf0)) { # add in the initial influence, to the first row of each obs # (inf0 was created on unsplit data) j <- which(!duplicated(split$row)) D[j,] <- D[j,] + (inf0%*% Hmat[,,1] %*% Wmat[,,1]) } if (ntime > 1) { interval <- findInterval(yindex, tindex, left.open=TRUE) D2 <- array(0., dim=c(dim(D), ntime)) D2[interval==0,,1] <- D[interval==0,] for (i in 1:(ntime-1)) { D2[interval==i,,i+1] = D[interval==i,] j <- tindex[i] D2[,,i+1] = D2[,,i+1] + D2[,,i] %*% (Hmat[,,j] %*% Wmat[,,j]) } D <- D2 } # undo any artificial split if (any(duplicated(split$row))) { if (ntime==1) D <- rowsum(D, split$row) else { # rowsums has to be fooled temp <- rowsum(matrix(D, ncol=(nstate*ntime)), split$row) # then undo it D <- array(temp, dim=c(nrow(temp), nstate, ntime)) } } @ \section{Accelerated Failure Time models} The [[surveg]] function fits parametric failure time models. This includes accerated failure time models, the Weibull, log-normal, and log-logistic models. It also fits as well as censored linear regression; with left censoring this is referred to in economics \emph{Tobit} regression. \subsection{Residuals} The residuals for a [[survreg]] model are one of several types \begin{description} \item[response] residual [[y]] value on the scale of the original data \item[deviance] an approximate deviance residual. A very bad idea statistically, retained for the sake of backwards compatability. \item[dfbeta] a matrix with one row per observation and one column per parameter showing the approximate influence of each observation on the final parameter value \item[dfbetas] the dfbeta residuals scaled by the standard error of each coefficient \item[working] residuals on the scale of the linear predictor \item[ldcase] likelihood displacement wrt case weights \item[ldresp] likelihood displacement wrt response changes \item[ldshape] likelihood displacement wrt changes in shape \item[matrix] matrix of derivatives of the log-likelihood wrt paramters \end{description} The other parameters are \begin{description} \item[rsigma] whether the scale parameters should be included in the result for dfbeta results. I can think of no reason why one would not want them --- unless of course the scale was fixed by the user, in which case there is no parameter. \item[collapse] optional vector of subject identifiers. This is for the case where a subject has multiple observations in a data set, and one wants to have residuals per subject rather than residuals per observation. \item[weighted] whether the residuals should be multiplied by the case weights. The sum of weighted residuals will be zero. \end{description} The routine starts with standard stuff, checking arguments for validity and etc. The two cases of response or working residuals require a lot less computation. and are the most common calls, so they are taken care of first. <>= # # Residuals for survreg objects residuals.survreg <- function(object, type=c('response', 'deviance', 'dfbeta', 'dfbetas', 'working', 'ldcase', 'ldresp', 'ldshape', 'matrix'), rsigma =TRUE, collapse=FALSE, weighted=FALSE, ...) { type <-match.arg(type) n <- length(object$linear.predictors) Terms <- object$terms if(!inherits(Terms, "terms")) stop("invalid terms component of object") # If the variance wasn't estimated then it has no error if (nrow(object$var) == length(object$coefficients)) rsigma <- FALSE # If there was a cluster directive in the model statment then remove # it. It does not correspond to a coefficient, and would just confuse # things later in the code. cluster <- untangle.specials(Terms,"cluster")$terms if (length(cluster) >0 ) Terms <- Terms[-cluster] strata <- attr(Terms, 'specials')$strata intercept <- attr(Terms, "intercept") response <- attr(Terms, "response") weights <- object$weights if (is.null(weights)) weighted <- FALSE <> <> <> <> } @ First retrieve the distribution, which is used multiple times. The common case is a character string pointing to some element of [[survreg.distributions]], but the other is a user supplied list of the form contained there. Some distributions are defined as the transform of another in which case we need to set [[itrans]] and [[dtrans]] and follow the link, otherwise the transformation and its inverse are the identity. <>= if (is.character(object$dist)) dd <- survreg.distributions[[object$dist]] else dd <- object$dist ytype <- attr(y, "type") if (is.null(dd$itrans)) { itrans <- dtrans <-function(x)x # reprise the work done in survreg to create a transformed y if (ytype=='left') y[,2] <- 2- y[,2] else if (type=='interval' && all(y[,3]<3)) y <- y[,c(1,3)] } else { itrans <- dd$itrans dtrans <- dd$dtrans # reprise the work done in survreg to create a transformed y tranfun <- dd$trans exactsurv <- y[,ncol(y)] ==1 if (any(exactsurv)) logcorrect <-sum(log(dd$dtrans(y[exactsurv,1]))) if (ytype=='interval') { if (any(y[,3]==3)) y <- cbind(tranfun(y[,1:2]), y[,3]) else y <- cbind(tranfun(y[,1]), y[,3]) } else if (ytype=='left') y <- cbind(tranfun(y[,1]), 2-y[,2]) else y <- cbind(tranfun(y[,1]), y[,2]) } if (!is.null(dd$dist)) dd <- survreg.distributions[[dd$dist]] deviance <- dd$deviance dens <- dd$density @ The next task is to decide what data we need. The response is always needed, but is normally saved as a part of the model. If it is a transformed distribution such as the Weibull (a transform of the extreme value) the saved object [[y]] is the transformed data, so we need to replicate that part of the survreg() code. (Why did I even allow for y=F in survreg? Because I was mimicing the lm function --- oh the long, long consequences of a design decision.) The covariate matrix [[x]] will be needed for all but response, deviance, and working residuals. If the model included a strata() term then there will be multiple scales, and the strata variable needs to be recovered. The variable [[sigma]] is set to a scalar if there are no strata, but otherwise to a vector with [[n]] elements containing the appropriate scale for each subject. The leverage type residuals all need the second derivative matrix. If there was a [[cluster]] statement in the model this will be found in [[naive.var]], otherwise in the [[var]] component. <>= if (is.null(object$naive.var)) vv <- object$var else vv <- object$naive.var need.x <- is.na(match(type, c('response', 'deviance', 'working'))) if (is.null(object$y) || !is.null(strata) || (need.x & is.null(object[['x']]))) mf <- stats::model.frame(object) if (is.null(object$y)) y <- model.response(mf) else y <- object$y if (!is.null(strata)) { temp <- untangle.specials(Terms, 'strata', 1) Terms2 <- Terms[-temp$terms] if (length(temp$vars)==1) strata.keep <- mf[[temp$vars]] else strata.keep <- strata(mf[,temp$vars], shortlabel=TRUE) strata <- as.numeric(strata.keep) nstrata <- max(strata) sigma <- object$scale[strata] } else { Terms2 <- Terms nstrata <- 1 sigma <- object$scale } if (need.x) { x <- object[['x']] #don't grab xlevels component if (is.null(x)) x <- model.matrix(Terms2, mf, contrasts.arg=object$contrasts) } @ The most common residual is type response, which requires almost no more work, for the others we need to create the matrix of derivatives before proceeding. We use the [[center]] component from the deviance function for the distribution, which returns the data point [[y]] itself for an exact, left, or right censored observation, and an appropriate midpoint for interval censored ones. <>= if (type=='response') { yhat0 <- deviance(y, sigma, object$parms) rr <- itrans(yhat0$center) - itrans(object$linear.predictor) } else { <> <> } @ The matrix of derviatives is used in all of the other cases. The starting point is the [[density]] function of the distribtion which return a matrix with columns of $F(x)$, $1-F(x)$, $f(x)$, $f'(x)/f(x)$ and $f''(x)/f(x)$. %' The matrix type residual contains columns for each of $$ L_i \quad \frac{\partial L_i}{\partial \eta_i} \quad \frac{\partial^2 L_i}{\partial \eta_i^2} \quad \frac{\partial L_i}{\partial \log(\sigma)} \quad \frac{\partial L_i}{\partial \log(\sigma)^2} \quad \frac{\partial^2 L_i}{\partial \eta \partial\log(\sigma)} $$ where $L_i$ is the contribution to the log-likelihood from each individual. Note that if there are multiple scales, i.e. a strata() term in the model, then terms 3--6 are the derivatives for that subject with respect to their \emph{particular} scale factor; derivatives with respect to all the other scales are zero for that subject. The log-likelihood can be written as \begin{align*} L &= \sum_{exact}\left[ \log(f(z_i)) -\log(\sigma_i) \right] + \sum_{censored} \log \left( \int_{z_i^l}^{z_i^u} f(u)du \right) \\ &\equiv \sum_{exact}\left[g_1(z_i) -\log(\sigma_i) \right] + \sum_{censored} \log(g_2(z_i^l, z_i^u)) \\ z_i &= (y_i - \eta_i)/ \sigma_i \end{align*} For the interval censored observations we have a $z$ defined at both the lower and upper endpoints. The linear predictor is $\eta = X\beta$. The derivatives are shown below. Note that $f(-\infty) = f(\infty) = F(-\infty)=0$, $F(\infty)=1$, $z^u = \infty$ for a right censored observation and $z^l = -\infty$ for a left censored one. \begin{align*} \frac{\partial g_1}{\partial \eta} &= - \frac{1}{\sigma} \left[\frac{f'(z)}{f(z)} \right] \\ %' \frac{\partial g_2}{\partial \eta} &= - \frac{1}{\sigma} \left[ \frac{f(z^u) - f(z^l)}{F(z^u) - F(z^l)} \right] \\ \frac{\partial^2 g_1}{\partial \eta^2} &= \frac{1}{\sigma^2} \left[ \frac{f''(z)}{f(z)} \right] - (\partial g_1 / \partial \eta)^2 \\ \frac{\partial^2 g_2}{\partial \eta^2} &= \frac{1}{\sigma^2} \left[ \frac{f'(z^u) - f'(z^l)}{F(z^u) - F(z^l)} \right] - (\partial g_2 / \partial \eta)^2 \\ \frac{\partial g_1}{\partial \log\sigma} && - \left[ \frac{zf'(z)}{f(z)} \right] \\ \frac{\partial g_2}{\partial \log\sigma} &= - \left[ \frac{z^uf(z^u) - z^lf(z^l)}{F(z^u) - F(z^l)} \right] \\ \frac{\partial^2 g_1}{\partial (\log\sigma)^2} &=& \left[ \frac{z^2 f''(z) + zf'(z)}{f(z)} \right] - (\partial g_1 / \partial \log\sigma)^2 \\ \frac{\partial^2 g_2}{\partial (\log\sigma)^2} &= \left[ \frac{(z^u)^2 f'(z^u) - (z^l)^2f'(z_l) } {F(z^u) - F(z^l)} \right] - \partial g_1 /\partial \log\sigma(1+\partial g_1 / \partial \log\sigma) \\ \frac{\partial^2 g_1}{\partial \eta \partial \log\sigma} &= \frac{zf''(z)}{\sigma f(z)} -\partial g_1/\partial \eta (1 + \partial g_1/\partial \log\sigma) \\ \frac{\partial^2 g_2}{\partial \eta \partial \log\sigma} &= \frac{z^uf'(z^u) - z^lf'(z^l)}{\sigma [F(z^u) - F(z^l)]} -\partial g_2/\partial \eta (1 + \partial g_2/\partial \log\sigma) \\ \end{align*} In the code [[z]] is the relevant point for exact, left, or right censored data, and [[z2]] the upper endpoint for an interval censored one. The variable [[tdenom]] contains the denominator for each subject (which is the same for all derivatives for that subject). For an interval censored observation we try to avoid numeric cancellation by using the appropriate tail of the distribution. For instance with $(z^l, z^u) = (12,15)$ the value of $F(x)$ will be very near 1 and it is better to subtract two upper tail values $(1-F)$ than two lower tail ones $F$. <>= status <- y[,ncol(y)] eta <- object$linear.predictors z <- (y[,1] - eta)/sigma dmat <- dens(z, object$parms) dtemp<- dmat[,3] * dmat[,4] #f' if (any(status==3)) { z2 <- (y[,2] - eta)/sigma dmat2 <- dens(z2, object$parms) } else { dmat2 <- dmat #dummy values z2 <- 0 } tdenom <- ((status==0) * dmat[,2]) + #right censored ((status==1) * 1 ) + #exact ((status==2) * dmat[,1]) + #left ((status==3) * ifelse(z>0, dmat[,2]-dmat2[,2], dmat2[,1] - dmat[,1])) #interval g <- log(ifelse(status==1, dmat[,3]/sigma, tdenom)) #loglik tdenom <- 1/tdenom dg <- -(tdenom/sigma) *(((status==0) * (0-dmat[,3])) + #dg/ eta ((status==1) * dmat[,4]) + ((status==2) * dmat[,3]) + ((status==3) * (dmat2[,3]- dmat[,3]))) ddg <- (tdenom/sigma^2) *(((status==0) * (0- dtemp)) + #ddg/eta^2 ((status==1) * dmat[,5]) + ((status==2) * dtemp) + ((status==3) * (dmat2[,3]*dmat2[,4] - dtemp))) ds <- ifelse(status<3, dg * sigma * z, tdenom*(z2*dmat2[,3] - z*dmat[,3])) dds <- ifelse(status<3, ddg* (sigma*z)^2, tdenom*(z2*z2*dmat2[,3]*dmat2[,4] - z * z*dmat[,3] * dmat[,4])) dsg <- ifelse(status<3, ddg* sigma*z, tdenom *(z2*dmat2[,3]*dmat2[,4] - z*dtemp)) deriv <- cbind(g, dg, ddg=ddg- dg^2, ds = ifelse(status==1, ds-1, ds), dds=dds - ds*(1+ds), dsg=dsg - dg*(1+ds)) @ Now, we can calcultate the actual residuals case by case. For the dfbetas there will be one column per coefficient, so if there are strata column 4 of the deriv matrix needs to be \emph{un}collapsed into a matrix with nstrata columns. The same manipulation is needed for the ld residuals. <>= if (type=='deviance') { yhat0 <- deviance(y, sigma, object$parms) rr <- (-1)*deriv[,2]/deriv[,3] #working residuals rr <- sign(rr)* sqrt(2*(yhat0$loglik - deriv[,1])) } else if (type=='working') rr <- (-1)*deriv[,2]/deriv[,3] else if (type=='dfbeta' || type== 'dfbetas' || type=='ldcase') { score <- deriv[,2] * x # score residuals if (rsigma) { if (nstrata > 1) { d4 <- matrix(0., nrow=n, ncol=nstrata) d4[cbind(1:n, strata)] <- deriv[,4] score <- cbind(score, d4) } else score <- cbind(score, deriv[,4]) } rr <- score %*% vv # cause column names to be retained # old: if (type=='dfbetas') rr[] <- rr %*% diag(1/sqrt(diag(vv))) if (type=='dfbetas') rr <- rr * rep(1/sqrt(diag(vv)), each=nrow(rr)) if (type=='ldcase') rr<- rowSums(rr*score) } else if (type=='ldresp') { rscore <- deriv[,3] * (x * sigma) if (rsigma) { if (nstrata >1) { d6 <- matrix(0., nrow=n, ncol=nstrata) d6[cbind(1:n, strata)] <- deriv[,6]*sigma rscore <- cbind(rscore, d6) } else rscore <- cbind(rscore, deriv[,6] * sigma) } temp <- rscore %*% vv rr <- rowSums(rscore * temp) } else if (type=='ldshape') { sscore <- deriv[,6] *x if (rsigma) { if (nstrata >1) { d5 <- matrix(0., nrow=n, ncol=nstrata) d5[cbind(1:n, strata)] <- deriv[,5] sscore <- cbind(sscore, d5) } else sscore <- cbind(sscore, deriv[,5]) } temp <- sscore %*% vv rr <- rowSums(sscore * temp) } else { #type = matrix rr <- deriv } @ Finally the two optional steps of adding case weights and collapsing over subject id. <>= #case weights if (weighted) rr <- rr * weights #Expand out the missing values in the result if (!is.null(object$na.action)) { rr <- naresid(object$na.action, rr) if (is.matrix(rr)) n <- nrow(rr) else n <- length(rr) } # Collapse if desired if (!missing(collapse)) { if (length(collapse) !=n) stop("Wrong length for 'collapse'") rr <- drop(rowsum(rr, collapse)) } rr @ \section{Survival curves} The survfit function was set up as a method so that we could apply the function to both formulas (to compute the Kaplan-Meier) and to coxph objects. The downside to this is that the manual pages get a little odd, but from a programming perspective it was a good idea. At one time, long long ago, we allowed the function to be called with ``Surv(time, status)'' as the formula, i.e., without a tilde. That was a bad idea, now abandoned. A note on times: one of the things that drove me nuts was the problem of ``tied but not quite tied'' times. As an example consider two values of 24173 = 23805 + 368. These are values from an actual study with times in days. However, the user chose to use age in years, and saved those values out in a CSV file, resulting in values for the above of 66.18206708000000 and 66.18206708000001. The R phrase \code{unique(x)} sees these two values as distinct but \code{table(x)} and \code{tapply} see it as a single value since they first apply \code{factor} to the values, and that in turn uses \code{as.character}. A transition through CSV is not necessary to create the problem: <>= tfun <- function(start, gap) { as.numeric(start)/365.25 - as.numeric(start + gap)/365.25 } test <- logical(200) for (i in 1:200) { test[i] <- tfun(as.Date("2010/01/01"), 29) == tfun(as.Date("2010/01/01") + i, 29) } table(test) @ The number of FALSE entries in the table depends on machine, compiler, and a host of other issues. There is discussion of this general issue in the R FAQ: ``why doesn't R think these numbers are equal''. The Kaplan-Meier and Cox model both pay careful attention to ties, and so both now use the \code{aeqSurv} routine to first preprocess the time data. It uses the same rules as \code{all.equal} to adjudicate ties and near ties. <>= survfit <- function(formula, ...) { UseMethod("survfit") } <> <> <> @ The result of a survival curve will have a \code{surv} or \code{pstate} component that is a vector or a matrix, and an optional strata component. From a user's point of view this is an object with [strata, newdata, state] as dimensions, where only 1, 2 or all three of these may appear. The first is always present, and is essentially the number of distinct curves created by the right-hand side of the equation (or by the strata in a coxph model). The newdata portion appears for survival curves from a Cox model, when curves for multiple covariate patterns were requested; the state portion only from a multi-state model; or both for a multi-state Cox model. The \code{surv} component contains the time points for the first stratum, the second, third, etc stacked one above the other. As with R matrices, if only 1 subscript is given for an array or matrix of curves, we treat the collection of curves as a vector of curves. We need to make sure that the new object has the same order of elements as the old -- users count on this. <>= dim.survfit <- function(x) { d1name <- "strata" d2name <- "data" d3name <- "states" if (is.null(x$strata)) {d1 <- d1name <- NULL} else d1 <- length(x$strata) if (is.null(x$newdata)) {d2 <- d2name <- NULL} else d2 <- nrow(x$newdata) if (is.null(x$states)) {d3 <- d3name <- NULL} else d3 <- length(x$states) if (inherits(x, "survfitcox") && is.null(d2) && is.null(d3) && is.matrix(x$surv)) { # older style survfit.coxph object, before I added newdata to the output d2name <- "data" d2 <- ncol(x$surv) } dd <- c(d1, d2, d3) names(dd) <- c(d1name, d2name, d3name) dd } # there is a separate function for survfitms objects "[.survfit" <- function(x, ... , drop=TRUE) { nmatch <- function(indx, target) { # This function lets R worry about character, negative, or # logical subscripts. # It always returns a set of positive integer indices temp <- 1:length(target) names(temp) <- target temp[indx] } if (!inherits(x, "survfit")) stop("[.survfit called on non-survfit object") ndots <- ...length() # the simplest, but not avail in R 3.4 # ndots <- length(list(...))# fails if any are missing, e.g. fit[,2] # ndots <- if (missing(drop)) nargs()-1 else nargs()-2 # a workaround dd <- dim(x) # for dd=NULL, an object with only one curve, x[1] is always legal if (is.null(dd)) dd <- c(strata=1L) # survfit object with only one curve dtype <- match(names(dd), c("strata", "data", "states")) if (ndots >0 && !missing(..1)) i <- ..1 else i <- NULL if (ndots> 1 && !missing(..2)) j <- ..2 else j <- NULL if (ndots > length(dd)) stop("incorrect number of dimensions") if (length(dtype) > 2) stop("invalid survfit object") # should never happen if (is.null(i) && is.null(j)) { # called with no subscripts given -- return x untouched return(x) } # Code below is easier if "i" is always the strata if (dtype[1] !=1) { dtype <- c(1, dtype) j <- i; i <- NULL dd <- c(1, dd) ndots <- ndots +1 } # We need to make a new one newx <- vector("list", length(x)) names(newx) <- names(x) for (k in c("logse", "version", "conf.int", "conf.type", "type", "call")) if (!is.null(x[[k]])) newx[[k]] <- x[[k]] class(newx) <- class(x) if (ndots== 1 && length(dd)==2) { # one subscript given for a two dimensional object # If one of the dimensions is 1, it is easier for me to fill in i and j if (dd[1]==1) {j <- i; i<- 1} else if (dd[2]==1) j <- 1 else { # the user has a mix of rows/cols index <- 1:prod(dd) itemp <- matrix(index, nrow=dd[1]) keep <- itemp[i] # illegal subscripts will generate an error if (length(keep) == length(index) && all(keep==index)) return(x) ii <- row(itemp)[keep] jj <- col(itemp)[keep] # at this point we have a matrix subscript of (ii, jj) # expand into a long pair of rows and cols temp <- split(seq(along.with=x$time), rep(1:length(x$strata), x$strata)) indx1 <- unlist(temp[ii]) # rows of the surv object indx2 <- rep(jj, x$strata[ii]) # return with each curve as a separate strata newx$n <- x$n[ii] for (k in c("time", "n.risk", "n.event", "n.censor", "n.enter")) if (!is.null(x[[k]])) newx[[k]] <- (x[[k]])[indx1] k <- cbind(indx1, indx2) for (j in c("surv", "std.err", "upper", "lower", "cumhaz", "std.chaz", "influence.surv", "influence.chaz")) if (!is.null(x[[j]])) newx[[j]] <- (x[[j]])[k] temp <- x$strata[ii] names(temp) <- 1:length(ii) newx$strata <- temp return(newx) } } # irow will be the rows that need to be taken # j the columns (of present) if (is.null(x$strata)) { if (is.null(i) || all(i==1)) irow <- seq(along.with=x$time) else stop("subscript out of bounds") newx$n <- x$n } else { if (is.null(i)) indx <- seq(along.with= x$strata) else indx <- nmatch(i, names(x$strata)) #strata to keep if (any(is.na(indx))) stop(paste("strata", paste(i[is.na(indx)], collapse=' '), 'not matched')) # Now, indx may not be in order: some can use curve[3:2] to reorder # The list/unlist construct will reorder the data temp <- split(seq(along.with =x$time), rep(1:length(x$strata), x$strata)) irow <- unlist(temp[indx]) if (length(indx) <=1 && drop) newx$strata <- NULL else newx$strata <- x$strata[i] newx$n <- x$n[indx] if (length(indx) ==1 & drop) x$strata <- NULL else newx$strata <- x$strata[indx] } if (length(dd)==1) { # no j dimension for (k in c("time", "n.risk", "n.event", "n.censor", "n.enter", "surv", "std.err", "cumhaz", "std.chaz", "upper", "lower", "influence.surv", "influence.chaz")) if (!is.null(x[[k]])) newx[[k]] <- (x[[k]])[irow] } else { # 2 dimensional object if (is.null(j)) j <- seq.int(ncol(x$surv)) # If the curve has been selected by strata and keep has only # one row, we don't want to lose the second subscript too if (length(irow)==1) drop <- FALSE for (k in c("time", "n.risk", "n.event", "n.censor", "n.enter")) if (!is.null(x[[k]])) newx[[k]] <- (x[[k]])[irow] for (k in c("surv", "std.err", "cumhaz", "std.chaz", "upper", "lower", "influence.surv", "influence.chaz")) if (!is.null(x[[k]])) newx[[k]] <- (x[[k]])[irow, j, drop=drop] } newx } @ \subsection{Kaplan-Meier} The most common use of the survfit function is with a formula as the first argument, and the most common outcome of such a call is a Kaplan-Meier curve. The id argument is from an older version of the competing risks code; most people will use [[cluster(id)]] in the formula instead. The istate argument only applies to competing risks, but don't print an error message if it is accidentally there. <>= survfit.formula <- function(formula, data, weights, subset, na.action, stype=1, ctype=1, id, cluster, robust, istate, timefix=TRUE, etype, error, ...) { Call <- match.call() Call[[1]] <- as.name('survfit') #make nicer printout for the user <> # Deal with the near-ties problem if (!is.logical(timefix) || length(timefix) > 1) stop("invalid value for timefix option") if (timefix) newY <- aeqSurv(Y) else newY <- Y if (missing(robust)) robust <- NULL # Call the appropriate helper function if (attr(Y, 'type') == 'left' || attr(Y, 'type') == 'interval') temp <- survfitTurnbull(X, newY, casewt, ...) else if (attr(Y, 'type') == "right" || attr(Y, 'type')== "counting") temp <- survfitKM(X, newY, casewt, stype=stype, ctype=ctype, id=id, cluster=cluster, robust=robust, ...) else if (attr(Y, 'type') == "mright" || attr(Y, "type")== "mcounting") temp <- survfitCI(X, newY, weights=casewt, stype=stype, ctype=ctype, id=id, cluster=cluster, robust=robust, istate=istate, ...) else { # This should never happen stop("unrecognized survival type") } # If a stratum had no one beyond start.time, the length 0 gives downstream # failure, e.g., there is no sensible printout for summary(fit, time= 100) # for such a curve temp$strata <- temp$strata[temp$strata >0] if (is.null(temp$states)) class(temp) <- 'survfit' else class(temp) <- c("survfitms", "survfit") if (!is.null(attr(mf, 'na.action'))) temp$na.action <- attr(mf, 'na.action') temp$call <- Call temp } @ This chunk of code is shared with resid.survfit <>= # create a copy of the call that has only the arguments we want, # and use it to call model.frame() indx <- match(c('formula', 'data', 'weights', 'subset','na.action', 'istate', 'id', 'cluster', "etype"), names(Call), nomatch=0) #It's very hard to get the next error message other than malice # eg survfit(wt=Surv(time, status) ~1) if (indx[1]==0) stop("a formula argument is required") temp <- Call[c(1, indx)] temp[[1L]] <- quote(stats::model.frame) mf <- eval.parent(temp) Terms <- terms(formula, c("strata", "cluster")) ord <- attr(Terms, 'order') if (length(ord) & any(ord !=1)) stop("Interaction terms are not valid for this function") n <- nrow(mf) Y <- model.response(mf) if (inherits(Y, "Surv2")) { # this is Surv2 style data # if there are any obs removed due to missing, remake the model frame if (length(attr(mf, "na.action"))) { temp$na.action <- na.pass mf <- eval.parent(temp) } if (!is.null(attr(Terms, "specials")$cluster)) stop("cluster() cannot appear in the model statement") new <- surv2data(mf) mf <- new$mf istate <- new$istate id <- new$id Y <- new$y if (anyNA(mf[-1])) { #ignore the response variable still found there if (missing(na.action)) temp <- get(getOption("na.action"))(mf[-1]) else temp <- na.action(mf[-1]) omit <- attr(temp, "na.action") mf <- mf[-omit,] Y <- Y[-omit] id <- id[-omit] istate <- istate[-omit] } n <- nrow(mf) } else { if (!is.Surv(Y)) stop("Response must be a survival object") id <- model.extract(mf, "id") istate <- model.extract(mf, "istate") } if (n==0) stop("data set has no non-missing observations") casewt <- model.extract(mf, "weights") if (is.null(casewt)) casewt <- rep(1.0, n) else { if (!is.numeric(casewt)) stop("weights must be numeric") if (any(!is.finite(casewt))) stop("weights must be finite") if (any(casewt <0)) stop("weights must be non-negative") casewt <- as.numeric(casewt) # transform integer to numeric } if (!is.null(attr(Terms, 'offset'))) warning("Offset term ignored") cluster <- model.extract(mf, "cluster") temp <- untangle.specials(Terms, "cluster") if (length(temp$vars)>0) { if (length(cluster) >0) stop("cluster appears as both an argument and a model term") if (length(temp$vars) > 1) stop("can not have two cluster terms") cluster <- mf[[temp$vars]] Terms <- Terms[-temp$terms] } ll <- attr(Terms, 'term.labels') if (length(ll) == 0) X <- factor(rep(1,n)) # ~1 on the right else X <- strata(mf[ll]) # Backwards support for the now-depreciated etype argument etype <- model.extract(mf, "etype") if (!is.null(etype)) { if (attr(Y, "type") == "mcounting" || attr(Y, "type") == "mright") stop("cannot use both the etype argument and mstate survival type") if (length(istate)) stop("cannot use both the etype and istate arguments") status <- Y[,ncol(Y)] etype <- as.factor(etype) temp <- table(etype, status==0) if (all(rowSums(temp==0) ==1)) { # The user had a unique level of etype for the censors newlev <- levels(etype)[order(-temp[,2])] #censors first } else newlev <- c(" ", levels(etype)[temp[,1] >0]) status <- factor(ifelse(status==0,0, as.numeric(etype)), labels=newlev) if (attr(Y, 'type') == "right") Y <- Surv(Y[,1], status, type="mstate") else if (attr(Y, "type") == "counting") Y <- Surv(Y[,1], Y[,2], status, type="mstate") else stop("etype argument incompatable with survival type") } @ Once upon a time I allowed survfit to be called without the `\textasciitilde 1' portion of the formula. This was a mistake for multiple reasons, but the biggest problem is timing. If the subject has a data statement but the first argument is not a formula, R needs to evaluate Surv(t,s) to know that it is a survival object, but it also needs to know that this is a survival object before evaluation in order to dispatch the correct method. The method below helps give a useful error message in some cases. <>= survfit.Surv <- function(formula, ...) stop("the survfit function requires a formula as its first argument") @ The last peice in this file is the function to create confidence intervals. It is called from multiple different places so it is well to have one copy. If $p$ is the survival probability and $s(p)$ its standard error, we can do confidence intervals on the simple scale of $ p \pm 1.96 s(p)$, but that does not have very good properties. Instead use a transformation $y = f(p)$ for which the standard error is $s(p) f'(p)$, leading to the confidence interval \begin{equation*} f^{-1}\left(f(p) +- 1.96 s(p)f'(p) \right) \end{equation*} Here are the supported transformations. \begin{center} \begin{tabular}{rccc} &$f$& $f'$ & $f^{-1}$ \\ \hline log & $\log(p)$ & $1/p$ & $ \exp(y)$ \\ log-log & $\log(-\log(p))$ & $1/\left[ p \log(p) \right]$ & $\exp(-\exp(y)) $ \\ logit & $\log(p/1-p)$ & $1/[p (1-p)]$ & $1- 1/\left[1+ \exp(y)\right]$ \\ arcsin & $\arcsin(\sqrt{p})$ & $1/(2 \sqrt{p(1-p)})$ &$\sin^2(y)$ \\ \end{tabular} \end{center} Plain intervals can give limits outside of (0,1), we truncate them when this happens. The log intervals can give an upper limit greater than 1, but the lower limit is always valid, and the log-log and logit. The arcsin require truncation in the middle of the formula. In all cases we return NA as the CI for survival=0: it makes the graphs look better. Some of the underlying routines compute the standard error of $p$ and some the standard error of $\log(p)$. The \code{selow} argument is used for the modified lower limits of Dory and Korn. When this is used for cumulative hazards the ulimit arg will be FALSE: no upper limit of 1. <>= survfit_confint <- function(p, se, logse=TRUE, conf.type, conf.int, selow, ulimit=TRUE) { zval <- qnorm(1- (1-conf.int)/2, 0,1) if (missing(selow)) scale <- 1.0 else scale <- ifelse(selow==0, 1.0, selow/se) # avoid 0/0 at the origin if (!logse) se <- ifelse(se==0, 0, se/p) # se of log(survival) = log(p) if (conf.type=='plain') { se2 <- se* p * zval # matches equation 4.3.1 in Klein & Moeschberger if (ulimit) list(lower= pmax(p -se2*scale, 0), upper = pmin(p + se2, 1)) else list(lower= pmax(p -se2*scale, 0), upper = p + se2) } else if (conf.type=='log') { #avoid some "log(0)" messages xx <- ifelse(p==0, NA, p) se2 <- zval* se temp1 <- exp(log(xx) - se2*scale) temp2 <- exp(log(xx) + se2) if (ulimit) list(lower= temp1, upper= pmin(temp2, 1)) else list(lower= temp1, upper= temp2) } else if (conf.type=='log-log') { xx <- ifelse(p==0 | p==1, NA, p) se2 <- zval * se/log(xx) temp1 <- exp(-exp(log(-log(xx)) - se2*scale)) temp2 <- exp(-exp(log(-log(xx)) + se2)) list(lower = temp1 , upper = temp2) } else if (conf.type=='logit') { xx <- ifelse(p==0, NA, p) # avoid log(0) messages se2 <- zval * se *(1 + xx/(1-xx)) temp1 <- 1- 1/(1+exp(log(p/(1-p)) - se2*scale)) temp2 <- 1- 1/(1+exp(log(p/(1-p)) + se2)) list(lower = temp1, upper=temp2) } else if (conf.type=="arcsin") { xx <- ifelse(p==0, NA, p) se2 <- .5 *zval*se * sqrt(xx/(1-xx)) list(lower= (sin(pmax(0, asin(sqrt(xx)) - se2*scale)))^2, upper= (sin(pmin(pi/2, asin(sqrt(xx)) + se2)))^2) } else stop("invalid conf.int type") } @ \subsection{Kaplan-Meier} This routine has been rewritten more times than any other in the package, as we trade off simplicty of the code with execution speed. This version does all of the oranizational work in S and calls a C routine for each separate curve. The first code did everything in C but was too hard to maintain and the most recent prior function did nearly everything in S. Introduction of robust variance prompted a movement of more of the code into C since that calculation is computationally intensive. <>= survfitKM <- function(x, y, weights=rep(1.0,length(x)), stype=1, ctype=1, se.fit=TRUE, conf.int= .95, conf.type=c('log', 'log-log', 'plain', 'none', 'logit', "arcsin"), conf.lower=c('usual', 'peto', 'modified'), start.time, id, cluster, robust, influence=FALSE, type) { if (!missing(type)) { if (!is.character(type)) stop("type argument must be character") # older style argument is allowed temp <- charmatch(type, c("kaplan-meier", "fleming-harrington", "fh2")) if (is.na(temp)) stop("invalid value for 'type'") type <- c(1,3,4)[temp] } else { if (!(ctype %in% 1:2)) stop("ctype must be 1 or 2") if (!(stype %in% 1:2)) stop("stype must be 1 or 2") type <- as.integer(2*stype + ctype -2) } conf.type <- match.arg(conf.type) conf.lower<- match.arg(conf.lower) if (is.logical(conf.int)) { # A common error is for users to use "conf.int = FALSE" # it's not correct, but allow it if (!conf.int) conf.type <- "none" conf.int <- .95 } if (!is.Surv(y)) stop("y must be a Surv object") if (attr(y, 'type') != 'right' && attr(y, 'type') != 'counting') stop("Can only handle right censored or counting data") ny <- ncol(y) # Will be 2 for right censored, 3 for counting # The calling routine has used 'strata' on x, so it is a factor with # no unused levels. But just in case a user called this... if (!is.factor(x)) stop("x must be a factor") xlev <- levels(x) # Will supply names for the curves x <- as.integer(x) # keep the integer index if (missing(start.time)) time0 <- min(0, y[,ny-1]) else time0 <- start.time # The user can call with cluster, id, robust, or any combination # Default for robust: if cluster or any id with > 1 event or # any weights that are not 0 or 1, then TRUE # If only id, treat it as the cluster too has.cluster <- !(missing(cluster) || length(cluster)==0) has.id <- !(missing(id) || length(id)==0) has.rwt<- (!missing(weights) && any(weights != floor(weights))) #has.rwt <- FALSE # we are rethinking this has.robust <- !missing(robust) && !is.null(robust) if (has.id) id <- as.factor(id) if (missing(robust) || is.null(robust)) { if (influence) { robust <- TRUE if (!(has.cluster || has.id)) { cluster <- seq(along=x) has.cluster <- TRUE } } else if (has.cluster || has.rwt || (has.id && anyDuplicated(id[y[,ncol(y)]==1]))) robust <- TRUE else robust <- FALSE } if (!is.logical(robust)) stop("robust must be TRUE/FALSE") if (has.cluster) { if (!robust) { warning("cluster specified with robust=FALSE, cluster ignored") ncluster <- 0 clname <- NULL } else { if (is.factor(cluster)) { clname <- levels(cluster) cluster <- as.integer(cluster) } else { clname <- sort(unique(cluster)) cluster <- match(cluster, clname) } ncluster <- length(clname) } } else if (robust) { if (has.id) { # treat the id as both identifier and clustering clname <- levels(id) cluster <- as.integer(id) ncluster <- length(clname) } else if (ncol(y)==2 || !has.robust) { # create our own clustering n <- nrow(y) cluster <- 1:n ncluster <- n clname <- 1:n } else stop("id or cluster option required") } else ncluster <- 0 if (is.logical(influence)) { # TRUE/FALSE is treated as all or nothing if (!influence) influence <- 0L else influence <- 3L } else if (!is.numeric(influence)) stop("influence argument must be numeric or logical") if (!(influence %in% 0:3)) stop("influence argument must be 0, 1, 2, or 3") else influence <- as.integer(influence) if (!robust && influence >0) { warning("robust=FALSE implies influence=FALSE") influence <- 0L } if (!se.fit) { # if the user asked for no standard error, skip any robust computation ncluster <- 0L influence <- 0L } # if start.time was set, delete obs if necessary keep <- y[,ny-1] >= time0 if (!all(keep)) { y <- y[keep,] if (length(id) >0) id <- id[keep] if (length(cluster) >0) cluster <- cluster[keep] x <- x[keep] weights <- weights[keep] } <> <> } @ At each event time we have \begin{itemize} \item n(t) = number at risk = sum of weigths for those at risk \item d(t) = number of events = sum of weights for the deaths \item e(t) = unweighted number of events \end{itemize} From this we can calculate the Kapan-Meier and Nelson-Aalen estimates. The Fleming-Harrington estimate is the analog of the Efron approximation in a Cox model. When there are no case weights the FH idea is quite simple. Assume that the real data is not tied, but we saw a coarsened version. If we see 3 events out of 10 subjects at risk the NA increment is 3/10 but the FH is 1/10 + 1/9 + 1/8, it is what we would have seen with the uncoarsened data. If there are case weights we give each of the 3 terms a 1/3 chance of being the first, second, or third event \begin{align*} KM(t) &= KM(t-) (1- d(t)/n(t) \\ NA(t) &= NA(t-) + d(t)/n(t) \\ FH(t) &= FH(t-) + \sum_{i=1}^{3} \frac{(d(t)/3}{n(t)- d(t)(i-1)/3} \end{align*} When one of these 3 subjects has an event but continues, which can happen with start/stop data, then this gets trickier: the second $d$ in the last equation above should include only the other 2. The idea is that each of those will certainly be present for the first event, has 2/3 chance of being present for the second, and 1/3 for the third. If we think of the size of the denominator as a random variable $Z$, an exact solution would use $E(1/Z)$, the FH uses $1/E(Z)$ and the NA uses $1/\max(Z)$ as the denominator for each of the 3 deaths. One problem with survival is near ties in Y: table, unique, ==, etc. can do different things in this case. Luckily, the parent survfit routine has dealt with that by using the \code{aeqSurv} function. The underlying C code allows the sort1/sort2 vectors to be a different length than y, weights, and cluster. When there is only one curve we use that to our advantage to avoid creating a new copy of the last 3, passing in the original data. When there are multiple curves I had an internal debate about efficiency. Is is better to make a subset of y for each curve = more memory, or keep the original y and address a different subset in each C call = worse memory cache performance? I don't know the answer. In either case the cluster vector needs to be re-done for each group. Say that curve 1 uses subjects 1-10 and curve 2 uses 11-n: we don't want the first curve to compute or keep the zero influence values for all the subjects who are not in it. Especially when returning the influence matrix, which can get too large for memory. If ny==3 and has.id is true, then do some extra setup work, which is to create a position vector of 1=first obs for the subject, 2 = last, 3=both, 0= other, for each set of back to back times. This is used to prevent counting a subject with data of (0,10], (10,15] in both the censored at 10 and entered at 10 totals. We assume the data has been vetted to prevent overlapping intervals, so that it suffices to sort by ending time. If a subject has holes in their timeline they will have more than one first and last indicator. <>= if (ny==3 & has.id) position <- survflag(y, id) else position <- integer(0) if (length(xlev) ==1) {# only one group if (ny==2) { sort1 <- NULL sort2 <- order(y[,1]) } else { sort2 <- order(y[,2]) sort1 <- order(y[,1]) } toss <- (y[sort2, ny-1] < time0) if (any(toss)) { # Some obs were removed by the start.time argument sort2 <- sort2[!toss] if (ny ==3) { index <- match(which(toss), sort1) sort1 <- sort1[-index] } } n.used <- length(sort2) if (ncluster > 0) cfit <- .Call(Csurvfitkm, y, weights, sort1-1L, sort2-1L, type, cluster-1L, ncluster, position, influence) else cfit <- .Call(Csurvfitkm, y, weights, sort1-1L, sort2-1L, type, 0L, 0L, position, influence) } else { # multiple groups ngroup <- length(xlev) cfit <- vector("list", ngroup) n.used <- integer(ngroup) if (influence) clusterid <- cfit # empty list of group id values for (i in 1:ngroup) { keep <- which(x==i & y[,ny-1] >= time0) if (length(keep) ==0) next; # rare case where all are < start.time ytemp <- y[keep,] n.used[i] <- nrow(ytemp) if (ny==2) { sort1 <- NULL sort2 <- order(ytemp[,1]) } else { sort2 <- order(ytemp[,2]) sort1 <- order(ytemp[,1]) } # Cluster is a nuisance: every curve might have a different set # We need to relabel them from 1 to "number of unique clusters in this # curve for the C routine if (ncluster > 0) { c2 <- cluster[keep] c.unique <- sort(unique(c2)) nc <- length(c.unique) c2 <- match(c2, c.unique) # renumber them if (influence >0) { clusterid[[i]] <-c.unique } } if (ncluster > 0) cfit[[i]] <- .Call(Csurvfitkm, ytemp, weights[keep], sort1 -1L, sort2 -1L, type, c2 -1L, length(c.unique), position, influence) else cfit[[i]] <- .Call(Csurvfitkm, ytemp, weights[keep], sort1 -1L, sort2 -1L, type, 0L, 0L, position, influence) } } @ <>= # create the survfit object if (length(n.used) == 1) { rval <- list(n= length(x), time= cfit$time, n.risk = cfit$n[,4], n.event= cfit$n[,5], n.censor=cfit$n[,6], surv = cfit$estimate[,1], std.err = cfit$std[,1], cumhaz = cfit$estimate[,2], std.chaz = cfit$std[,2]) } else { strata <- sapply(cfit, function(x) if (is.null(x$n)) 0L else nrow(x$n)) names(strata) <- xlev # we need to collapse the curves rval <- list(n= as.vector(table(x)), time = unlist(lapply(cfit, function(x) x$time)), n.risk= unlist(lapply(cfit, function(x) x$n[,4])), n.event= unlist(lapply(cfit, function(x) x$n[,5])), n.censor=unlist(lapply(cfit, function(x) x$n[,6])), surv = unlist(lapply(cfit, function(x) x$estimate[,1])), std.err =unlist(lapply(cfit, function(x) x$std[,1])), cumhaz =unlist(lapply(cfit, function(x) x$estimate[,2])), std.chaz=unlist(lapply(cfit, function(x) x$std[,2])), strata=strata) if (ny==3) rval$n.enter <- unlist(lapply(cfit, function(x) x$n[,8])) } if (ny ==3) { rval$n.enter <- cfit$n[,8] rval$type <- "counting" } else rval$type <- "right" if (se.fit) { rval$logse = (ncluster==0 || (type==2 || type==4)) # se(log S) or se(S) rval$conf.int = conf.int rval$conf.type= conf.type if (conf.lower != "usual") rval$conf.lower = conf.lower if (conf.lower == "modified") { nstrat = length(n.used) events <- rval$n.event >0 if (nstrat ==1) events[1] <- TRUE else events[1 + cumsum(c(0, rval$strata[-nstrat]))] <- TRUE zz <- 1:length(events) n.lag <- rep(rval$n.risk[events], diff(c(zz[events], 1+max(zz)))) # # n.lag = the # at risk the last time there was an event (or # the first time of a strata) # } std.low <- switch(conf.lower, 'usual' = rval$std.err, 'peto' = sqrt((1-rval$surv)/ rval$n.risk), 'modified' = rval$std.err * sqrt(n.lag/rval$n.risk)) if (conf.type != "none") { ci <- survfit_confint(rval$surv, rval$std.err, logse=rval$logse, conf.type, conf.int, std.low) rval <- c(rval, list(lower=ci$lower, upper=ci$upper)) } } else { # for consistency don't return the se if std.err=FALSE rval$std.err <- NULL rval$std.chaz <- NULL } # Add the influence, if requested by the user # remember, if type= 3 or 4, the survival influence has to be constructed. if (influence > 0) { if (type==1 | type==2) { if (influence==1 || influence ==3) { if (length(xlev)==1) { rval$influence.surv <- cfit$influence1 row.names(rval$influence.surv) <- clname } else { temp <- vector("list", ngroup) for (i in 1:ngroup) { temp[[i]] <- cfit[[i]]$influence1 row.names(temp[[i]]) <- clname[clusterid[[i]]] } rval$influence.surv <- temp } } if (influence==2 || influence==3) { if (length(xlev)==1) { rval$influence.chaz <- cfit$influence2 row.names(rval$influence.chaz) <- clname } else { temp <- vector("list", ngroup) for (i in 1:ngroup) { temp[[i]] <- cfit[[i]]$influence2 row.names(temp[[i]]) <- clname[clusterid[[i]]] } rval$influence.chaz <- temp } } } else { # everything is derived from the influence of the cumulative hazard if (length(xlev) ==1) { temp <- cfit$influence2 row.names(temp) <- clname } else { temp <- vector("list", ngroup) for (i in 1:ngroup) { temp[[i]] <- cfit[[i]]$influence2 row.names(temp[[i]]) <- clname[clusterid[[i]]] } } if (influence==2 || influence ==3) rval$influence.chaz <- temp if (influence==1 || influence==3) { # if an obs moves the cumulative hazard up, then it moves S down if (length(xlev) ==1) rval$influence.surv <- -temp * rep(rval$surv, each=nrow(temp)) else { for (i in 1:ngroup) temp[[i]] <- -temp[[i]] * rep(cfit[[i]]$estimate[,1], each=nrow(temp[[i]])) rval$influence.surv <- temp } } } } if (!missing(start.time)) rval$start.time <- start.time rval @ Now for the real work using C routines. My standard for a variable named ``zed'' is to use zed2 for the S object and zed for the data part of the object; the latter is what the C code works with. <>= #include #include "survS.h" #include "survproto.h" SEXP survfitkm(SEXP y2, SEXP weight2, SEXP sort12, SEXP sort22, SEXP type2, SEXP id2, SEXP nid2, SEXP position2, SEXP influence2) { int i, i1, i2, j, k, person1, person2; int nused, nid, type, influence; int ny, ntime; double *tstart=0, *stime, *status, *wt; double v1, v2, dtemp, haz; double temp, dtemp2, dtemp3, frac, btemp; int *sort1=0, *sort2, *id=0; static const char *outnames[]={"time", "n", "estimate", "std.err", "influence1", "influence2", ""}; SEXP rlist; double *gwt=0, *inf1=0, *inf2=0; /* =0 to silence -Wall */ int *gcount=0; int n1, n2, n3, n4; int *position=0, hasid; double wt1, wt2, wt3, wt4; /* output variables */ double *n[10], *dtime, *kvec, *nvec, *std[2], *imat1=0, *imat2=0; /* =0 to silence -Wall*/ double km, nelson; /* current estimates */ /* map the input data */ ny = ncols(y2); /* 2= ordinary survival 3= start,stop data */ nused = nrows(y2); if (ny==3) { tstart = REAL(y2); stime = tstart + nused; sort1 = INTEGER(sort12); } else stime = REAL(y2); status= stime +nused; wt = REAL(weight2); sort2 = INTEGER(sort22); nused = LENGTH(sort22); type = asInteger(type2); nid = asInteger(nid2); if (LENGTH(position2) > 0) { hasid =1; position = INTEGER(position2); } else hasid=0; influence = asInteger(influence2); /* nused was used for two things just above. The first was the length of the input data y, only needed for a moment to set up tstart, stime, and status. The second is the number of these observations we will actually use, which is the length of sort2. This routine can be called multiple times with sort1/sort2 pointing to different subsets of the data while y, wt, id and position can remain unchanged */ if (length(id2)==0) nid =0; /* no robust variance */ else id = INTEGER(id2); /* pass 1, get the number of unique times, needed for memory allocation Number of xval groups (unique id values) has been supplied Data is sorted by time */ ntime =1; temp = stime[sort2[0]]; for (i=1; i>= /* Allocate memory for the output n has 6 columns for number at risk, events, censor, then the 3 weighted versions of the same, then optionally two more for number added to the risk set (when ny=3) */ PROTECT(rlist = mkNamed(VECSXP, outnames)); dtime = REAL(SET_VECTOR_ELT(rlist, 0, allocVector(REALSXP, ntime))); if (ny==2) j=7; else j=9; n[0] = REAL(SET_VECTOR_ELT(rlist, 1, allocMatrix(REALSXP, ntime, j))); for (i=1; i0 ) { /* robust variance */ gcount = (int *) R_alloc(nid, sizeof(int)); if (type <3) { /* working vectors for the influence */ gwt = (double *) R_alloc(3*nid, sizeof(double)); inf1 = gwt + nid; inf2 = inf1 + nid; for (i=0; i< nid; i++) { gwt[i] =0.0; gcount[i] = 0; inf1[i] =0; inf2[i] =0; } } else { gwt = (double *) R_alloc(2*nid, sizeof(double)); inf2 = gwt + nid; for (i=0; i< nid; i++) { gwt[i] =0.0; gcount[i] = 0; inf2[i] =0; } } /* these are not accumulated, so do not need to be zeroed */ if (type <3) { if (influence==1 || influence ==3) imat1 = REAL(SET_VECTOR_ELT(rlist, 4, allocMatrix(REALSXP, nid, ntime))); if (influence==2 || influence==3) imat2 = REAL(SET_VECTOR_ELT(rlist, 5, allocMatrix(REALSXP, nid, ntime))); } else if (influence !=0) imat2 = REAL(SET_VECTOR_ELT(rlist, 5, allocMatrix(REALSXP, nid, ntime))); } <> <> UNPROTECT(1); return(rlist); } @ Pass 2 goes from the last time to the first and fills in the \code{n} matrix. <>= R_CheckUserInterrupt(); /*check for control-C */ /* ** person1, person2 track through sort1 and sort2, respectively ** likewise with i1 and i2 */ person1 = nused-1; person2 = nused-1; n1=0; wt1=0; for (k=ntime-1; k>=0; k--) { dtime[k] = stime[sort2[person2]]; /* current time point */ n2=0; n3=0; wt2=0; wt3=0; for (; person2 >=0; person2--) { i2= sort2[person2]; if (stime[i2] != dtime[k]) break; n1++; /* number at risk */ wt1 += wt[i2]; /* weighted number at risk */ if (status[i2] ==1) { n2++; /* events */ wt2 += wt[i2]; } else if (hasid==0 || (position[i2]& 2)) { /* if there are no repeated obs for a subject (hasid=0) ** or this is the last of a string (a,b](b,c](c,d].. for ** a subject (position[i2]=2 or 3), then it is a 'real' censor */ n3++; wt3 += wt[i2]; } } if (ny==3) { /* remove any with start time >=dtime*/ n4 =0; wt4 =0; for (; person1 >=0; person1--) { i1 = sort1[person1]; if (tstart[i1] < dtime[k]) break; n1--; wt1 -= wt[i1]; if (hasid==0 || (position[i1] & 1)) { /* if there are no repeated id (hasid=0) or this is the ** first of a string of (a,b](b,c](c,d] for a subject, then ** this is a 'real' entry */ n4++; wt4 += wt[i1]; } } if (n4>0) { n[6][k+1] = n4; n[7][k+1] = wt4; } } n[0][k] = n1; n[1][k]=n2; n[2][k]=n3; n[3][k] = wt1; n[4][k]=wt2; n[5][k]=wt3; } if (ny ==3) { /* fill in number entered for the initial interval */ n4=0; wt4=0; for (; person1>=0; person1--) { i1 = sort1[person1]; if (hasid==0 || (position[i1] & 1)) { n4++; wt4 += wt[i1]; } } n[6][0] = n4; n[7][0] = wt4; } @ The rest of the code is identical for simple survival or start-stop data. The cumulative hazard estimates are the Nelson-Aalen-Breslow (same estimate, three different papers) or the Fleming-Harrington. \begin{align*} \Lambda_A(t) &\ \sum{u_j \le t} d_j/r_j \\ \Lambda_{FH}(t) &= \sum{u_j \le t} \frac{d_j} {f_j \sum_{k=0}^{f_j-1} (r_j - kd_j/f_j)} \end{align*} To understand the Fleming-Harrington estimate, suppose that at some time point we had three deaths out of 10 at risk. The Aalen estimate gives a hazard estimate of 3/10. The FH estimate assumes that the deaths didn't actually all happen at once, even though rounding in the data collection process makes it appear that way, so the better estimate is 1/10 + 1/9 + 1/8. The third person to die, whoever that was, would have had only 8 at risk when their event happened. The estimate of survival is either the Kaplan-Meier or the exponential of the hazard. \begin{equation*} KM(t) = \prod_{u_j \le t} \frac{r_j - d_j}{r_j} \end{equation*} The third pass goes from smallest time to largest. 99 times out of 100 the user will choose type=1, so we try to avoid testing those expression n times. <>= R_CheckUserInterrupt(); /*check for control-C */ nelson =0.0; km=1.0; v1=0; v2=0; if (nid==0) { /* simple variance */ if (type==1 || type==3) { /* Nelson-Aalen hazard */ for (i=0; i0 && n[4][i]>0) { /* at least one event with wt>0*/ nelson += n[4][i]/n[3][i]; v2 += n[4][i]/(n[3][i]*n[3][i]); } nvec[i] = nelson; std[0][i] = sqrt(v2); std[1][i] = sqrt(v2); } } else { /* Fleming hazard */ for (i=0; i0 && n[4][i]>0) { /* at least one event */ km *= (n[3][i]-n[4][i])/n[3][i]; v1 += n[4][i]/(n[3][i] * (n[3][i] - n[4][i])); /* Greenwood */ } kvec[i] = km; std[0][i] = sqrt(v1); } } else { /* exp survival */ for (i=0; i< ntime; i++) { kvec[i] = exp(-nvec[i]); std[0][i] = std[1][i]; } } } else { /* infinitesimal jackknife variance */ <> } @ The robust variance is based on an infinitesimal jackknife (IJ). Let $S_{-i}(t)$ be the survival curve without subject $i$ and $J_i(t) = S_i(t) - S_{-i}(t)$ be the change in the survival curve from adding subject $i$ back in. Then the jackknife estimate of variance is $$ \sigma^2_J(t) = \sum \left( J_i(t) - \overline J(t) \right)^2 $$ The IJ estimate instead uses the linear approximation to the jackknife, since it is normally less work to compute the derivative than a whole new estimate. Notice that if all the weights were doubled the expression below will stay the same since the derivative will drop by 1/2. \begin{align*} \sigma^2_{IJ}(t_k) & = \sum_i w_i U_{ik}^2 \\ U_{ik} & = \frac{\partial S(t_k)}{\partial w_i} \end{align*} The big problem with the IJ estimate is that a first derivative matrix $U$ will have one row per subject and one column per event time. Since the number of unique event times tends to grow with $n$, this matrix very rapidly becomes too large to manage. Instead use a grouped jackknife with $g$ groups, $g$ will often be on the order of 20--50. The 0/1 design matrix $B$ has $n$ rows and $g$ columns, one column per group, marking which subject is in each group. The grouped jackknife can be written as \begin{align*} U'WBB'W U &= V'V \end{align*} Our goal is to accumulate and use $V$ instead of $U$. The working vectors \code{inf1} and \code{inf2} contain the current estimate for the survival S and cumulative hazard H, at a given time. They are saved into the \code{imat} array for users, if desired. First work this out for the cumulative hazard, which is simpler, and a single subject $k$. \begin{align} H(t) &= \sum_{s\le t} \frac{\sum w_i dN_i(s)}{\sum w_i Y_i(s)} \nonumber\\ &= \sum _{s\le t} h(s) \nonumber \\ U_k(t) &= U_k(t-) + \frac{\partial h(t)}{\partial w_k} \nonumber \\ &= U_k(t-) + \frac{1}{\sum w_i Y_i(t)} \left(dN_k(t) - Y_k(t)h(t) \right) \label{Una} \\ \sum_k w_k U_k(t) &= 0 \nonumber \end{align} using the counting process notation of $N(t)$ for events and $Y(t)$ for at risk. The weighted sum of the first derivatives is zero, so we don't need a mean when computing the variance estimate. (This is true for all IJ estimators.) $V$ involves the weighted sum of this over groups, for the increment to each row of $V$ the rightmost term of \eqref{Una} is replaced by the weighted sum over each group. Since $h$ is the same for every subject at risk, we only need accumulate the sum of subjects in and sum of events in each group. The first can be kept as a running sum with $O(n)$ effort. When using the FH2 estimate tied deaths are different. Say that subject $i$ dies at some time $t$ where there are 2 other tied deaths. Let $w_i$ for $i=1,2,3$ be the weight of those who die and $s$ the sum of weights for all the others. The contribution to the cumulative hazard and derivative at this time point is \begin{align*} h &= \frac{w_1+w_2+w_3}{3} \frac{1}{s+w_1+w_2+w_3} + \frac{w_1+w_2+w_3}{3} \frac{1}{s+ 2(w_1+w_2+w_3)/3} + \frac{w_1+w_2+w_3}{3} \frac{1}{s+ (w_1 + w_2 + w_3)/3} \\ &\equiv a(b_1 + b_2 + b_3) \frac{\partial h}{\partial w_i} &= &= \left\{ \begin{array}{cl} \frac{b_1 + b_2 + b_3}{3} - a b_1^2 - (2/3)a b_2^2 - (1/3)a b_3^2 & i\le 3 \\ -a(b_1^2 + b_2^2 + b_3^2) & i> 3 \end{array} \right . \end{align*} The idea is that if the data had been gathered with more precision, then there would not be ties. The first death has 1/3 chance of being subject 1,2, or 3 and all are in the denominator. The second also has 1/3 chance of being 1--3, and each of these has 2/3 chance of still being in the denominator, etc. The standard variance will be $ab_1^2 + ab_2^2 + ab_3^2$. For the Kaplan-Meier we have \begin{align} KM(t) &= KM(t-) [1 - h(t)] \nonumber\\ U_k(t) &= \frac{\partial KM(t)}{\partial w_k} \nonumber\\ &= U_k(t-) [1- h(t)] - KM(t-)\frac{\partial h(t)}{\partial w_k} \label{Ukm} \end{align} The $V$ matrix is again a weighted sum. The first term of \eqref{Ukm} does not change, it multiplies the current value times $1-h$. The second term involves the same summation as the cumulative hazard. When using $\exp(-H)$ as the survival estimate then \begin{align*} \frac{partial S(t)}{\partial w_k} &= \frac{\partial e^{-H(t)}}{\partial w_k}\\ &= e^{-H(t)} \partial{H(t)}{\partial w_k} \end{align*} so in this case only the robust variance for the cumulative hazard $H$ is needed, and the parent R routine can fill in the rest. The variance for a given survival time is $\sum V^2$, which is always returned. The code keeps the current $V$ vector for the hazard $H$ in \code{inf2}, and if necessary that for the KM in \code{inf1}. A last step is to add up the squares of all of these, so the algorithm is $O(gp)$ where $p$ is the number of unique event times and $g$ is the number of groups. The sum of weights for each group is kept in a vector \code{gwt}, which is updated as subjects enter and leave. Say that a study had n= 10 million subjects in g=100 groups with d = 1 million deaths. At each death we update the 'hazard' part of the influence for all 100 groups, which is O(gd). The deaths at that time point have a second increment, to whichever group each is in, but since time is sorted that adds O(n) for indexing and O(d) for the work. The most important thing is to avoid doing anything that would be O(ng) or O(nd). For method=3 the hazard part of the increment is also different for a death, the solution is to do an ordinary increment for everyone in the O(gd) step, then correct it when doing the O(d) update. <>= v1=0; v2 =0; km=1; nelson =0; person2=0; if (ny==3) { person1 =0; } else { /* at the start, everyone is at risk */ for (i=0; i< nused; i++) { i2 = id[i]; gcount[i2]++; gwt[i2] += wt[i]; } } if (type==1) { for (i=0; i< ntime; i++) { if (ny==3) { /* add in new subjects */ for (; person1 < nused; person1++) { /* add in those whose start time is < dtime */ i1 = sort1[person1]; if (tstart[i1] >= dtime[i]) break; gcount[id[i1]]++; gwt[id[i1]] += wt[i1]; } } if (n[1][i] > 0 && n[4][i]>0) { /* need to update the sums */ haz = n[4][i]/n[3][i]; for (k=0; k< nid; k++) { inf1[k] = inf1[k] *(1.0 -haz) + gwt[k]*km*haz/n[3][i]; inf2[k] -= gwt[k] * haz/n[3][i]; } for (; person2 dtime[i]) break; /* those at this time */ if (status[i2]==1) { inf1[id[i2]] -= km* wt[i2]/n[3][i]; inf2[id[i2]] += wt[i2]/n[3][i]; } gcount[id[i2]] --; if (gcount[id[i2]] ==0) gwt[id[i2]] = 0.0; else gwt[id[i2]] -= wt[i2]; } km *= (1-haz); nelson += haz; v1=0; v2=0; for (k=0; k dtime[i]) break; gcount[id[i2]] --; if (gcount[id[i2]] ==0) gwt[id[i2]] = 0.0; else gwt[id[i2]] -= wt[i2]; } } kvec[i] = km; nvec[i] = nelson; std[0][i] = sqrt(v1); std[1][i] = sqrt(v2); if (influence==1 || influence ==3) for (k=0; k= dtime[i]) break; gcount[id[i1]]++; gwt[id[i1]] += wt[i1]; } } if (n[1][i] > 0 && n[4][i] >0) { /* need to update the sums */ dtemp =0; /* the working denominator */ dtemp2=0; /* sum of squares */ dtemp3=0; temp = n[3][i] - n[4][i]; /* sum of weights for the non-deaths */ for (k=n[1][i]; k>0; k--) { frac = k/n[1][i]; btemp = 1/(temp + frac*n[4][i]); /* "b" in the math */ dtemp += btemp; dtemp2 += btemp*btemp*frac; dtemp3 += btemp*btemp; /* non-death deriv */ } dtemp /= n[1][i]; /* average denominator */ if (n[4][i] != n[1][i]) { /* case weights */ dtemp2 *= n[4][i]/ n[1][i]; dtemp3 *= n[4][i]/ n[1][i]; } nelson += n[4][i]*dtemp; haz = n[4][i]/n[3][i]; for (k=0; k< nid; k++) { inf1[k] = inf1[k] *(1.0 -haz) + gwt[k]*km*haz/n[3][i]; if (gcount[k]>0) inf2[k] -= gwt[k] * dtemp3; } for (; person2 dtime[i]) break; if (status[i2]==1) { inf1[id[i2]] -= km* wt[i2]/n[3][i]; inf2[id[i2]] += wt[i2] *(dtemp + dtemp3 - dtemp2); } gcount[id[i2]] --; if (gcount[id[i2]] ==0) gwt[id[i2]] = 0.0; else gwt[id[i2]] -= wt[i2]; } km *= (1-haz); v1=0; v2=0; for (k=0; k dtime[i]) break; gcount[id[i2]] --; if (gcount[id[i2]] ==0) gwt[id[i2]] = 0.0; else gwt[id[i2]] -= wt[i2]; } } kvec[i] = km; nvec[i] = nelson; std[0][i] = sqrt(v1); std[1][i] = sqrt(v2); if (influence==1 || influence ==3) for (k=0; k= dtime[i]) break; gcount[id[i1]]++; gwt[id[i1]] += wt[i1]; } } if (n[1][i] > 0 && n[4][i]>0) { /* need to update the sums */ haz = n[4][i]/n[3][i]; for (k=0; k< nid; k++) { inf2[k] -= gwt[k] * haz/n[3][i]; } for (; person2 dtime[i]) break; if (status[i2]==1) { inf2[id[i2]] += wt[i2]/n[3][i]; } gcount[id[i2]] --; if (gcount[id[i2]] ==0) gwt[id[i2]] = 0.0; else gwt[id[i2]] -= wt[i2]; } nelson += haz; v2=0; for (k=0; k dtime[i]) break; gcount[id[i2]] --; if (gcount[id[i2]] ==0) gwt[id[i2]] = 0.0; else gwt[id[i2]] -= wt[i2]; } } kvec[i] = exp(-nelson); nvec[i] = nelson; std[1][i] = sqrt(v2); std[0][i] = sqrt(v2); if (influence>0) for (k=0; k= dtime[i]) break; gcount[id[i1]]++; gwt[id[i1]] += wt[i1]; } } if (n[1][i] > 0 && n[4][i] >0) { /* need to update the sums */ dtemp =0; /* the working denominator */ dtemp2=0; /* sum of squares */ dtemp3=0; temp = n[3][i] - n[4][i]; /* sum of weights for the non-deaths */ for (k=n[1][i]; k>0; k--) { frac = k/n[1][i]; btemp = 1/(temp + frac*n[4][i]); /* "b" in the math */ dtemp += btemp; dtemp2 += btemp*btemp*frac; dtemp3 += btemp*btemp; /* non-death deriv */ } dtemp /= n[1][i]; /* average denominator */ if (n[4][i] != n[1][i]) { /* case weights */ dtemp2 *= n[4][i]/ n[1][i]; dtemp3 *= n[4][i]/ n[1][i]; } nelson += n[4][i]*dtemp; for (k=0; k< nid; k++) { if (gcount[k]>0) inf2[k] -= gwt[k] * dtemp3; } for (; person2 dtime[i]) break; if (status[i2]==1) { inf2[id[i2]] += wt[i2] *(dtemp + dtemp3 - dtemp2); } gcount[id[i2]] --; if (gcount[id[i2]] ==0) gwt[id[i2]] = 0.0; else gwt[id[i2]] -= wt[i2]; } v2=0; for (k=0; k dtime[i]) break; gcount[id[i2]] --; if (gcount[id[i2]] ==0) gwt[id[i2]] = 0.0; else gwt[id[i2]] -= wt[i2]; } } kvec[i] = exp(-nelson); nvec[i] = nelson; std[1][i] = sqrt(v2); std[0][i] = sqrt(v2); if (influence>0) for (k=0; k>= <> survfitCI <- function(X, Y, weights, id, cluster, robust, istate, stype=1, ctype=1, se.fit=TRUE, conf.int= .95, conf.type=c('log', 'log-log', 'plain', 'none', 'logit', "arcsin"), conf.lower=c('usual', 'peto', 'modified'), influence = FALSE, start.time, p0, type){ if (!missing(type)) { if (!missing(ctype) || !missing(stype)) stop("cannot have both an old-style 'type' argument and the stype/ctype arguments that replaced it") if (!is.character(type)) stop("type argument must be character") # older style argument is allowed temp <- charmatch(type, c("kaplan-meier", "fleming-harrington", "fh2")) if (is.na(temp)) stop("invalid value for 'type'") type <- c(1,3,4)[temp] } else { if (!(ctype %in% 1:2)) stop("ctype must be 1 or 2") if (!(stype %in% 1:2)) stop("stype must be 1 or 2") type <- as.integer(2*stype + ctype -2) } if (type != 1) warning("only stype=1, ctype=1 currently implimented for multi-state data") conf.type <- match.arg(conf.type) conf.lower<- match.arg(conf.lower) if (conf.lower != "usual") warning("conf.lower is ignored for multi-state data") if (is.logical(conf.int)) { # A common error is for users to use "conf.int = FALSE" # it's illegal per documentation, but be kind if (!conf.int) conf.type <- "none" conf.int <- .95 } if (is.logical(influence)) { # TRUE/FALSE is treated as all or nothing if (!influence) influence <- 0L else influence <- 3L } else if (!is.numeric(influence)) stop("influence argument must be numeric or logical") if (!(influence %in% 0:3)) stop("influence argument must be 0, 1, 2, or 3") else influence <- as.integer(influence) if (!se.fit) { # if the user asked for no standard error, skip any robust computation ncluster <- 0L influence <- 0L } type <- attr(Y, "type") # This line should be unreachable, unless they call "surfitCI" directly if (type !='mright' && type!='mcounting') stop(paste("multi-state computation doesn't support \"", type, "\" survival data", sep='')) # If there is a start.time directive, start by removing any prior events if (!missing(start.time)) { if (!is.numeric(start.time) || length(start.time) !=1 || !is.finite(start.time)) stop("start.time must be a single numeric value") toss <- which(Y[,ncol(Y)-1] <= start.time) if (length(toss)) { n <- nrow(Y) if (length(toss)==n) stop("start.time has removed all observations") Y <- Y[-toss,,drop=FALSE] X <- X[-toss] weights <- weights[-toss] if (length(id) ==n) id <- id[-toss] if (!missing(istate) && length(istate)==n) istate <- istate[-toss] } } n <- nrow(Y) status <- Y[,ncol(Y)] ncurve <- length(levels(X)) # The user can call with cluster, id, robust or any combination. # If only id, treat it as the cluster too if (missing(robust) || length(robust)==0) robust <- TRUE if (!robust) stop("multi-state survfit supports only a robust variance") has.cluster <- !(missing(cluster) || length(cluster)==0) has.id <- !(missing(id) || length(id)==0) if (has.id) id <- as.factor(id) else { if (ncol(Y) ==3) stop("an id statement is required for start,stop data") id <- 1:n # older default, which could lead to invalid curves } if (influence && !(has.cluster || has.id)) { cluster <- seq(along.with=X) has.cluster <- TRUE } if (has.cluster) { if (is.factor(cluster)) { clname <- levels(cluster) cluster <- as.integer(cluster) } else { clname <- sort(unique(cluster)) cluster <- match(cluster, clname) } ncluster <- length(clname) } else { if (has.id) { # treat the id as both identifier and clustering clname <- levels(id) cluster <- as.integer(id) ncluster <- length(clname) } else { ncluster <- 0 # has neither clname <- NULL } } if (missing(istate) || is.null(istate)) mcheck <- survcheck2(Y, id) else mcheck <- survcheck2(Y, id, istate) if (any(mcheck$flag > 0)) stop("one or more flags are >0 in survcheck") states <- mcheck$states istate <- mcheck$istate nstate <- length(states) smap <- c(0, match(attr(Y, "states"), states)) Y[,ncol(Y)] <- smap[Y[,ncol(Y)] +1] # new states may be a superset status <- Y[,ncol(Y)] if (mcheck$flag["overlap"] > 0) stop("a subject has overlapping time intervals") # if (mcheck$flag["gap"] > 0 || mcheck$flag["jump"] > 0) # warning("subject(s) with time gaps, results may be questionable") # The states of the status variable are the first columns in the output # any extra initial states are later in the list. # Now that we know the names, verify that p0 is correct (if present) if (!missing(p0) && !is.null(p0)) { if (length(p0) != nstate) stop("wrong length for p0") if (!is.numeric(p0) || abs(1-sum(p0)) > sqrt(.Machine$double.eps)) stop("p0 must be a numeric vector that adds to 1") } else p0 <- NULL @ The status vector will have values of 0 for censored. <>= curves <- vector("list", ncurve) names(curves) <- levels(X) if (ncol(Y)==2) { # 1 transition per subject # dummy entry time that is < any event time t0 <- min(0, Y[,1]) entry <- rep(t0-1, nrow(Y)) for (i in levels(X)) { indx <- which(X==i) curves[[i]] <- docurve2(entry[indx], Y[indx,1], status[indx], istate[indx], weights[indx], states, id[indx], se.fit, influence, p0) } } else { <> <> } <> } @ In the multi-state case we can calculate the current P(state) vector $p(t)$ using the product-limit form, while the cumulative hazard $c(t)$ is a sum. \begin{align*} p(t) &= p(0)\prod_{s<=t} [I + dA(s)] \\ &= p(0) \prod_{s<=t} H(s) \\ c(t) &= \sum_{s<=t} dA(s) \end{align*} Where $p$ is a row vector and $H$ is the multi-state hazard matrix. $H(t)$ is a simple transition matrix. Row $j$ of $H$ describes the outcome of everyone who was in state $j$ at time $t-0$; and is the fraction of them who are in states $1, 2, \ldots$ at time $t+0$. Let $Y_{ij}(t)$ be the indicator function which is 1 if subject $i$ is in state $j$ at time $t-0$, then \begin{equation} H_{jk}(t) = \frac{\sum_i w_i Y_{ij}(t) Y_{ik}(t+)} {\sum_i w_i Y_{ij}(t)} \label{H} \end{equation} Each row of $H$ sums to 1: everyone has to go somewhere. This formula collapses to the Kaplan-Meier in the simple case where $p(t)$ is a vector of length 2 with state 1 = alive and state 2 = dead. The variance is based on per-subject influence. Since $p(t)$ is a vector the influence can be written as a matrix with one row per subject and one column per state. $$ U_{ij}(t) \equiv \frac{\partial p_j(t)}{\partial w_i}. $$ This can be calculate using a recursive formula. First, the derivative of a matrix product $AB$ is $d(A)B + Ad(B)$ where $d(A)$ is the elementwise derivative of $A$ and similarly for $B$. (Write out each element of the matrix product.) Since $p(t) = p(t-)H(t)$, the $i$th row of U satisfies \begin{align} U_i(t) &= \frac{\partial p(t)}{\partial w_i} \nonumber \\ &= \frac{\partial p(t-)}{\partial w_i} H(t) + p(t-) \frac{\partial H(t)}{\partial w_i} \nonumber \\ &= U_i(t-) H(t) + p(t-) \frac{\partial H(t)}{\partial w_i} \label{ci} \end{align} The first term of \ref{ci} collapses to ordinary matrix multiplication. The second term does not: each at risk subject has a unique matrix derivative $\partial H$; $n$ vectors of length $p$ can be arranged into a matrix, making the code simple, but $n$ $p$ by $p$ matrices are not so neat. However, note that \begin{enumerate} \item $\partial H$ is zero for anyone not in the risk set, since their weight does not appear in $H$. \item Each subject who is at risk will be in one (and only one) of the states at the event time, their weight only appears in that row of $H$. Thus for each at risk subject $\partial H$ has only one non-zero row. \end{enumerate} Say that the subject enters the given event time in state $j$ and ends it in state $k$. (For most subjects at most time poinnts $k=j$: if there are 100 at risk at time $t$ and 1 changes state, the other 99 stay put.) Let $n_j(t)= \sum_i Y_{ij}(t)w_i$ be the weighted number of subjects in state $j$, these are the contributers to row $j$ of $H$. Using equation \ref{H}, the derivative of row $j$ with respect to the subject is $(1_k - H_j)/n_j$ where $1_k$ is a vector with 1 in position $k$. The product of $p(t)$ with this matrix is the vector $p_j(t)(1_k - H_j)/n_j$. The second term thus turns out to be fairly simple to compute, but I have not seen a way to write it in a compact matrix form The weighted sum of each column of $U$ will be zero (if computed correctly) and the weighted sum of squares for each column will be the infinitesimal jackknife estimate of variance for the elements of $p$. The entire variance-covariance matrix for the states is $U'W^2U$ where $W$ is a diagonal matrix of weights, but we currently don't report that back. Note that this is for sampling weights. If one has real case weights, where an integer weight of 2 means 2 observations that were collapsed in to one row of data to save space, then the variance is $U'WU$. Case weights were somewhat common in my youth due to small computer memory, but I haven't seen such data in 20 years. The residuals for the cumulative hazard are an easier computation, since each hazard function stands alone. In a multistate model with $k$ states there are potentially $k(k-1)$ hazard functions arranged in a $k$ by $k$ matrix, i.e., as used for the NA update; in the code both the hazard, the IJ scores and the standard errors are kept as matrices with a column for each combination that does occur. At each event time only the rows of U2 that correspond to the risk set will be updated. Below is the function for a single curve. For the status variable a value if 0 is ``no event''. One nuisance in the function is that we need to ensure the tapply command gives totals for all states, not just the ones present in the data --- a call using the \code{subset} argument might not have all the states --- which leads to using factor commands. Another more confusing one is for multiple rows per subject data, where the cstate and U objects have only one row per subject; any given subject is only in one state at a time. This leads to indices of [[atrisk]] for the set of rows in the risk set but [[aindx]] for the subjects in the risk set, [[death]] for the rows that have an event this time and [[dindx]] for the corresponding subjects. The setup for (start, stop] data is a bit more work. We want to ensure that a given subject remains in the same group and that they have a continuous period of observation. If the input data was the result of a tmerge call, say, it might have a lot of extra 'censored' rows. For instance a subject whose state pattern is (0, 5, 1), (5,10, 2), i.e., a transition to state 1 at day 5 and state 2 on day 10 might input as (0,2,0), (2,5,1), (5,6,0), (6,8,0), (8,10,2) instead. These extra censors cause an unnecessary row of output on days 2, 6, and 8. Remove these before going further. <>= # extra censors indx <- order(id, Y[,2]) # in stop order extra <- (survflag(Y[indx,], id[indx]) ==0 & (Y[indx,3] ==0)) # If a subject had obs of (a, b)(b,c)(c,d), and c was a censoring # time, that is an "extra" censoring/entry at c that we don't want # to count. Deal with it by changing that subject # to (a,b)(b,d). Won't change S(t), only the n.censored/n.enter count. if (any(extra)) { e2 <- indx[extra] Y <- cbind(Y[-(1+e2),1], Y[-e2,-1]) status <- status[-e2] X <- X[-e2] id <- id[-e2] istate <- istate[-e2] weights <- weights[-e2] indx <- order(id, Y[,2]) } @ <>= # Now to work for (i in levels(X)) { indx <- which(X==i) # temp <- docurve1(Y[indx,1], Y[indx,2], status[indx], # istate[indx], weights[indx], states, id[indx]) curves[[i]] <- docurve2(Y[indx,1], Y[indx,2], status[indx], istate[indx], weights[indx], states, id[indx], se.fit, influence, p0) } @ <>= # Turn the result into a survfit type object grabit <- function(clist, element) { temp <-(clist[[1]][[element]]) if (is.matrix(temp)) { do.call("rbind", lapply(clist, function(x) x[[element]])) } else { xx <- as.vector(unlist(lapply(clist, function(x) x[element]))) if (inherits(temp, "table")) matrix(xx, byrow=T, ncol=length(temp)) else xx } } # we want to rearrange the cumulative hazard to be in time order # with one column for each observed transtion. nstate <- length(states) temp <- matrix(0, nstate, nstate) indx1 <- match(rownames(mcheck$transitions), states) indx2 <- match(colnames(mcheck$transitions), states, nomatch=0) #ignore censor temp[indx1, indx2[indx2>0]] <- mcheck$transitions[,indx2>0] ckeep <- which(temp>0) names(ckeep) <- outer(1:nstate, 1:nstate, paste, sep='.')[ckeep] #browser() if (length(curves) ==1) { keep <- c("n", "time", "n.risk", "n.event", "n.censor", "pstate", "p0", "cumhaz", "influence.pstate") if (se.fit) keep <- c(keep, "std.err", "sp0") kfit <- (curves[[1]])[match(keep, names(curves[[1]]), nomatch=0)] names(kfit$p0) <- states if (se.fit) kfit$logse <- FALSE kfit$cumhaz <- t(kfit$cumhaz[ckeep,,drop=FALSE]) colnames(kfit$cumhaz) <- names(ckeep) } else { kfit <- list(n = as.vector(table(X)), #give it labels time = grabit(curves, "time"), n.risk= grabit(curves, "n.risk"), n.event= grabit(curves, "n.event"), n.censor=grabit(curves, "n.censor"), pstate = grabit(curves, "pstate"), p0 = grabit(curves, "p0"), strata= unlist(lapply(curves, function(x) if (is.null(x$time)) 0L else length(x$time)))) kfit$p0 <- matrix(kfit$p0, ncol=nstate, byrow=TRUE, dimnames=list(names(curves), states)) if (se.fit) { kfit$std.err <- grabit(curves, "std.err") kfit$sp0<- matrix(grabit(curves, "sp0"), ncol=nstate, byrow=TRUE) kfit$logse <- FALSE } # rearrange the cumulative hazard to be in time order, with columns # for each transition kfit$cumhaz <- do.call(rbind, lapply(curves, function(x) t(x$cumhaz[ckeep,,drop=FALSE]))) colnames(kfit$cumhaz) <- names(ckeep) if (influence) kfit$influence.pstate <- lapply(curves, function(x) x$influence.pstate) } if (!missing(start.time)) kfit$start.time <- start.time kfit$transitions <- mcheck$transitions @ <>= # # Last bit: add in the confidence bands: # if (se.fit && conf.type != "none") { ci <- survfit_confint(kfit$pstate, kfit$std.err, logse=FALSE, conf.type, conf.int) kfit <- c(kfit, ci, conf.type=conf.type, conf.int=conf.int) } kfit$states <- states kfit$type <- attr(Y, "type") kfit @ The updated docurve function is here. One issue that was not recognized originally is delayed entry. If most of the subjects start at time 0, say, but one of them starts at day 100 then that last subject is not a part of $p_0$. We will define $p_0$ as the distribution of states just before the first event. The code above has already ensured that each subject has a unique value for istate, so we don't have to search for the right one. The initial vector and leverage are \begin{align*} p_0 &= (\sum I{s_i=1}w_i, \sum I{s_i=2}w_i, \ldots)/ \sum w_i \\ \frac{\partial p_0}{\partial w_k} &= [(I{s_k=1}, I{s_k=2}, ...)- p_0]/\sum w_i \end{align*} The input data set is not necessarily sorted by time or subject. The data has been checked so that subjects don't have gaps, however. The cstate variable for each subject contains their first istate value. Only those intervals that overlap the first event time contribute to $p_0$. Now: what to report as the ``time'' for the initial row. The values for it come from (first event time -0), i.e. all who are at risk at the smallest \code{etime} with status $>0$. But for normal plotting the smallest start time seems to be a good default. In the usual (start, stop] data a large chunk of the subjects have a common start time. However, if the first event doesn't happen for a while and subjects are dribbling in, then the best point to start a plot is open to debate. Que sera sera. <>= docurve2 <- function(entry, etime, status, istate, wt, states, id, se.fit, influence=FALSE, p0) { timeset <- sort(unique(etime)) nstate <- length(states) uid <- sort(unique(id)) index <- match(id, uid) # Either/both of id and cstate might be factors. Data may not be in # order. Get the initial state for each subject temp1 <- order(id, entry) temp2 <- match(uid, id[temp1]) cstate <- (as.numeric(istate)[temp1])[temp2] # initial state for each # The influence matrix can be huge, make sure we have enough memory if (influence) { needed <- max(nstate * length(uid), 1 + length(timeset)) if (needed > .Machine$integer.max) stop("number of rows for the influence matrix is > the maximum integer") } storage.mode(wt) <- "double" # just in case someone had integer weights # Compute p0 (unless given by the user) if (is.null(p0)) { if (all(status==0)) t0 <- max(etime) #failsafe else t0 <- min(etime[status!=0]) # first transition event at.zero <- (entry < t0 & etime >= t0) wtsum <- sum(wt[at.zero]) # weights for a subject may change p0 <- tapply(wt[at.zero], istate[at.zero], sum) / wtsum p0 <- ifelse(is.na(p0), 0, p0) #for a state not in at.zero, tapply =NA } # initial leverage matrix nid <- length(uid) i0 <- matrix(0., nid, nstate) if (all(p0 <1)) { #actually have to compute it who <- index[at.zero] # this will have no duplicates for (j in 1:nstate) i0[who,j] <- (ifelse(istate[at.zero]==states[j], 1, 0) - p0[j])/wtsum } storage.mode(cstate) <- "integer" storage.mode(status) <- "integer" # C code has 0 based subscripts if (influence) se.fit <- TRUE # se.fit is free in this case fit <- .Call(Csurvfitci, c(entry, etime), order(entry) - 1L, order(etime) - 1L, length(timeset), status, as.integer(cstate) - 1L, wt, index -1L, p0, i0, as.integer(se.fit) + 2L*as.integer(influence)) if (se.fit) out <- list(n=length(etime), time= timeset, p0 = p0, sp0= sqrt(colSums(i0^2)), pstate = fit$p, std.err=fit$std, n.risk = fit$nrisk, n.event= fit$nevent, n.censor=fit$ncensor, cumhaz = fit$cumhaz) else out <- list(n=length(etime), time= timeset, p0=p0, pstate = fit$p, n.risk = fit$nrisk, n.event = fit$nevent, n.censor= fit$ncensor, cumhaz= fit$cumhaz) if (influence) { temp <- array(fit$influence, dim=c(length(uid), nstate, 1+ length(timeset)), dimnames=list(uid, NULL, NULL)) out$influence.pstate <- aperm(temp, c(1,3,2)) } out } @ \subsubsection{C-code} (This is set up as a separate file in the source code directory since it is easier to make emacs stay in C-mode if the file has a .nw extension.) <>= #include "survS.h" #include "survproto.h" #include SEXP survfitci(SEXP ftime2, SEXP sort12, SEXP sort22, SEXP ntime2, SEXP status2, SEXP cstate2, SEXP wt2, SEXP id2, SEXP p2, SEXP i02, SEXP sefit2) { <> <> <> } @ Arguments to the routine are the following. For an R object ``zed'' I use the convention of [[zed2]] to refer to the object and [[zed]] to the contents of the object. \begin{description} \item[ftime] A two column matrix containing the entry and exit times for each subject. \item[sort1] Order vector for the entry times. The first element of sort1 points to the first entry time, etc. \item[sort2] Order vector for the event times. \item[ntime] Number of unique event time values. This fixes the size of the output arrays. \item[status] Status for each observation. 0= censored \item[cstate] The initial state for each subject, which will be updated during computation to always be the current state. \item[wt] Case weight for each observation. \item[id] The subject id for each observation. \item[p] The initial distribution of states. This will be updated during computation to be the current distribution. \item[i0] The initial influence matrix, number of subjects by number of states \item[sefit] If 1 then do the se compuatation, if 2 also return the full influence matrix upon which it is based, if 0 the se is not needed. \end{description} Note that code is called with id and not cluster: there is a basic premise that each id is a single subject and thus has a unique "current state" at any given time point. The history of this is that before the survcheck routine, we did not have a good way for a user to normalize the 'current state' variable for a subject, so this routine takes care of that tracking process. When multi-state Cox models were added we became more formal about this, and users can now have data sets with quite odd patterns of transitions and current state, ones that survcheck calls a teleport. At some point this routine should be updated as well. Cumulative hazard estimates make at least some sense when a subject has a hole, though P(state |t) curves do not. Declare all of the variables. <>= int i, j, k, kk; /* generic loop indices */ int ck, itime, eptr; /*specific indices */ double ctime; /*current time of interest, in the main loop */ int oldstate, newstate; /*when changing state */ double temp, *temp2; /* scratch double, and vector of length nstate */ double *dptr; /* reused in multiple contexts */ double *p; /* current prevalence vector */ double **hmat; /* hazard matrix at this time point */ double **umat=0; /* per subject leverage at this time point */ int *atrisk; /* 1 if the subject is currently at risk */ int *ns; /* number curently in each state */ int *nev; /* number of events at this time, by state */ double *ws; /* weighted count of number state */ double *wtp; /* case weights indexed by subject */ double wevent; /* weighted number of events at current time */ int nstate; /* number of states */ int n, nperson; /*number of obs, subjects*/ double **chaz; /* cumulative hazard matrix */ /* pointers to the R variables */ int *sort1, *sort2; /*sort index for entry time, event time */ double *entry,* etime; /*entry time, event time */ int ntime; /* number of unique event time values */ int *status; /*0=censored, 1,2,... new states */ int *cstate; /* current state for each subject */ int *dstate; /* the next state, =cstate if not an event time */ double *wt; /* weight for each observation */ double *i0; /* initial influence */ int *id; /* for each obs, which subject is it */ int sefit; /* returned objects */ SEXP rlist; /* the returned list and variable names of same */ const char *rnames[]= {"nrisk","nevent","ncensor", "p", "cumhaz", "std", "influence.pstate", ""}; SEXP setemp; double **pmat, **vmat=0, *cumhaz, *usave=0; /* =0 to silence -Wall warning */ int *ncensor, **nrisk, **nevent; @ Now set up pointers for all of the R objects sent to us. The two that will be updated need to be replaced by duplicates. <>= ntime= asInteger(ntime2); nperson = LENGTH(cstate2); /* number of unique subjects */ n = LENGTH(sort12); /* number of observations in the data */ PROTECT(cstate2 = duplicate(cstate2)); cstate = INTEGER(cstate2); entry= REAL(ftime2); etime= entry + n; sort1= INTEGER(sort12); sort2= INTEGER(sort22); status= INTEGER(status2); wt = REAL(wt2); id = INTEGER(id2); PROTECT(p2 = duplicate(p2)); /*copy of initial prevalence */ p = REAL(p2); nstate = LENGTH(p2); /* number of states */ i0 = REAL(i02); sefit = asInteger(sefit2); /* allocate space for the output objects ** Ones that are put into a list do not need to be protected */ PROTECT(rlist=mkNamed(VECSXP, rnames)); setemp = SET_VECTOR_ELT(rlist, 0, allocMatrix(INTSXP, ntime, nstate)); nrisk = imatrix(INTEGER(setemp), ntime, nstate); /* time by state */ setemp = SET_VECTOR_ELT(rlist, 1, allocMatrix(INTSXP, ntime, nstate)); nevent = imatrix(INTEGER(setemp), ntime, nstate); /* time by state */ setemp = SET_VECTOR_ELT(rlist, 2, allocVector(INTSXP, ntime)); ncensor = INTEGER(setemp); /* total at each time */ setemp = SET_VECTOR_ELT(rlist, 3, allocMatrix(REALSXP, ntime, nstate)); pmat = dmatrix(REAL(setemp), ntime, nstate); setemp = SET_VECTOR_ELT(rlist, 4, allocMatrix(REALSXP, nstate*nstate, ntime)); cumhaz = REAL(setemp); if (sefit >0) { setemp = SET_VECTOR_ELT(rlist, 5, allocMatrix(REALSXP, ntime, nstate)); vmat= dmatrix(REAL(setemp), ntime, nstate); } if (sefit >1) { /* the max space is larger for a matrix than a vector ** This is pure sneakiness: if I allocate a vector then n*nstate*(ntime+1) ** may overflow, as it is an integer argument. Using the rows and cols of ** a matrix neither overflows. But once allocated, I can treat setemp ** like a vector since usave is a pointer to double, which is bigger than ** integer and won't overflow. */ setemp = SET_VECTOR_ELT(rlist, 6, allocMatrix(REALSXP, n*nstate, ntime+1)); usave = REAL(setemp); } /* allocate space for scratch vectors */ ws = (double *) R_alloc(2*nstate, sizeof(double)); /*weighted number in state */ temp2 = ws + nstate; ns = (int *) R_alloc(2*nstate, sizeof(int)); nev = ns + nstate; atrisk = (int *) R_alloc(2*nperson, sizeof(int)); dstate = atrisk + nperson; wtp = (double *) R_alloc(nperson, sizeof(double)); hmat = (double**) dmatrix((double *)R_alloc(nstate*nstate, sizeof(double)), nstate, nstate); chaz = (double**) dmatrix((double *)R_alloc(nstate*nstate, sizeof(double)), nstate, nstate); if (sefit >0) umat = (double**) dmatrix((double *)R_alloc(nperson*nstate, sizeof(double)), nstate, nperson); /* R_alloc does not zero allocated memory */ for (i=0; i>= if (sefit ==1) { dptr = i0; for (j=0; j1) { /* copy influence, and save it */ dptr = i0; for (j=0; j>= itime =0; /*current time index, for output arrays */ eptr = 0; /*index to sort1, the entry times */ for (i=0; i> <> /* Take the current events and censors out of the risk set */ for (; i0) cstate[id[j]] = status[j]-1; /*new state */ atrisk[id[j]] =0; } else break; } itime++; } @ The key variables for the computation are the matrix $H$ and the current prevalence vector $P$. $H$ is created anew at each unique time point. Row $j$ of $H$ concerns everyone in state $j$ just before the time point, and contains the transitions at that time point. So the $jk$ element is the (weighted) fraction who change from state $j$ to state $k$, and the $jj$ element the fraction who stay put. Each row of $H$ by definition sums to 1. If no one is in the state then the $jj$ element is set to 1. A second version which we call H2 has 1 subtracted from each diagonal giving row sums are 0, we go back and forth depending on which is needed at the moment. If there are no events at this time point $P$ and $U$ do not update. <>= for (j=0; j0) { newstate = status[k] -1; /* 0 based subscripts */ oldstate = cstate[id[k]]; if (oldstate != newstate) { /* A "move" to the same state does not count */ dstate[id[k]] = newstate; nev[newstate]++; wevent += wt[k]; hmat[oldstate][newstate] += wt[k]; } } else ncensor[itime]++; } else break; } if (wevent > 0) { /* there was at least one move with weight > 0 */ /* finish computing H */ for (j=0; j0) { temp =0; for (k=0; k0) { <> } <> } @ The most complicated part of the code is the update of the per subject influence matrix $U$. The influence for a subject is the derivative of the current estimates wrt the case weight of that subject. Since $p$ is a vector the influence $U$ is easily represented as a matrix with one row per subject and one column per state. Refer to equation \eqref{ci} for the derivation. Let $m$ and $n$ be the old and new states for subject $i$, and $n_m$ the sum of weights for all subjects at risk in state $m$. Then \begin{equation*} U_{ij}(t) = \sum_k \left[ U_{ik}(t-)H_{kj}\right] + p_m(t-)(I_{n=j} - H_{mj})/ n_m \end{equation*} \begin{enumerate} \item The first term above is simple matrix multiplication. \item The second adds a vector with mean zero. \end{enumerate} If standard errors are not needed we can skip this calculation. <>= /* Update U, part 1 U = U %*% H -- matrix multiplication */ for (j=0; j>= /* Finally, update chaz and p. */ for (j=0; j>= /* store into the matrices that will be passed back */ for (j=0; j0) { temp =0; for (k=0; k 1) for (k=0; k>= /* return a list */ UNPROTECT(3); return(rlist); @ \subsubsection{Printing and plotting} The \code{survfitms} class differs from a \code{survfit}, but many of the same methods nearly apply. <>= # Methods for survfitms objects <> <> @ The subscript method is a near copy of that for survfit objects, but with a slightly different set of components. The object could have strata and will almost always have multiple columns. Following convention, if there is only one subscript we treat the object as though it were a vector. The \code{nmatch} function allow the user to use either names or integer indices. <>= "[.survfitms" <- function(x, ..., drop=FALSE) { nmatch <- function(i, target) { # This function lets R worry about character, negative, # or logical subscripts # It always returns a set of positive integer indices temp <- seq(along.with=target) names(temp) <- target temp[i] } if (!is.null(x$influence.pstate) || !is.null(x$influence.cumhaz)) x <- survfit0(x, x$start.time) # make influence and pstate align ndots <- ...length() # the simplest, but not avail in R 3.4 # ndots <- length(list(...))# fails if any are missing, e.g. fit[,2] # ndots <- if (missing(drop)) nargs()-1 else nargs()-2 # a workaround dd <- dim(x) dmatch <- match(c("strata", "data", "states"), names(dd), nomatch=0) if (is.null(x$states)) stop("survfitms object has no states component") if (dmatch[3]==0) stop ("survfitms object has no states dimension") dtype <- match(names(dd), c("strata", "data", "states")) if (ndots==0) return(x) # no subscript given if (ndots >0 && !missing(..1)) i <- ..1 else i <- NULL if (ndots> 1 && !missing(..2)) j <- ..2 else j <- NULL if (ndots> 2 && !missing(..3)) k <- ..3 else k <- NULL if (is.null(i) & is.null(j) & is.null(k)) return(x) # only one curve # Make a new object newx <- vector("list", length(x)) names(newx) <- names(x) for (kk in c("logse", "version", "conf.int", "conf.type", "type", "start.time", "call")) if (!is.null(x[[kk]])) newx[[kk]] <- x[[kk]] newx$transitions <- NULL # may no longer be accurate, and not needed class(newx) <- class(x) # Like a matrix, let the user use a single subscript if they desire if (ndots==1 && length(dd) > 1) { # the 'treat it as a vector' case if (!is.numeric(i)) stop("single subscript must be numeric") if (any(dmatch==2)) stop("single index subscripts are not supported for a survfit objet with both data and state dimesions") # when subscripting a mix, these don't endure newx$cumhaz <- newx$std.chaz <- newx$influence.chaz <- NULL newx$transitions <- newx$states <- newx$newdata <- NULL # what strata and columns do I need? itemp <- matrix(1:prod(dd), nrow=dd[1]) jj <- (col(itemp))[i] # columns ii <- (row(itemp))[i] # this is now the strata id if (dtype[1]!=1 || dd[1]==1) # no strata or only 1 irow <- rep(seq(along.with= x$time), length(ii)) else { itemp2 <- split(1:sum(x$strata), rep(1:length(x$strata), x$strata)) irow <- unlist(itemp2[ii]) # rows of the pstate object } inum <- x$strata[ii] # number of rows in each ii indx <- cbind(irow, rep(jj,ii)) # matrix index for pstate # The n.risk, n.event, .. matrices dont have a newdata dimension. if (all(dtype!=2) || dd["data"]==1) kk <- jj else { # both data and states itemp <- matrix(1:(dd["data"]*dd["states"]), nrow=dd[2]) kk <- (col(itemp))[jj] # the state of each selected one indx2 <- cbind(irow, rep(k, irow)) } newx$n <- x$n[ii] newx$time <- x$time[irow] for (z in c("n.risk", "n.event", "n.censor", "n.enter")) if (!is.null(x[[z]])) newx[[z]] <- (x[[z]])[indx2] for (z in c("pstate", "std.err", "upper", "lower")) if (!is.null(x[[z]])) newx[[z]] <- (x[[z]])[indx] newx$strata <- x$strata[ii] names(newx$strata) <- seq(along.with=ii) return(newx) } # not a single subscript, i.e., the usual case # Backwards compatability: If x$strata=NULL, it is a semantic argument # of whether there is still "1 stratum". I have used the second # form at times, e.g. x[1,,2] for an object with only data and state # dimensions. # If there are no strata, 1 too many subscripts, and the first is 1, # assume this case and toss the first if (ndots == (length(dd)+1)) { if (is.null(x$strata) && (is.null(i) || (length(i)==1 && i==1))) { i <-j; j <-k; k <- NULL } else stop("incorrect number of dimensions") } else if (ndots != length(dd)) stop("incorrect number of dimensions") # create irow, which selects for the time dimension of x if (dtype[1]!=1 || is.null(i)) { irow <- seq(along.with= x$time) } else { i <- nmatch(i, names(x$strata)) itemp <- split(1:sum(x$strata), rep(1:length(x$strata), x$strata)) irow <- unlist(itemp[i]) # rows of the pstate object } # Select the n, strata, and time components of the output. Make j,k # point to the subscripts other than strata (makes later code a touch # simpler.) newx$time <- x$time[irow] if (dtype[1] !=1) { # there are no strata newx$n <- x$n k <- j; j <- i; dd <- c(0, dd) dtype <- c(1, dtype) } else { # there are strata if (is.null(i)) i <-seq(along.with=x$strata) if ((drop && length(i)>1) || !drop) newx$strata <- x$strata[i] newx$n <- x$n[i] } # The n.censor and n.enter values do not repeat with multiple X values for (z in c("n.censor", "n.enter")) if (!is.null(x[[z]])) newx[[z]] <- (x[[z]])[irow, drop=FALSE] # two cases: with newx or without newx (pstate is always present) nstate <- length(x$states) if (dtype[2] !=2) { # j indexes the states, there is no data dimension if (is.null(j)) j <- seq.int(nstate) else j <- nmatch(j, x$states) # keep these as start points for plotting, even though they won't make # true sense if states are subset, since rows won't sum to 1 if (!is.null(x$p0)) { if (is.matrix(x$p0)) newx$p0 <- x$p0[i,j, drop=FALSE] else newx$p0 <- x$p0[j] } if (!is.null(x$sp0)) { if (is.matrix(x$sp0)) newx$sp0 <- x$sp0[i,j, drop=FALSE] else newx$sp0 <- x$sp0[j] } # in the rare case of a single strata with 1 obs, don't drop dims if (length(irow)==1 && length(j) > 1) drop2 <- FALSE else drop2 <- drop for (z in c("n.risk", "n.event")) if (!is.null(x[[z]])) newx[[z]] <- (x[[z]])[irow,j, drop=drop2] for (z in c("pstate", "std.err", "upper", "lower")) if (!is.null(x[[z]])) newx[[z]] <- (x[[z]])[irow,j, drop=drop2] if (!is.null(x$influence.pstate)) { if (is.list(x$influence.pstate)) { if (length(i)==1) newx$influence.pstate <- x$influence.pstate[[i]] else newx$influence.pstate <- lapply(x$influence.pstate[i], function(x) x[,,j, drop= drop]) } else newx$influence.pstate <- x$influence.pstate[,,j, drop=drop] } if (length(j)== nstate && all(j == seq.int(nstate))) { # user kept all the states, in original order newx$states <- x$states for (z in c("cumhaz", "std.chaz")) if (!is.null(x[[z]])) newx[[z]] <- (x[[z]])[irow,, drop=drop2] if (!is.null(x$influence.chaz)) { if (is.list(x$influence.chaz)) { newx$influence.chaz <- x$influence.chaz[i] if (length(i)==1 && drop) newx$influence.chaz <- x$influence.chaz[[i]] } else newx$influence.chaz <- x$influence.chaz } } else { # Some states were dropped, leaving no consistent way to # subscript cumhaz, or not one I have yet seen clearly # So remove it from the object newx$cumhaz <- newx$std.chaz <- newx$influence.chaz <- NULL if (length(j)==1 & drop) { newx$states <- NULL temp <- class(newx) class(newx) <- temp[temp!="survfitms"] } else newx$states <- x$states[j] } } else { # j points at newdata, k points at states if (is.null(j)) j <- seq.int(dd[2]) else j <- nmatch(j, seq.int(dd[2])) if (is.null(k)) k <- seq.int(nstate) else k <- nmatch(k, x$states) # keep these as start points for plotting, even though they won't make # true sense is states are subset, since rows won't sum to 1 # (all data= sets have the same p0) if (!is.null(x$p0)) { if (is.matrix(x$p0)) newx$p0 <- x$p0[i,k] else newx$p0 <- x$p0[k] } if (!is.null(x$sp0)) { if (is.matrix(x$sp0)) newx$sp0 <- x$p0[i,k] else newx$sp0 <- x$sp0[k] } if (length(irow)==1) { if (length(j) > 1) drop2 <- FALSE else drop2<- drop if (length(k) > 1) drop3 <- FALSE else drop3 <- drop } else drop2 <- drop3 <- drop for (z in c("n.risk", "n.event")) if (!is.null(x[[z]])) newx[[z]] <- (x[[z]])[irow, k, drop=drop3] for (z in c("pstate", "std.err", "upper", "lower")) if (!is.null(x[[z]])) newx[[z]] <- (x[[z]])[irow,j,k, drop=drop2] if (!is.null(x$influence.pstate)) { if (is.list(x$influence.pstate)) { if (length(i)==1) newx$influence.pstate <- (x$influence.pstate[[i]])[,,j,k, drop=drop] else newx$influence.pstate <- lapply(x$influence.pstate[i], function(x) x[,,j,k, drop= drop]) } else newx$influence.pstate <- x$influence.pstate[,,j,k, drop=drop] } if (length(k)== nstate && all(k == seq.int(nstate))) { # user kept all the states newx$states <- x$states for (z in c("cumhaz", "std.chaz")) if (!is.null(x[[z]])) newx[[z]] <- (x[[z]])[irow,j,, drop=drop2] if (!is.null(x$influence.chaz)) { if (is.list(x$influence.chaz)) { newx$influence.chaz <- (x$influence.chaz[i])[,j,] if (length(i)==1 && drop) newx$influence.chaz <- x$influence.chaz[[i]] } else newx$influence.chaz <- x$influence.chaz[,j,] } } else { # never drop the states component. Otherwise downstream code # will start looking for x$surv instead of x$pstate newx$states <- x$states[k] newx$cumhaz <- newx$std.chaz <- newx$influence.chaz <- NULL x$transitions <- NULL } if (length(j)==1 && drop) newx$newdata <- NULL else newx$newdata <- x$newdata[j,,drop=FALSE] #newdata is a data frame } newx } @ The summary.survfit and summary.survfitms functions share a significant amount of code. One part of the code that once was subtle is dealing with intermediate time points; the findInterval function in base R has made that much easier. Since the result does not involve interpolation, one should be able to create a special index vector i and return \code{time[i]}, \code{surv[i,]}, etc, to subscript all the curves in a survfit object at once. But that approach, though efficient in theory, runs into two problems. First is the extrapolated value for the curves at time points before the first event, which is allowed to be different for different curves in survfitms objects. The second is that there is interpolation of a sort: the n.event and n.censor components are summed over intervals when the selected time points are sparse, and that process is very tricky for multiple curves at once. At one point the code took that approach, but it became too complex to maintain. The current approach is slower but more transparent: do the individual curves one by one, then paste together the results. <>= summary.survfit <- function(object, times, censored=FALSE, scale=1, extend=FALSE, rmean=getOption('survfit.rmean'), ...) { fit <- object #save typing if (!inherits(fit, 'survfit')) stop("summary.survfit can only be used for survfit objects") if (is.null(fit$logse)) fit$logse <- TRUE #older style # The print.rmean option is depreciated, it is still listened # to in print.survfit, but ignored here if (is.null(rmean)) rmean <- "common" if (is.numeric(rmean)) { if (is.null(fit$start.time)) { if (rmean < min(fit$time)) stop("Truncation point for the mean time in state is < smallest survival") } else if (rmean < fit$start.time) stop("Truncation point for the mean time in state is < smallest survival") } else { rmean <- match.arg(rmean, c('none', 'common', 'individual')) if (length(rmean)==0) stop("Invalid value for rmean option") } # adding time 0 makes the mean and median easier fit0 <- survfit0(fit, fit$start.time) #add time 0 temp <- survmean(fit0, scale=scale, rmean) table <- temp$matrix #for inclusion in the output list rmean.endtime <- temp$end.time if (!is.null(fit$strata)) { nstrat <- length(fit$strata) } delta <- function(x, indx) { # sums between chosen times if (is.logical(indx)) indx <- which(indx) if (!is.null(x) && length(indx) >0) { fx <- function(x, indx) diff(c(0, c(0, cumsum(x))[indx+1])) if (is.matrix(x)) { temp <- apply(x, 2, fx, indx=indx) # don't return a vector when only 1 time point is given if (is.matrix(temp)) temp else matrix(temp, nrow=1) } else fx(x, indx) } else NULL } if (missing(times)) { <> } else { fit <- fit0 <> times <- sort(times) #in case the user forgot if (is.null(fit$strata)) fit <- findrow(fit, times, extend) else { ltemp <- vector("list", nstrat) for (i in 1:nstrat) ltemp[[i]] <- findrow(fit[i], times, extend) fit <- unpacksurv(fit, ltemp) } } # finish off the output structure fit$table <- table if (length(rmean.endtime)>0 && !any(is.na(rmean.endtime[1]))) fit$rmean.endtime <- rmean.endtime # A survfit object may contain std(log S) or std(S), summary always std(S) if (!is.null(fit$std.err) && fit$logse) fit$std.err <- fit$std.err * fit$surv # Expand the strata if (!is.null(fit$strata)) fit$strata <- factor(rep(1:nstrat, fit$strata), 1:nstrat, labels= names(fit$strata)) if (scale != 1) { # fix scale in the output fit$time <- fit$time/scale } class(fit) <- "summary.survfit" fit } @ The simple case of no times argument. <>= if (!censored) { index <- (rowSums(as.matrix(fit$n.event)) >0) for (i in c("time","n.risk", "n.event", "surv", "pstate", "std.err", "upper", "lower", "cumhaz", "std.chaz")) { if (!is.null(fit[[i]])) { # not all components in all objects temp <- fit[[i]] if (is.matrix(temp)) temp <- temp[index,,drop=FALSE] else if (!is.array(temp)) temp <- temp[index] #simple vector else temp <- temp[index,,, drop=FALSE] # 3 way fit[[i]] <- temp } } # The n.enter and n.censor values are accumualated # both of these are simple vectors if (is.null(fit$strata)) { for (i in c("n.enter", "n.censor")) if (!is.null(fit[[i]])) fit[[i]] <- delta(fit[[i]], index) } else { sindx <- rep(1:nstrat, fit$strata) for (i in c("n.enter", "n.censor")) { if (!is.null(fit[[i]])) fit[[i]] <- unlist(sapply(1:nstrat, function(j) delta(fit[[i]][sindx==j], index[sindx==j]))) } # the "factor" is needed for the case that a strata has no # events at all, and hence 0 lines of output fit$strata[] <- as.vector(table(factor(sindx[index], 1:nstrat))) } } #if missing(times) and censored=TRUE, the fit object is ok as it is @ To deal with selected times we first define a subscripting function. For indices of 0, which are requested times that are before the first event, it fills in the initial value. <>= ssub<- function(x, indx) { #select an object and index if (!is.null(x) && length(indx)>0) { if (is.matrix(x)) x[pmax(1,indx),,drop=FALSE] else if (is.array(x)) x[pmax(1,indx),,,drop=FALSE] else x[pmax(1, indx)] } else NULL } @ This function does the real work, for any single curve. The default value for init is correct for survival curves. Say that the data has values at time 5, 10, 15, 20 \ldots, and a user asks for \code{times=c(7, 15, 20, 30)}. In the input object \code{n.risk} refers to the number at risk just before time 5, 10, \ldots; it is a left-continuous function. The survival is a right-continuous function. So at time 7 we want to take the survival from time 5 and number at risk from time 10; \code{indx1} will be the right-continuous index and \code{indx2} the left continuous one. The value of n.risk at time 30 has to be computed. For counts of events, censoring, and entry we want to know the total number that happened during the intervals of 0-7, 7-15, 15-20 and 20-30. Technically censorings at time 15 happen just after time 15 so would go into the third line of the report. However, this would lead to terrible confusion for the user since using \code{times=c(5, 10, 15, 20)} would lead to different counts than a call that did not contain the times argument, so all 3 of the intermediates are computed using indx1. A report at time 30 is made only if extend=TRUE, in which case we need to compute a tail value for n.risk. <>= findrow <- function(fit, times, extend) { if (FALSE) { if (is.null(fit$start.time)) mintime <- min(fit$time, 0) else mintime <- fit$start.time ptimes <- times[times >= mintime] } else ptimes <- times[is.finite(times)] if (!extend) { maxtime <- max(fit$time) ptimes <- ptimes[ptimes <= maxtime] } ntime <- length(fit$time) index1 <- findInterval(ptimes, fit$time) index2 <- 1 + findInterval(ptimes, fit$time, left.open=TRUE) if (length(index1) ==0) stop("no points selected for one or more curves, consider using the extend argument") # The pmax() above encodes the assumption that n.risk for any # times before the first observation = n.risk at the first obs fit$time <- ptimes for (i in c("surv", "pstate", "upper", "lower", "std.err", "cumhaz", "std.chaz")) { if (!is.null(fit[[i]])) fit[[i]] <- ssub(fit[[i]], index1) } if (is.matrix(fit$n.risk)) { # Every observation in the data has to end with a censor or event. # So by definition the number at risk after the last observed time # value must be 0. fit$n.risk <- rbind(fit$n.risk,0)[index2,,drop=FALSE] } else fit$n.risk <- c(fit$n.risk, 0)[index2] for (i in c("n.event", "n.censor", "n.enter")) fit[[i]] <- delta(fit[[i]], index1) fit } # For a single component, turn it from a list into a single vector, matrix # or array unlistsurv <- function(x, name) { temp <- lapply(x, function(x) x[[name]]) if (is.vector(temp[[1]])) unlist(temp) else if (is.matrix(temp[[1]])) do.call("rbind", temp) else { # the cumulative hazard is the only component that is an array # it's third dimension is n xx <- unlist(temp) dd <- dim(temp[[1]]) dd[3] <- length(xx)/prod(dd[1:2]) array(xx, dim=dd) } } # unlist all the components built by a set of calls to findrow # and remake the strata unpacksurv <- function(fit, ltemp) { keep <- c("time", "surv", "pstate", "upper", "lower", "std.err", "cumhaz", "n.risk", "n.event", "n.censor", "n.enter", "std.chaz") for (i in keep) if (!is.null(fit[[i]])) fit[[i]] <- unlistsurv(ltemp, i) fit$strata[] <- sapply(ltemp, function(x) length(x$time)) fit } @ Repeat the code for survfitms objects. The only real difference is the preservation of \code{pstate} and \code{cumhaz} instead of \code{surv}, and the use of survmean2. <>= summary.survfitms <- function(object, times, censored=FALSE, scale=1, extend=FALSE, rmean= getOption("survfit.rmean"), ...) { fit <- object # save typing if (!inherits(fit, 'survfitms')) stop("summary.survfitms can only be used for survfitms objects") if (is.null(fit$logse)) fit$logse <- FALSE # older style # The print.rmean option is depreciated, it is still listened # to in print.survfit, but ignored here if (is.null(rmean)) rmean <- "common" if (is.numeric(rmean)) { if (is.null(fit$start.time)) { if (rmean < min(fit$time)) stop("Truncation point for the mean is < smallest survival") } else if (rmean < fit$start.time) stop("Truncation point for the mean is < smallest survival") } else { rmean <- match.arg(rmean, c('none', 'common', 'individual')) if (length(rmean)==0) stop("Invalid value for rmean option") } fit0 <- survfit0(fit, fit$start.time) # add time 0 temp <- survmean2(fit0, scale=scale, rmean) table <- temp$matrix #for inclusion in the output list rmean.endtime <- temp$end.time if (!missing(times)) { if (!is.numeric(times)) stop ("times must be numeric") times <- sort(times) } if (!is.null(fit$strata)) { nstrat <- length(fit$strata) sindx <- rep(1:nstrat, fit$strata) } delta <- function(x, indx) { # sums between chosen times if (is.logical(indx)) indx <- which(indx) if (!is.null(x) && length(indx) >0) { fx <- function(x, indx) diff(c(0, c(0, cumsum(x))[indx+1])) if (is.matrix(x)) { temp <- apply(x, 2, fx, indx=indx) if (is.matrix(temp)) temp else matrix(temp, nrow=1) } else fx(x, indx) } else NULL } if (missing(times)) { <> } else { fit <-fit0 # easier to work with <> times <- sort(times) if (is.null(fit$strata)) fit <- findrow(fit, times, extend) else { ltemp <- vector("list", nstrat) for (i in 1:nstrat) ltemp[[i]] <- findrow(fit[i,], times, extend) fit <- unpacksurv(fit, ltemp) } } # finish off the output structure fit$table <- table if (length(rmean.endtime)>0 && !any(is.na(rmean.endtime))) fit$rmean.endtime <- rmean.endtime if (!is.null(fit$strata)) fit$strata <- factor(rep(names(fit$strata), fit$strata)) # A survfit object may contain std(log S) or std(S), summary always std(S) if (!is.null(fit$std.err) && fit$logse) fit$std.err <- fit$std.err * fit$surv if (scale != 1) { # fix scale in the output fit$time <- fit$time/scale } class(fit) <- "summary.survfitms" fit } <> <> @ Printing for a survfitms object is different than for a survfit one. The big difference is that I don't have an estimate of the median, or any other quantile for that matter. Mean time in state makes sense, but I don't have a standard error for it at the moment. The other is that there is usually a mismatch between the n.event matrix and the n.risk matrix. The latter has all the states that were possible whereas the former only has states with an arrow pointing in. We need to manufacture the 0 events for the other states. <>= print.survfitms <- function(x, scale=1, rmean = getOption("survfit.rmean"), ...) { if (!is.null(cl<- x$call)) { cat("Call: ") dput(cl) cat("\n") } omit <- x$na.action if (length(omit)) cat(" ", naprint(omit), "\n") x <- survfit0(x, x$start.time) if (is.null(rmean)) rmean <- "common" if (is.numeric(rmean)) { if (is.null(x$start.time)) { if (rmean < min(x$time)) stop("Truncation point for the mean is < smallest survival") } else if (rmean < x$start.time) stop("Truncation point for the mean is < smallest survival") } else { rmean <- match.arg(rmean, c('none', 'common', 'individual')) if (length(rmean)==0) stop("Invalid value for rmean option") } temp <- survmean2(x, scale=scale, rmean) if (is.null(temp$end.time)) print(temp$matrix, ...) else { etime <- temp$end.time dd <- dimnames(temp$matrix) cname <- dd[[2]] cname[length(cname)] <- paste0(cname[length(cname)], '*') dd[[2]] <- cname dimnames(temp$matrix) <- dd print(temp$matrix, ...) if (length(etime) ==1) cat(" *restricted mean time in state (max time =", format(etime, ...), ")\n") else cat(" *restricted mean time in state (per curve cutoff)\n") } invisible(x) } @ This part of the computation is set out separately since it is called by both print and summary. <>= survmean2 <- function(x, scale=1, rmean) { nstate <- length(x$states) #there will always be at least 1 state ngrp <- max(1, length(x$strata)) if (is.null(x$newdata)) ndata <- 0 else ndata <- nrow(x$newdata) if (ngrp >1) { igrp <- rep(1:ngrp, x$strata) rname <- names(x$strata) } else { igrp <- rep(1, length(x$time)) rname <- NULL } # The n.event matrix may not have nstate columms. Its # colnames are the first elements of states, however if (is.matrix(x$n.event)) { nc <- ncol(x$n.event) nevent <- tapply(x$n.event, list(rep(igrp, nc), col(x$n.event)), sum) dimnames(nevent) <- list(rname, x$states[1:nc]) } else { nevent <- tapply(x$n.event, igrp, sum) names(nevent) <- rname } if (ndata< 2) { outmat <- matrix(0., nrow=nstate*ngrp , ncol=2) outmat[,1] <- rep(x$n, nstate) outmat[1:length(nevent), 2] <- c(nevent) if (ngrp >1) rowname <- c(outer(rname, x$states, paste, sep=", ")) else rowname <- x$states } else { outmat <- matrix(0., nrow=nstate*ndata*ngrp, ncol=2) outmat[,1] <- rep(x$n, nstate*ndata) outmat[, 2] <- rep(c(nevent), each=ndata) temp <- outer(1:ndata, x$states, paste, sep=", ") if (ngrp >1) rowname <- c(outer(rname, temp, paste, sep=", ")) else rowname <- temp nstate <- nstate * ndata } # Caculate the mean time in each state if (rmean != "none") { if (is.numeric(rmean)) maxtime <- rep(rmean, ngrp) else if (rmean=="common") maxtime <- rep(max(x$time), ngrp) else maxtime <- tapply(x$time, igrp, max) meantime <- matrix(0., ngrp, nstate) if (!is.null(x$influence)) stdtime <- meantime for (i in 1:ngrp) { # a 2 dimensional matrix is an "array", but a 3-dim array is # not a "matrix", so check for matrix first. if (is.matrix(x$pstate)) temp <- x$pstate[igrp==i,, drop=FALSE] else if (is.array(x$pstate)) temp <- matrix(x$pstate[igrp==i,,,drop=FALSE], ncol= nstate) else temp <- matrix(x$pstate[igrp==i], ncol=1) tt <- x$time[igrp==i] # Now cut it off at maxtime delta <- diff(c(tt[tt nrow(temp)) delta <- delta[1:nrow(temp)] if (length(delta) < nrow(temp)) delta <- c(delta, rep(0, nrow(temp) - length(delta))) meantime[i,] <- colSums(delta*temp) if (!is.null(x$influence)) { # calculate the variance if (is.list(x$influence)) itemp <- apply(x$influence[[i]], 1, function(x) colSums(x*delta)) else itemp <- apply(x$influence, 1, function(x) colSums(x*delta)) stdtime[i,] <- sqrt(rowSums(itemp^2)) } } outmat <- cbind(outmat, c(meantime)/scale) cname <- c("n", "nevent", "rmean") if (!is.null(x$influence)) { outmat <- cbind(outmat, c(stdtime)/scale) cname <- c(cname, "std(rmean)") } # report back a single time, if there is only one if (all(maxtime == maxtime[1])) maxtime <- maxtime[1] } else cname <- c("n", "nevent") dimnames(outmat) <- list(rowname, cname) if (rmean=='none') list(matrix=outmat) else list(matrix=outmat, end.time=maxtime/scale) } @ \section{Matrix exponentials and transition matrices} For multi-state models, we need to compute the exponential of the transition matrix, sometimes many times. The matrix exponential is formally defined as \begin{equation*} \exp(R) = I + \sum_{j=1}^\infty R^i/i! \end{equation*} The computation is nicely solved by the expm package \emph{if} we didn't need derivatives and/or high speed. We want both. For the package there are three cases: \begin{enumerate} \item If there is only one departure state, then there is a fast closed form solution, shown below. This case occurs whenever an event time is unique, i.e., no other event times are tied with this one. This always holds for competing risk models. \item If the rate matrix $R$ is upper triangular and the (non-zero) diagonal elements are distinct, there is a fast matrix decomposition algorithm. If the transition matrix is acylic then it can be rearranged to be in upper triangular form. The decomposition also gives a simple expression for the derivative. \item In the general case we use a Pade-Laplace algorithm: the same found in the matexp package. \end{enumerate} For a rate matrix $R$, $R_{jk}$ is the rate of transition from state $j$ to state $k$, and is itself an exponential $R_{jk} = \exp(\eta_{jk})$. Thus all non-diagonal values must be $/ge 0$. Transitions that do not occur have rate 0. The diagonal element is determined by the constraint that row sums are 0. Let $A= \exp(R)$. Also be aware that $\exp(A)\exp(B) \ne \exp(A+B)$ for the case of matrices. If there is only one non-zero diagonal element, $R_{jj}$ say, then \begin{align*} A_{jj} &= e^{R_{jj}} \\ A_{jk} &= \left(1- e^{R_{jj}}\right) \frac{R_{jk}}/{\sum_{l\ne j} R_{jl}} \\ A_{kk} &= 1; k\ne j \end{align*} and all other elements of $A$ are zero. The derivative of $A$ with respect to $\eta_{jk}$ will be 0 for all rows except row $j$. \begin{align*} \frac{\partial A_{jj}}{\partial \eta_{jk}} &= \frac{\partial \exp(-\sum_{k!=j} \eta_{jk})}{\partial \eta_{jk}} \\ &= -\eta_{jk} A_{jj} \\ \frac{\partial A_{jk}}{\partial \eta_{jk}} &= eta_{jk}A_{jj} \;\mbox{single event type} \\ \frac{\partial A_{jk}}{\partial \eta_{jm}}&= A_{jj} eta_{jm}\frac{R_{jm}}{\sum_{l\ne j} R_{jl}} + (A_{jj} -1) \frac{\eta_{jm} (1- \sum_{l\ne j} R_{jl})}{(\sum_{l\ne j} R_{jl})^2} \end{align*} If time is continuous then most events will be at a unique event time, and this fast computation will be the most common case. If the state space is acylic, the case for many survival problems, then we can reorder the states so that R is upper triangular. In that case, the diagonal elements of R are the eigenvalues. If these are unique (ignoring the zeros), then an algorithm of Kalbfleisch and Lawless gives both A and the derivatives of A in terms of a matrix decomposition. For the remaining cases use the Pade' approximation as found in the matexp package. The overall stategy is the following: \begin{enumerate} \item Call \code{survexpmsetup} once, which will decide if the matrix is acyclic, and return a reorder vector if so or a flag if it is not. This determination is based on the possible transitions, e.g., on the transitions matrix from survcheck. \item Call \code{survexpm} for each individual transition matrix. In that routine \begin{itemize} \item First check for the simple case, otherwise \item Do not need derivatives: call survexpm \item Do need derivatives \begin{itemize} \item If upper triangular and no tied values, use the deriv routine \item Otherwise use the Pade routine \end{itemize} \end{itemize} \end{enumerate} <>= survexpmsetup <- function(rmat) { # check the validity of the transition matrix, and determine if it # is acyclic, i.e., can be reordered into an upper triangular matrix. if (!is.matrix(rmat) || nrow(rmat) != ncol(rmat) || any(diag(rmat) > 0) || any(rmat[row(rmat) != col(rmat)] < 0)) stop ("input is not a transition matrix") if (!is.logical(all.equal(rowSums(rmat), rep(0, ncol(rmat))))) stop("input is not a transition matrix") nc <- ncol(rmat) lower <- row(rmat) > col(rmat) if (all(rmat[lower] ==0)) return(0) # already in order # score each state by (number of states it follows) - (number it precedes) temp <- 1*(rmat >0) # 0/1 matrix indx <- order(colSums(temp) - rowSums(temp)) temp <- rmat[indx, indx] # try that ordering if (all(temp[lower]== 0)) indx # it worked! else -1 # there is a loop in the states } @ \subsection{Decompostion} Based on Kalbfleisch and Lawless, ``The analysis of panel data under a Markov assumption'' (J Am Stat Assoc, 1985:863-871), the rate matrix $R$ can be written as $ADA^{-1}$ for some matrix $A$, where $D$ is a diagonal matrix of eigenvalues, provided all of the eigenvalues are distinct. Then $R^k = A D^k A^{-1}$, and using the definition of a matrix exponential we see that $\exp(R) = A \exp(D) A^{-1}$. The exponential of a diagonal matrix is simply a diagonal matrix of the exponentials. The matrix $Rt$ for a scalar $t$ has decomposition $A\exp(Dt)A^{-1}$; a single decompostion suffices for all values of $t$. A particular example is \begin{equation} R = \begin{pmatrix} r_{11} & r_{12} & r_{13} & 0 & 0 & r_{15}\\ 0 & r_{22} & 0 & r_{24} & 0 & r_{25}\\ 0 & 0 & r_{33} & r_{34} & r_{35} & r_{35}\\ 0 & 0 & 0 & r_{44} & r_{45} & r_{45} \\ 0 & 0 & 0 & 0 & r_{55} & r_{55} \\ 0 & 0 & 0 & 0 & 0 & 0 \end{pmatrix}. \end{equation} Since this is a transition matrix the diagonal elements are constrained so that row sums are zero: $r_{ii} = -\sum_{j\ne i} r_{ij}$. Since R is an upper triangular matrix it's eigenvalues lie on the diagonal. If none of the the eigenvalues are repeated, then the Prentice result applies. The decompostion is quite simple since $R$ is triangular. We want the eigenvectors, i.e. solutions to \begin{align*} R v_i &= r_{ii} v_i \\ % R v_2 &= r_{22} v_2 \\ % R v_3 &= r_{33} v_3 \\ % R v_4 &= r_{44} v_4 \\ % R v_5 &= r_{55} v_5 \\ % R v_6 &= r_{66} v_6 \end{align*} for $i= 1, \dots, 6$, where $v_i$ are the colums of $V$. It turns out that the set of eigenvectors is also upper triangular; we can solve for them one by one using back substitution. For the first eigenvector we have $v_1 = (1, 0,0,0,0,0)$. For the second we have the equations \begin{align*} r_{11} x + r_{12}y &= r_{22} x \\ r_{22}y &= r_{22} y \end{align*} which has the solution $(r_{12}/(r_{22}- r_{11}), 1, 0,0,0,0)$, and the process recurs for other rows. Since $V$ is triangular the inverse of $V$ is upper triangular and also easy to compute. This approach fails if there are tied eigenvalues. Kalbfleice and Lawless comment that this case is rare, but one can then use a decomposition to Jordan canonical form re Cox and Miller, the Theory of Stochastic Processes, 1965. Although this leads to some nice theorems it does not give a simple comutational form, however, and it is easier to fall back on the pade routine. At this time, the pade routine is as fast as the triangluar code, at least for small matrices without deriviatives. <>= survexpm <- function(rmat, time=1.0, setup, eps=1e-6) { # rmat is a transition matrix, so the diagonal elements are 0 or negative if (length(rmat)==1) exp(rmat[1]*time) #failsafe -- should never be called else { nonzero <- (diag(rmat) != 0) if (sum(nonzero ==0)) diag(nrow(rmat)) # expm(0 matrix) = identity if (sum(nonzero) ==1) { j <- which(nonzero) emat <- diag(nrow(rmat)) temp <- exp(rmat[j,j] * time) emat[j,j] <- temp emat[j, -j] <- (1-temp)* rmat[j, -j]/sum(rmat[j,-j]) emat } else if (missing(setup) || setup[1] < 0 || any(diff(sort(diag(rmat)))< eps)) pade(rmat*time) else { if (setup[1]==0) .Call(Ccdecomp, rmat, time)$P else { temp <- rmat temp[setup, setup] <- .Call(Ccdecomp, rmat[setup, setup], time) temp$P } } } } @ The routine below is modeled after the cholesky routines in the survival library. To help with notation, the return values are labeled as in the Kalbfleisch and Lawless paper, except that their Q = our rmat. Q = A diag(d) Ainv and P= exp(Qt) <>= /* ** Compute the eigenvectors for the upper triangular matrix R */ #include #include "R.h" #include "Rinternals.h" SEXP cdecomp(SEXP R2, SEXP time2) { int i,j,k; int nc, ii; static const char *outnames[]= {"d", "A", "Ainv", "P", ""}; SEXP rval, stemp; double *R, *A, *Ainv, *P; double *dd, temp, *ediag; double time; nc = ncols(R2); /* number of columns */ R = REAL(R2); time = asReal(time2); /* Make the output matrices as copies of R, so as to inherit ** the dimnames and etc */ PROTECT(rval = mkNamed(VECSXP, outnames)); stemp= SET_VECTOR_ELT(rval, 0, allocVector(REALSXP, nc)); dd = REAL(stemp); stemp = SET_VECTOR_ELT(rval, 1, allocMatrix(REALSXP, nc, nc)); A = REAL(stemp); for (i =0; i< nc*nc; i++) A[i] =0; /* R does not zero memory */ stemp = SET_VECTOR_ELT(rval, 2, duplicate(stemp)); Ainv = REAL(stemp); stemp = SET_VECTOR_ELT(rval, 3, duplicate(stemp)); P = REAL(stemp); ediag = (double *) R_alloc(nc, sizeof(double)); /* ** Compute the eigenvectors ** For each column of R, find x such that Rx = kx ** The eigenvalue k is R[i,i], x is a column of A ** Remember that R is in column order, so the i,j element is in ** location i + j*nc */ ii =0; /* contains i * nc */ for (i=0; i=0; j--) { /* fill in the rest */ temp =0; for (k=j; k<=i; k++) temp += R[j + k*nc]* A[k +ii]; A[j +ii] = temp/(dd[i]- R[j + j*nc]); } ii += nc; } /* ** Solve for A-inverse, which is also upper triangular. The diagonal ** of A and the diagonal of A-inverse are both 1. At the same time ** solve for P = A D Ainverse, where D is a diagonal matrix ** with exp(eigenvalues) on the diagonal. ** P will also be upper triangular, and we can solve for it using ** nearly the same code as above. The prior block had RA = x with A the ** unknown and x successive colums of the identity matrix. ** We have PA = AD, so x is successively columns of AD. ** Imagine P and A are 4x4 and we are solving for the second row ** of P. Remember that P[2,1]= A[2,3] = A[2,4] =0; the equations for ** this row of P are: ** ** 0*A[1,2] + P[2,2]A[2,2] + P[2,3] 0 + P[2,4] 0 = A[2,2] D[2] ** 0*A[1,3] + P[2,2]A[2,3] + P[2,3]A[3,3] + P[2,4] 0 = A[2,3] D[3] ** 0*A[1,4] + P[2,2]A[2,4] + P[2,3]A[3,4] + P[2,4]A[4,4] = A[2,4] D[4] ** ** For A-inverse the equations are (use U= A-inverse for a moment) ** 0*A[1,2] + U[2,2]A[2,2] + U[2,3] 0 + U[2,4] 0 = 1 ** 0*A[1,3] + U[2,2]A[2,3] + U[2,3]A[3,3] + U[2,4] 0 = 0 ** 0*A[1,4] + U[2,2]A[2,4] + U[2,3]A[3,4] + U[2,4]A[4,4] = 0 */ ii =0; /* contains i * nc */ for (i=0; i=0; j--) { /* fill in the rest of the column*/ temp =0; for (k=j+1; k<=i; k++) temp += A[j + k*nc]* Ainv[k +ii]; Ainv[j +ii] = -temp; } /* column i of P */ P[i + ii] = ediag[i]; for (j=0; j>= derivative <- function(rmat, time, dR, setup, eps=1e-8) { if (missing(setup) || setup[1] <0 || any(diff(sort(diag(rmat)))< eps)) return (pade(rmat*time, dR*time)) if (setup==0) dlist <- .Call(Ccdecomp, rmat, time) else dlist <- .Call(Ccdecomp, rmat[setup, setup], time) ncoef <- dim(dR)[3] nstate <- nrow(rmat) dmat <- array(0.0, dim=c(nstate, nstate, ncoef)) vtemp <- outer(dlist$d, dlist$d, function(a, b) { ifelse(abs(a-b)< eps, time* exp(time* (a+b)/2), (exp(a*time) - exp(b*time))/(a-b))}) # two transitions can share a coef, but only for the same X variable for (i in 1:ncoef) { G <- dlist$Ainv %*% dR[,,i] %*% dlist$A V <- G*vtemp dmat[,,i] <- dlist$A %*% V %*% dlist$Ainv } dlist$dmat <- dmat # undo the reordering, if needed if (setup[1] >0) { indx <- order(setup) dlist <- list(P = dlist$P[indx, indx], dmat = apply(dmat,1:2, function(x) x[indx, indx])) } dlist } @ The Pade approximation is found in the file pade.R. There is a good discussion of the problem at www.maths.manchester.ac.uk/~higham/talks/exp09.pdf. The pade function copied code from the matexp package, which in turn is based on Higham 2005. Let B be a matrix and define \begin{eqnarray*} r_m(B) &= p(B)/q(B) \\ p(B) &= \sum_{j=0^m} \frac{((2m-j)! m!}{(2m)!(m-j)! j!} B^j \\ q(B) &= p(-B) \end{eqnarray*} The algorithm for calculating $\exp(A)$ is based on the following table \begin{center} \begin{tabular}{c|ccccc} $||A||_1$ & 0.15 & .25 & .95 & 2.1 & 3.4 \\ m & 3 & 5 & 7 & 9 & 13 \end{tabular} \end{center} The 1 norm of a matrix is \code{max(colSums(A))}. If the norm is $\le 3.4$ the $\exp(A) = r_m(A)$ using the table. Otherwise, find $s$ such that $B = A/2^s$ has norm $<=3.4$ and use the table method to find $\exp(B)$, then $\exp(A) \approx B^(2^s)$, the latter involves repeated squaring of the matrix. The expm code has a lot of extra steps whose job is to make sure that elements of $A$ are not too disparate in size. Transition matrices are nice and we can skip all of that. This makes the pade function conserably faster than the expm function from the Matrix library. In fact, if there aren't any tied event times, most elements of the rate matrix will be zero, and others are on the order of 1/(number at risk), so that $m=3$ is the most common outcome. \section{Plotting survival curves} This version of the curves uses the newer form of the survfit object, which fixes an original design decision that I now consider to have been a mistake. That is, an ordinary survival curve did not store the intial (time=0, S=1) point in the survfit object, leaving it up to plotting and/or printing routines to glue it back on. Later additions of delayed starting time and multi-state curves meant that I had to store those values anyway, sticking them into appended objects. The version3 survfit object puts the intial time back where it belongs, and makes this routine easier to write. The plot, lines, and points routines use several common code blocks in order to maintain consistency. The xmax argument has been a long term issue. Using xmax on a plot call, we would like that xmax to persist in a subsequent lines.survfit call. But, the problem with this is that lines might not be called after plot.survfit: someone might have other data and then want to add a survfit line to it (rare case I know). If we save the xlimits in some global object, there is no way to erase that object every time a high level call is made. <>= plot.survfit<- function(x, conf.int, mark.time=FALSE, pch=3, col=1,lty=1, lwd=1, cex=1, log=FALSE, xscale=1, yscale=1, xlim, ylim, xmax, fun, xlab="", ylab="", xaxs='r', conf.times, conf.cap=.005, conf.offset=.012, conf.type=c('log', 'log-log', 'plain', 'logit', "arcsin"), mark, noplot="(s0)", cumhaz=FALSE, firstx, ymin, ...) { dotnames <- names(list(...)) if (any(dotnames =='type')) stop("The graphical argument 'type' is not allowed") x <- survfit0(x, x$start.time) # align data at 0 for plotting <> <> <> <> <> <> <> type <- 's' <> invisible(lastx) } lines.survfit <- function(x, type='s', pch=3, col=1, lty=1, lwd=1, cex=1, mark.time=FALSE, xmax, fun, conf.int=FALSE, conf.times, conf.cap=.005, conf.offset=.012, conf.type=c('log', 'log-log', 'plain', 'logit', "arcsin"), mark, noplot="(s0)", cumhaz=FALSE, ...) { x <- survfit0(x, x$start.time) xlog <- par("xlog") <> <> <> <> # remember a prior xmax if (missing(xmax)) xmax <- getOption("plot.survfit")$xmax <> <> invisible(lastx) } points.survfit <- function(x, fun, censor=FALSE, col=1, pch, noplot="(s0)", cumhaz=FALSE, ...) { conf.int <- conf.times <- FALSE # never draw these with 'points' x <- survfit0(x, x$start.time) <> <> if (ncurve==1 || (length(col)==1 && missing(pch))) { if (censor) points(stime, ssurv, ...) else points(stime[x$n.event>0], ssurv[x$n.event>0], ...) } else { c2 <- 1 #cycles through the colors and characters col <- rep(col, length=ncurve) if (!missing(pch)) { if (length(pch)==1) pch2 <- rep(strsplit(pch, '')[[1]], length=ncurve) else pch2 <- rep(pch, length=ncurve) } for (j in 1:ncol(ssurv)) { for (i in unique(stemp)) { if (censor) who <- which(stemp==i) else who <- which(stemp==i & x$n.event >0) if (missing(pch)) points(stime[who], ssurv[who,j], col=col[c2], ...) else points(stime[who], ssurv[who,j], col=col[c2], pch=pch2[c2], ...) c2 <- c2+1 } } } } @ <>= # decide on logarithmic axes, yes or no if (is.logical(log)) { ylog <- log xlog <- FALSE if (ylog) logax <- 'y' else logax <- "" } else { ylog <- (log=='y' || log=='xy') xlog <- (log=='x' || log=='xy') logax <- log } if (!missing(fun)) { if (is.character(fun)) { if (fun=='log'|| fun=='logpct') ylog <- TRUE if (fun=='cloglog') { xlog <- TRUE if (ylog) logax <- 'xy' else logax <- 'x' } if (fun=="cumhaz" && missing(cumhaz)) cumhaz <- TRUE } } @ <>= # The default for plot and lines is to add confidence limits # if there is only one curve if (missing(conf.int) && missing(conf.times)) conf.int <- (!is.null(x$std.err) && prod(dim(x) ==1)) if (missing(conf.times)) conf.times <- NULL else { if (!is.numeric(conf.times)) stop('conf.times must be numeric') if (missing(conf.int)) conf.int <- TRUE } if (!missing(conf.int)) { if (is.numeric(conf.int)) { conf.level <- conf.int if (conf.level<0 || conf.level > 1) stop("invalid value for conf.int") if (conf.level ==0) conf.int <- FALSE else if (conf.level != x$conf.int) { x$upper <- x$lower <- NULL # force recomputation } conf.int <- TRUE } else conf.level = 0.95 } # Organize data into stime, ssurv, supper, slower stime <- x$time std <- NULL yzero <- FALSE # a marker that we have an "ordinary survival curve" with min 0 smat <- function(x) { # the rest of the routine is simpler if everything is a matrix dd <- dim(x) if (is.null(dd)) as.matrix(x) else if (length(dd) ==2) x else matrix(x, nrow=dd[1]) } if (cumhaz) { # plot the cumulative hazard instead if (is.null(x$cumhaz)) stop("survfit object does not contain a cumulative hazard") if (is.numeric(cumhaz)) { dd <- dim(x$cumhaz) if (is.null(dd)) nhazard <- 1 else nhazard <- prod(dd[-1]) if (cumhaz != floor(cumhaz)) stop("cumhaz argument is not integer") if (any(cumhaz < 1 | cumhaz > nhazard)) stop("subscript out of range") ssurv <- smat(x$cumhaz)[,cumhaz, drop=FALSE] if (!is.null(x$std.chaz)) std <- smat(x$std.chaz)[,cumhaz, drop=FALSE] } else if (is.logical(cumhaz)) { ssurv <- smat(x$cumhaz) if (!is.null(x$std.chaz)) std <- smat(x$std.chaz) } else stop("invalid cumhaz argument") } else if (inherits(x, "survfitms")) { i <- !(x$states %in% noplot) if (all(i) || !any(i)) { # the !any is a failsafe, in case none are kept we ignore noplot ssurv <- smat(x$pstate) if (!is.null(x$std.err)) std <- smat(x$std.err) if (!is.null(x$lower)) { slower <- smat(x$lower) supper <- smat(x$upper) } } else { i <- which(i) # the states to keep # we have to be careful about subscripting if (length(dim(x$pstate)) ==3) { ssurv <- smat(x$pstate[,,i, drop=FALSE]) if (!is.null(x$std.err)) std <- smat(x$std.err[,,i, drop=FALSE]) if (!is.null(x$lower)) { slower <- smat(x$lower[,,i, drop=FALSE]) supper <- smat(x$upper[,,i, drop=FALSE]) } } else { ssurv <- x$pstate[,i, drop=FALSE] if (!is.null(x$std.err)) std <- x$std.err[,i, drop=FALSE] if (!is.null(x$lower)) { slower <- smat(x$lower[,i, drop=FALSE]) supper <- smat(x$upper[,i, drop=FALSE]) } } } } else { yzero <- TRUE ssurv <- as.matrix(x$surv) # x$surv will have one column if (!is.null(x$std.err)) std <- as.matrix(x$std.err) # The fun argument usually applies to single state survfit objects # First deal with the special case of fun='cumhaz', which is here for # backwards compatability; people should use the cumhaz argument if (!missing(fun) && is.character(fun) && fun=="cumhaz") { cumhaz <- TRUE if (!is.null(x$cumhaz)) { ssurv <- as.matrix(x$cumhaz) if (!is.null(x$std.chaz)) std <- as.matrix(x$std.chaz) } else { ssurv <- as.matrix(-log(x$surv)) if (!is.null(x$std.err)) { if (x$logse) std <- as.matrix(x$std.err) else std <- as.matrix(x$std.err/x$surv) } } } } # set up strata if (is.null(x$strata)) { nstrat <- 1 stemp <- rep(1, length(x$time)) # same length as stime } else { nstrat <- length(x$strata) stemp <- rep(1:nstrat, x$strata) # same length as stime } ncurve <- nstrat * ncol(ssurv) @ If confidence limits are to be plotted, and they were not part of the data that is passed in, create them. Confidence limits for the cumulative hazard must always be created, and they don't use transforms. <>= conf.type <- match.arg(conf.type) if (conf.type=="none") conf.int <- FALSE if (conf.int== "none") conf.int <- FALSE if (conf.int=="only") { plot.surv <- FALSE conf.int <- TRUE } else plot.surv <- TRUE if (conf.int) { if (is.null(std)) stop("object does not have standard errors, CI not possible") if (cumhaz) { if (missing(conf.type)) conf.type="plain" temp <- survfit_confint(ssurv, std, logse=FALSE, conf.type, conf.level, ulimit=FALSE) supper <- as.matrix(temp$upper) slower <- as.matrix(temp$lower) } else if (is.null(x$upper)) { if (missing(conf.type) && !is.null(x$conf.type)) conf.type <- x$conf.type temp <- survfit_confint(ssurv, std, logse= x$logse, conf.type, conf.level, ulimit=FALSE) supper <- as.matrix(temp$upper) slower <- as.matrix(temp$lower) } else if (!inherits(x, "survfitms")) { supper <- as.matrix(x$upper) slower <- as.matrix(x$lower) } } else supper <- slower <- NULL @ The functional form of the fun argument can be whatever the user wants. For the character form we try to thin out the obvious mistakes. If fun=='cumhaz', the code above has already replaced ssurv with the cumulative hazard, so this part of the code should plug in an identity function. <>= if (!missing(fun)){ if (is.character(fun)) { if (cumhaz) { tfun <- switch(tolower(fun), 'log' = function(x) x, 'cumhaz'=function(x) x, 'identity'= function(x) x, stop("Invalid function argument") ) } else if (inherits(x, "survfitms")) { tfun <-switch(tolower(fun), 'log' = function(x) log(x), 'event'=function(x) x, 'cloglog'=function(x) log(-log(1-x)), 'cumhaz' = function(x) x, 'pct' = function(x) x*100, 'identity'= function(x) x, stop("Invalid function argument") ) } else { yzero <- FALSE tfun <- switch(tolower(fun), 'log' = function(x) x, 'event'=function(x) 1-x, 'cumhaz'=function(x) x, 'cloglog'=function(x) log(-log(x)), 'pct' = function(x) x*100, 'logpct'= function(x) 100*x, #special case further below 'identity'= function(x) x, 'f' = function(x) 1-x, 's' = function(x) x, 'surv' = function(x) x, stop("Unrecognized function argument") ) } } else if (is.function(fun)) tfun <- fun else stop("Invalid 'fun' argument") ssurv <- tfun(ssurv ) if (!is.null(supper)) { supper <- tfun(supper) slower <- tfun(slower) } } @ The \code{mark} argument is a holdover from S, when pch could not have numeric values; mark has since disappeared from the manual page for \code{par}. We honor it for backwards compatability. To be consistent with matplot and others, we allow pch to be a character string or a vector of characters. <>= if (missing(mark.time) & !missing(mark)) mark.time <- TRUE if (missing(pch) && !missing(mark)) pch <- mark if (length(pch)==1 && is.character(pch)) pch <- strsplit(pch, "")[[1]] # Marks are not placed on confidence bands pch <- rep(pch, length.out=ncurve) mcol <- rep(col, length.out=ncurve) if (is.numeric(mark.time)) mark.time <- sort(mark.time) # The actual number of curves is ncurve*3 if there are confidence bands, # unless conf.times has been given. Colors and line types in the latter # match the curves # If the number of line types is 1 and lty is an integer, then use lty # for the curve and lty+1 for the CI # If the length(lty) <= length(ncurve), use the same color for curve and CI # otherwise assume the user knows what they are about and has given a full # vector of line types. # Colors and line widths work like line types, excluding the +1 rule. if (conf.int & is.null(conf.times)) { if (length(lty)==1 && is.numeric(lty)) lty <- rep(c(lty, lty+1, lty+1), ncurve) else if (length(lty) <= ncurve) lty <- rep(rep(lty, each=3), length.out=(ncurve*3)) else lty <- rep(lty, length.out= ncurve*3) if (length(col) <= ncurve) col <- rep(rep(col, each=3), length.out=3*ncurve) else col <- rep(col, length.out=3*ncurve) if (length(lwd) <= ncurve) lwd <- rep(rep(lwd, each=3), length.out=3*ncurve) else lwd <- rep(lwd, length.out=3*ncurve) } else { col <- rep(col, length.out=ncurve) lty <- rep(lty, length.out=ncurve) lwd <- rep(lwd, length.out=ncurve) } @ Create the frame for the plot. We draw an empty figure, letting R figure out the limits. <>= # check consistency if (!missing(xlim)) { if (!missing(xmax)) warning("cannot have both xlim and xmax arguments, xmax ignored") if (!missing(firstx)) stop("cannot have both xlim and firstx arguments") } if (!missing(ylim)) { if (!missing(ymin)) stop("cannot have both ylim and ymin arguments") } # Do axis range computations if (!missing(xlim) && !is.null(xlim)) { tempx <- xlim xmax <- xlim[2] if (xaxs == 'S') tempx[2] <- tempx[1] + diff(tempx)*1.04 } else { temp <- stime[is.finite(stime)] if (!missing(xmax) && missing(xlim)) temp <- pmin(temp, xmax) else xmax <- NULL if (xaxs=='S') { rtemp <- range(temp) delta <- diff(rtemp) #special x- axis style for survival curves if (xlog) tempx <- c(min(rtemp[rtemp>0]), min(rtemp)+ delta*1.04) else tempx <- c(min(rtemp), min(rtemp)+ delta*1.04) } else if (xlog) tempx <- range(temp[temp > 0]) else tempx <- range(temp) } if (!missing(xlim) || !missing(xmax)) options(plot.survfit = list(xmax=tempx[2])) else options(plot.survfit = NULL) if (!missing(ylim) && !is.null(ylim)) tempy <- ylim else { skeep <- is.finite(stime) & stime >= tempx[1] & stime <= tempx[2] if (ylog) { if (!is.null(supper)) tempy <- range(c(slower[is.finite(slower) & slower>0 & skeep], supper[is.finite(supper) & skeep])) else tempy <- range(ssurv[is.finite(ssurv)& ssurv>0 & skeep]) if (tempy[2]==1) tempy[2] <- .99 # makes for a prettier axis if (any(c(ssurv, slower)[skeep] ==0)) { tempy[1] <- tempy[1]*.8 ssurv[ssurv==0] <- tempy[1] if (!is.null(slower)) slower[slower==0] <- tempy[1] } } else { if (!is.null(supper)) tempy <- range(c(supper[skeep], slower[skeep]), finite=TRUE, na.rm=TRUE) else tempy <- range(ssurv[skeep], finite=TRUE, na.rm= TRUE) if (yzero) tempy <- range(c(0, tempy)) } } if (!missing(ymin)) tempy[1] <- ymin # # Draw the basic box # temp <- if (xaxs=='S') 'i' else xaxs plot(range(tempx, finite=TRUE, na.rm=TRUE)/xscale, range(tempy, finite=TRUE, na.rm=TRUE)*yscale, type='n', log=logax, xlab=xlab, ylab=ylab, xaxs=temp,...) if(yscale != 1) { if (ylog) par(usr =par("usr") -c(0, 0, log10(yscale), log10(yscale))) else par(usr =par("usr")/c(1, 1, yscale, yscale)) } if (xscale !=1) { if (xlog) par(usr =par("usr") -c(log10(xscale), log10(xscale), 0,0)) else par(usr =par("usr")*c(xscale, xscale, 1, 1)) } @ The use of [[par(usr)]] just above is a bit sneaky. I want the lines and points routines to be able to add to the plot, \emph{without} passing them a global parameter that determines the y-scale or forcing the user to repeat it. The next functions do the actual drawing. <>= # Create a step function, removing redundancies that sometimes occur in # curves with lots of censoring. dostep <- function(x,y) { keep <- is.finite(x) & is.finite(y) if (!any(keep)) return() #all points were infinite or NA if (!all(keep)) { # these won't plot anyway, so simplify (CI values are often NA) x <- x[keep] y <- y[keep] } n <- length(x) if (n==1) list(x=x, y=y) else if (n==2) list(x=x[c(1,2,2)], y=y[c(1,1,2)]) else { # replace verbose horizonal sequences like # (1, .2), (1.4, .2), (1.8, .2), (2.3, .2), (2.9, .2), (3, .1) # with (1, .2), (.3, .2),(3, .1). # They are slow, and can smear the looks of the line type. temp <- rle(y)$lengths drops <- 1 + cumsum(temp[-length(temp)]) # points where the curve drops #create a step function if (n %in% drops) { #the last point is a drop xrep <- c(x[1], rep(x[drops], each=2)) yrep <- rep(y[c(1,drops)], c(rep(2, length(drops)), 1)) } else { xrep <- c(x[1], rep(x[drops], each=2), x[n]) yrep <- c(rep(y[c(1,drops)], each=2)) } list(x=xrep, y=yrep) } } drawmark <- function(x, y, mark.time, censor, cex, ...) { if (!is.numeric(mark.time)) { xx <- x[censor>0] yy <- y[censor>0] if (any(censor >1)) { # tied death and censor, put it on the midpoint j <- pmax(1, which(censor>1) -1) i <- censor[censor>0] yy[i>1] <- (yy[i>1] + y[j])/2 } } else { #interpolate xx <- mark.time yy <- approx(x, y, xx, method="constant", f=0)$y } points(xx, yy, cex=cex, ...) } @ The code to draw the lines and confidence bands. <>= c1 <- 1 # keeps track of the curve number c2 <- 1 # keeps track of the lty, col, etc xend <- yend <- double(ncurve) if (length(conf.offset) ==1) temp.offset <- (1:ncurve - (ncurve+1)/2)* conf.offset* diff(par("usr")[1:2]) else temp.offset <- rep(conf.offset, length=ncurve) * diff(par("usr")[1:2]) temp.cap <- conf.cap * diff(par("usr")[1:2]) for (j in 1:ncol(ssurv)) { for (i in unique(stemp)) { #for each strata who <- which(stemp==i) # if n.censor is missing, then assume any line that does not have an # event would not be present but for censoring, so there must have # been censoring then # otherwise categorize is 0= no censor, 1=censor, 2=censor and death if (is.null(x$n.censor)) censor <- ifelse(x$n.event[who]==0, 1, 0) else censor <- ifelse(x$n.censor[who]==0, 0, 1 + (x$n.event[who] > 0)) xx <- stime[who] yy <- ssurv[who,j] if (conf.int) { ylower <- (slower[who,j]) yupper <- (supper[who,j]) } if (!is.null(xmax) && max(xx) > xmax) { # truncate on the right xn <- min(which(xx > xmax)) xx <- xx[1:xn] yy <- yy[1:xn] xx[xn] <- xmax yy[xn] <- yy[xn-1] if (conf.int) { ylower <- ylower[1:xn] yupper <- yupper[1:xn] ylower[xn] <- ylower[xn-1] yupper[xn] <- yupper[xn-1] } } if (plot.surv) { if (type=='s') lines(dostep(xx, yy), lty=lty[c2], col=col[c2], lwd=lwd[c2]) else lines(xx, yy, type=type, lty=lty[c2], col=col[c2], lwd=lwd[c2]) if (is.numeric(mark.time) || mark.time) drawmark(xx, yy, mark.time, censor, pch=pch[c1], col=mcol[c1], cex=cex) } xend[c1] <- max(xx) yend[c1] <- yy[length(yy)] if (conf.int && !is.null(conf.times)) { # add vertical bars at the specified times x2 <- conf.times + temp.offset[c1] templow <- approx(xx, ylower, x2, method='constant', f=1)$y temphigh<- approx(xx, yupper, x2, method='constant', f=1)$y segments(x2, templow, x2, temphigh, lty=lty[c2], col=col[c2], lwd=lwd[c2]) if (conf.cap>0) { segments(x2-temp.cap, templow, x2+temp.cap, templow, lty=lty[c2], col=col[c2], lwd=lwd[c2] ) segments(x2-temp.cap, temphigh, x2+temp.cap, temphigh, lty=lty[c2], col=col[c2], lwd=lwd[c2]) } } c1 <- c1 +1 c2 <- c2 +1 if (conf.int && is.null(conf.times)) { if (type == 's') { lines(dostep(xx, ylower), lty=lty[c2], col=col[c2],lwd=lwd[c2]) c2 <- c2 +1 lines(dostep(xx, yupper), lty=lty[c2], col=col[c2], lwd= lwd[c2]) c2 <- c2 + 1 } else { lines(xx, ylower, lty=lty[c2], col=col[c2],lwd=lwd[c2], type=type) c2 <- c2 +1 lines(xx, yupper, lty=lty[c2], col=col[c2], lwd= lwd[c2], type= type) c2 <- c2 + 1 } } } } lastx <- list(x=xend, y=yend) @ \section{State space figures} The statefig function was written to do ``good enough'' state space figures quickly and easily. There are certainly figures it can't draw and many figures that can be drawn better, but it accomplishes its purpose. The key argument \code{layout}, the first, is a vector of numbers. The value (1,3,4,2) for instance has a single state, then a column with 3 states, then a column with 4, then a column with 2. If \code{layout} is instead a 1 column matrix then do the same from top down. If it is a 2 column matrix then they provided their own spacing. <>= statefig <- function(layout, connect, margin=.03, box=TRUE, cex=1, col=1, lwd=1, lty=1, bcol= col, acol=col, alwd = lwd, alty= lty, offset=0) { # set up an empty canvas frame(); # new environment par(usr=c(0,1,0,1)) if (!is.numeric(layout)) stop("layout must be a numeric vector or matrix") if (!is.matrix(connect) || nrow(connect) != ncol(connect)) stop("connect must be a square matrix") nstate <- nrow(connect) dd <- dimnames(connect) if (!is.null(dd[[1]])) statenames <- dd[[1]] else if (is.null(dd[[2]])) stop("connect must have the state names as dimnames") else statenames <- dd[[2]] # expand out all of the graphical parameters. This lets users # use a vector of colors, line types, etc narrow <- sum(connect!=0) acol <- rep(acol, length=narrow) alwd <- rep(alwd, length=narrow) alty <- rep(alty, length=narrow) bcol <- rep(bcol, length=nstate) lty <- rep(lty, length=nstate) lwd <- rep(lwd, length=nstate) col <- rep(col, length=nstate) # text colors <> <> <> dimnames(cbox) <- list(statenames, c("x", "y")) invisible(cbox) } <> @ The drawing region is always (0,1) by (0,1). A user can enter their own matrix of coordinates. Otherwise the free space is divided with one portion on each end and 2 portions between boxes. If there were 3 columns for instance they will have x coordinates of 1/6, 1/6 + 1/3, 1/6 + 2/3. Ditto for dividing up the y coordinate. The primary nuisance is that we want to count down from the top instead of up from the bottom. A 1 by 1 matrix is treated as a column matrix. <>= if (is.matrix(layout) && ncol(layout)==2 && nrow(layout) > 1) { # the user provided their own if (any(layout <0) || any(layout >1)) stop("layout coordinates must be between 0 and 1") if (nrow(layout) != nstate) stop("layout matrix should have one row per state") cbox <- layout } else { if (any(layout <=0 | layout != floor(layout))) stop("non-integer number of states in layout argument") space <- function(n) (1:n -.5)/n # centers of the boxes if (sum(layout) != nstate) stop("number of boxes != number of states") cbox <- matrix(0, ncol=2, nrow=nstate) #coordinates will be here n <- length(layout) ix <- rep(seq(along=layout), layout) if (is.vector(layout) || ncol(layout)> 1) { #left to right cbox[,1] <- space(n)[ix] for (i in 1:n) cbox[ix==i,2] <- 1 -space(layout[i]) } else { # top to bottom cbox[,2] <- 1- space(n)[ix] for (i in 1:n) cbox[ix==i,1] <- space(layout[i]) } } @ Write the text out. Compute the width and height of each box. Then compute the margin. The only tricky thing here is that we want the area around the text to \emph{look} the same left-right and up-down, which depends on the geometry of the plotting region. <>= text(cbox[,1], cbox[,2], statenames, cex=cex, col=col) # write the labels textwd <- strwidth(statenames, cex=cex) textht <- strheight(statenames, cex=cex) temp <- par("pin") #plot region in inches dx <- margin * temp[2]/mean(temp) # extra to add in the x dimension dy <- margin * temp[1]/mean(temp) # extra to add in y if (box) { drawbox <- function(x, y, dx, dy, lwd, lty, col) { lines(x+ c(-dx, dx, dx, -dx, -dx), y+ c(-dy, -dy, dy, dy, -dy), lwd=lwd, lty=lty, col=col) } for (i in 1:nstate) drawbox(cbox[i,1], cbox[i,2], textwd[i]/2 + dx, textht[i]/2 + dy, col=bcol[i], lwd=lwd[i], lty=lty[i]) dx <- 2*dx; dy <- 2*dy # move arrows out from the box } @ Now for the hard part, which is drawing the arrows. The entries in the connection matrix are 0= no connection or $1+d$ for $-1 < d < 1$. The connection is an arc that passes from the center of box 1 to the center of box 2, and through a point that is $dz$ units above the midpoint of the line from box 1 to box 2, where $2z$ is the length of that line. For $d=1$ we get a half circle to the right (with respect to traversing the line from A to B) and for $d= -1$ we get a half circle to the left. If $d=0$ it is a straight line. If A and B are the starting and ending points then AB is the chord of a circle. Draw radii from the center to A, B, and through the midpoint $c$ of AB. This last has length $dz$ above the chord and $r- dz$ below where $r$ is the radius. Then we have \begin{align*} r^2 & = z^2 + (r-dz)^2 \\ 2rdz &= z^2 + (dz)^2 \\ r &= \left[z (1+ d^2) \right ]/ 2d \end{align*} Be careful with negative $d$, which is used to denote left-hand arcs. The angle $\theta$ from A to B is the arctan of $B-A$, and the center of the circle is at $C = (A+B)/2 + (r - dz)(\sin \theta, -\cos \theta)$. We then need to draw the arc $C + r(\cos \phi, \sin \phi)$ for some range of angles $\phi$. The angles to the centers of the boxes are $\arctan(A-C)$ and $\arctan(B-C)$, but we want to start and end outside the box. It turned out that this is more subtle than I thought. The solution below uses two helper functions \code{statefigx} and \code{statefigy}. The first accepts $C$, $r$, the range of $\phi$ values, and a target $y$ value. It returns the angles, within the range, such that the endpoint of the arc has horizontal coordinate $x$, or an empty vector if none such exists. For an arc there are sometimes two solutions. First calculate the angles for which the arc will strike the horizontal line. If the arc is too short to reach the line then there is no intersection. The return legal angles. <>= statefigx <- function(x, C, r, a1, a2) { temp <-(x - C[1])/r if (abs(temp) >1) return(NULL) # no intersection of the arc and x phi <- acos(temp) # this will be from 0 to pi pi <- 3.1415926545898 # in case someone has a variable "pi" if (x > C[1]) phi <- c(phi, pi - phi) else phi <- -c(phi, pi - phi) # Add reflection about the X axis, in both forms phi <- c(phi, -phi, 2*pi - phi) amax <- max(a1, a2) amin <- min(a1, a2) phi[phi amin] } statefigy <- function(y, C, r, a1, a2) { pi <- 3.1415926545898 # in case someone has a variable named "pi" amax <- max(a1, a2) amin <- min(a1, a2) temp <-(y - C[2])/r if (abs(temp) >1) return(NULL) # no intersection of the arc and y phi <- asin(temp) # will be from -pi/2 to pi/2 phi <- c(phi, sign(phi)*pi -phi) # reflect about the vertical phi <- c(phi, phi + 2*pi) phi[phi amin] } @ <>= phi <- function(x1, y1, x2, y2, d, delta1, delta2) { # d = height above the line theta <- atan2(y2-y1, x2-x1) # angle from center to center if (abs(d) < .001) d=.001 # a really small arc looks like a line z <- sqrt((x2-x1)^2 + (y2 - y1)^2) /2 # half length of chord ab <- c((x1 + x2)/2, (y1 + y2)/2) # center of chord r <- abs(z*(1 + d^2)/ (2*d)) if (d >0) C <- ab + (r - d*z)* c(-sin(theta), cos(theta)) # center of arc else C <- ab + (r + d*z)* c( sin(theta), -cos(theta)) a1 <- atan2(y1-C[2], x1-C[1]) # starting angle a2 <- atan2(y2-C[2], x2-C[1]) # ending angle if (abs(a2-a1) > pi) { # a1= 3 and a2=-3, we don't want to include 0 # nor for a1=-3 and a2=3 if (a1>0) a2 <- a2 + 2 *pi else a1 <- a1 + 2*pi } if (d > 0) { #counterclockwise phi1 <- min(statefigx(x1 + delta1[1], C, r, a1, a2), statefigx(x1 - delta1[1], C, r, a1, a2), statefigy(y1 + delta1[2], C, r, a1, a2), statefigy(y1 - delta1[2], C, r, a1, a2), na.rm=TRUE) phi2 <- max(statefigx(x2 + delta2[1], C, r, a1, a2), statefigx(x2 - delta2[1], C, r, a1, a2), statefigy(y2 + delta2[2], C, r, a1, a2), statefigy(y2 - delta2[2], C, r, a1, a2), na.rm=TRUE) } else { # clockwise phi1 <- max(statefigx(x1 + delta1[1], C, r, a1, a2), statefigx(x1 - delta1[1], C, r, a1, a2), statefigy(y1 + delta1[2], C, r, a1, a2), statefigy(y1 - delta1[2], C, r, a1, a2), na.rm=TRUE) phi2 <- min(statefigx(x2 + delta2[1], C, r, a1, a2), statefigx(x2 - delta2[1], C, r, a1, a2), statefigy(y2 + delta2[2], C, r, a1, a2), statefigy(y2 - delta2[2], C, r, a1, a2), na.rm=TRUE) } list(center=C, angle=c(phi1, phi2), r=r) } @ Now draw the arrows, one at a time. I arbitrarily declare that 20 segments is enough for a smooth curve. <>= arrow2 <- function(...) arrows(..., angle=20, length=.1) doline <- function(x1, x2, d, delta1, delta2, lwd, lty, col) { if (d==0 && x1[1] ==x2[1]) { # vertical line if (x1[2] > x2[2]) # downhill arrow2(x1[1], x1[2]- delta1[2], x2[1], x2[2] + delta2[2], lwd=lwd, lty=lty, col=col) else arrow2(x1[1], x1[2]+ delta1[2], x2[1], x2[2] - delta2[2], lwd=lwd, lty=lty, col=col) } else if (d==0 && x1[2] == x2[2]) { # horizontal line if (x1[1] > x2[1]) # right to left arrow2(x1[1]-delta1[1], x1[2], x2[1] + delta2[1], x2[2], lwd=lwd, lty=lty, col=col) else arrow2(x1[1]+delta1[1], x1[2], x2[1] - delta2[1], x2[2], lwd=lwd, lty=lty, col=col) } else { temp <- phi(x1[1], x1[2], x2[1], x2[2], d, delta1, delta2) if (d==0) { arrow2(temp$center[1] + temp$r*cos(temp$angle[1]), temp$center[2] + temp$r*sin(temp$angle[1]), temp$center[1] + temp$r*cos(temp$angle[2]), temp$center[2] + temp$r*sin(temp$angle[2]), lwd=lwd, lty=lty, col=col) } else { # approx the curve with 21 segments # arrowhead on the last one phi <- seq(temp$angle[1], temp$angle[2], length=21) lines(temp$center[1] + temp$r*cos(phi), temp$center[2] + temp$r*sin(phi), lwd=lwd, lty=lty, col=col) arrow2(temp$center[1] + temp$r*cos(phi[20]), temp$center[2] + temp$r*sin(phi[20]), temp$center[1] + temp$r*cos(phi[21]), temp$center[2] + temp$r*sin(phi[21]), lwd=lwd, lty=lty, col=col) } } } @ The last arrow bit is the offset. If offset $\ne 0$ and there is a bidirectional arrow between two boxes, and the arc for both of them is identical, then move each arrow just a bit, orthagonal to a segment connecting the middle of the two boxes. If the line goes from (x1, y1) to (x2, y2), then the normal to the line at (x1, x2) is (y2-y1, x1-x2), normalized to length 1. The -1 below (\code{-offset}) makes the shift obey a left-hand rule: looking down a line segement towards the arrow head, we shift to the left. This makes two horizontal arrows stack in the normal typographical order for chemical reactions, the right facing one above the left facing. A user can use a negative value for offset to reverse this if they wish. <>= k <- 1 for (j in 1:nstate) { for (i in 1:nstate) { if (i != j && connect[i,j] !=0) { if (connect[i,j] == 2-connect[j,i] && offset!=0) { #add an offset toff <- c(cbox[j,2] - cbox[i,2], cbox[i,1] - cbox[j,1]) toff <- -offset *toff/sqrt(sum(toff^2)) doline(cbox[i,]+toff, cbox[j,]+toff, connect[i,j]-1, delta1 = c(textwd[i]/2 + dx, textht[i]/2 + dy), delta2 = c(textwd[j]/2 + dx, textht[j]/2 + dy), lty=alty[k], lwd=alwd[k], col=acol[k]) } else doline(cbox[i,], cbox[j,], connect[i,j]-1, delta1 = c(textwd[i]/2 + dx, textht[i]/2 + dy), delta2 = c(textwd[j]/2 + dx, textht[j]/2 + dy), lty=alty[k], lwd=alwd[k], col=acol[k]) k <- k +1 } } } @ \section{tmerge} The tmerge function was designed around a set of specific problems. The idea is to build up a time dependent data set one endpoint at at time. The primary arguments are \begin{itemize} \item data1: the base data set that will be added onto \item data2: the source for new information \item id: the subject identifier in the new data \item \ldots: additional arguments that add variables to the data set \item tstart, tstop: used to set the time range for each subject \item options \end{itemize} The created data set has three new variables (at least), which are \code{id}, \code{tstart} and \code{tstop}. The key part of the call are the ``\ldots'' arguments which each can be one of four types: tdc() and cumtdc() add a time dependent variable, event() and cumevent() add a new endpoint. In the survival routines time intervals are open on the left and closed on the right, i.e., (tstart, tstop]. Time dependent covariates apply from the start of an interval and events occur at the end of an interval. If a data set already had intervals of (0,10] and (10, 14] a new time dependent covariate or event at time 8 would lead to three intervals of (0,8], (8,10], and (10,14]; the new time-dependent covariate value would be added to the second interval, a new event would be added to the first one. A typical call would be <>= newdata <- tmerge(newdata, old, id=clinic, diabetes=tdc(diab.time)) @ which would add a new time dependent covariate \code{diabetes} to the data set. <>= tmerge <- function(data1, data2, id, ..., tstart, tstop, options) { Call <- match.call() # The function wants to recognize special keywords in the # arguments, so define a set of functions which will be used to # mark objects new <- new.env(parent=parent.frame()) assign("tdc", function(time, value=NULL, init=NULL) { x <- list(time=time, value=value, default= init); class(x) <- "tdc"; x}, envir=new) assign("cumtdc", function(time, value=NULL, init=NULL) { x <- list(time=time, value=value, default= init); class(x) <-"cumtdc"; x}, envir=new) assign("event", function(time, value=NULL, censor=NULL) { x <- list(time=time, value=value, censor=censor); class(x) <-"event"; x}, envir=new) assign("cumevent", function(time, value=NULL, censor=NULL) { x <- list(time=time, value=value, censor=censor); class(x) <-"cumevent"; x}, envir=new) if (missing(data1) || missing(data2) || missing(id)) stop("the data1, data2, and id arguments are required") if (!inherits(data1, "data.frame")) stop("data1 must be a data frame") <> <> <> } <> @ The program can't use formulas because the \ldots arguments need to be named. This results in a bit of evaluation magic to correctly assess arguments. The routine below could have been set out as a separate top-level routine, the argument is where we want to document it: within the tmerge page or on a separate one. I decided on the former. <>= tmerge.control <- function(idname="id", tstartname="tstart", tstopname="tstop", delay =0, na.rm=TRUE, tdcstart=NA_real_, ...) { extras <- list(...) if (length(extras) > 0) stop("unrecognized option(s):", paste(names(extras), collapse=', ')) if (length(idname) != 1 || make.names(idname) != idname) stop("idname option must be a valid variable name") if (!is.null(tstartname) && (length(tstartname) !=1 || make.names(tstartname) != tstartname)) stop("tstart option must be NULL or a valid variable name") if (length(tstopname) != 1 || make.names(tstopname) != tstopname) stop("tstop option must be a valid variable name") if (length(delay) !=1 || !is.numeric(delay) || delay < 0) stop("delay option must be a number >= 0") if (length(na.rm) !=1 || ! is.logical(na.rm)) stop("na.rm option must be TRUE or FALSE") if (length(tdcstart) !=1) stop("tdcstart must be a single value") list(idname=idname, tstartname=tstartname, tstopname=tstopname, delay=delay, na.rm=na.rm, tdcstart=tdcstart) } if (!inherits(data1, "tmerge") && !is.null(attr(data1, "tname"))) { # old style object that someone saved! tm.retain <- list(tname = attr(data1, "tname"), tevent= list(name=attr(data1, "tevent"), censor= attr(data1, "tcensor")), tdcvar = attr(data1, "tdcvar"), n = nrow(data1)) attr(data1, "tname") <- attr(data1, "tevent") <- NULL attr(data1, "tcensor") <- attr(data1, "tdcvar") <- NULL attr(data1, "tm.retain") <- tm.retain class(data1) <- c("tmerge", class(data1)) } if (inherits(data1, "tmerge")) { tm.retain <- attr(data1, "tm.retain") firstcall <- FALSE # check out whether the object looks legit: # has someone tinkered with it? This won't catch everything tname <- tm.retain$tname tevent <- tm.retain$tevent tdcvar <- tm.retain$tdcvar if (nrow(data1) != tm.retain$n) stop("tmerge object has been modified, size") if (any(is.null(match(unlist(tname), names(data1)))) || any(is.null(match(tm.retain$tcdname, names(data1)))) || any(is.null(match(tevent$name, names(data1))))) stop("tmerge object has been modified, missing variables") for (i in seq(along=tevent$name)) { ename <- tevent$name[i] if (is.numeric(data1[[ename]])) { if (!is.numeric(tevent$censor[[i]])) stop("event variable ", ename, " no longer matches it's original class") } else if (is.character(data1[[ename]])) { if (!is.character(tevent$censor[[i]])) stop("event variable ", ename, " no longer matches it's original class") } else if (is.logical(data1[[ename]])) { if (!is.logical(tevent$censor[[i]])) stop("event variable ", ename, " no longer matches it's original class") } else if (is.factor(data1[[ename]])) { if (levels(data1[[ename]])[1] != tevent$censor[[i]]) stop("event variable ", ename, " has a new first level") } else stop("event variable ", ename, " is of an invalid class") } } else { firstcall <- TRUE tname <- tevent <- tdcvar <- NULL if (is.name(Call[["id"]])) { idx <- as.character(Call[["id"]]) if (missing(options)) options <-list(idname= idx) else if (is.null(options$idname)) options$idname <- idx } } if (!missing(options)) { if (!is.list(options)) stop("options must be a list") if (!is.null(tname)) { # If an option name matches one already in tname, don't confuse # the tmerge.control routine with duplicate arguments temp <- match(names(options), names(tname), nomatch=0) topt <- do.call(tmerge.control, c(options, tname[temp==0])) if (any(temp >0)) { # A variable name is changing midstream, update the # variable names in data1 varname <- tname[c("idname", "tstartname", "tstopname")] temp2 <- match(varname, names(data1)) names(data1)[temp2] <- varname } } else topt <- do.call(tmerge.control, options) } else if (length(tname)) topt <- do.call(tmerge.control, tname) else topt <- tmerge.control() # id, tstart, tstop are found in data2 if (missing(id)) stop("the id argument is required") if (missing(data1) || missing(data2)) stop("two data sets are required") id <- eval(Call[["id"]], data2, enclos=emptyenv()) #don't find it elsewhere if (is.null(id)) stop("id variable not found in data2") if (any(is.na(id))) stop("id variable cannot have missing values") if (firstcall) { if (!missing(tstop)) { tstop <- eval(Call[["tstop"]], data2) if (length(tstop) != length(id)) stop("tstop and id must be the same length") # The neardate routine will check for legal tstop data type } if (!missing(tstart)) { tstart <- eval(Call[["tstart"]], data2) if (length(tstart)==1) tstart <- rep(tstart, length(id)) if (length(tstart) != length(id)) stop("tstart and id must be the same length") if (any(tstart >= tstop)) stop("tstart must be < tstop") } } else { if (!missing(tstart) || !missing(tstop)) stop("tstart and tstop arguments only apply to the first call") } @ Get the \ldots arguments. They are evaluated in a special frame, set up earlier, so that the definitions of the functions tdc, cumtdc, event, and cumevent are local to tmerge. Check that they are all legal: each argument is named, and is one of the four allowed types. <>= # grab the... arguments notdot <- c("data1", "data2", "id", "tstart", "tstop", "options") dotarg <- Call[is.na(match(names(Call), notdot))] dotarg[[1]] <- as.name("list") # The as-yet dotarg arguments if (missing(data2)) args <- eval(dotarg, envir=new) else args <- eval(dotarg, data2, enclos=new) argclass <- sapply(args, function(x) (class(x))[1]) argname <- names(args) if (any(argname== "")) stop("all additional argments must have a name") check <- match(argclass, c("tdc", "cumtdc", "event", "cumevent")) if (any(is.na(check))) stop(paste("argument(s)", argname[is.na(check)], "not a recognized type")) @ The tcount matrix keeps track of what we have done, and is added to the final object at the end. This is useful to the user for debugging what may have gone right or wrong in their usage. <>= # The tcount matrix is useful for debugging tcount <- matrix(0L, length(argname), 9) dimnames(tcount) <- list(argname, c("early","late", "gap", "within", "boundary", "leading", "trailing", "tied", "missid")) tcens <- tevent$censor tevent <- tevent$name if (is.null(tcens)) tcens <- vector('list', 0) @ The very first call to the routine is special, since this is when the range of legal times is set. We also apply an initial sort to the data if necessary so that times are in order. There are 2 cases: \begin{enumerate} \item Adding a time range: tstop comes from data2, optional tstart, and the id can be simply matched, by which we mean no duplicates in data1. \item The more common case: there is no tstop, one observation per subject, and the first optional argument is an event or cumevent. We then use its time as the range. \end{enumerate} One thing we could add, but didn't, was to warn if any of the three new variables will stomp on ones already in data1. Note that in case 2 we cannot wait for the later code to deal with duplicate id/time pairs, since that later code requires a valid starting point. That code will work out which of a duplicate should be retained, however. <>= newdata <- data1 #make a copy if (firstcall) { # We don't look for topt$id. What if the user had id=clinic, but their # starting data set also had a variable named "id". We want clinic for # this first call. idname <- Call[["id"]] if (!is.name(idname)) stop("on the first call 'id' must be a single variable name") # The line below finds tstop and tstart variables in data1 indx <- match(c(topt$idname, topt$tstartname, topt$tstopname), names(data1), nomatch=0) if (any(indx[1:2]>0) && FALSE) { # warning currently turned off. Be chatty? overwrite <- c(topt$tstartname, topt$tstopname)[indx[2:3]] warning("overwriting data1 variables", paste(overwrite, collapse=' ')) } temp <- as.character(idname) if (!is.na(match(temp, names(data1)))) { data1[[topt$idname]] <- data1[[temp]] baseid <- data1[[temp]] } else stop("id variable not found in data1") if (any(duplicated(baseid))) stop("for the first call (that establishes the time range) data1 must have no duplicate identifiers") if (missing(tstop)) { if (length(argclass)==0 || argclass[1] != "event") stop("neither a tstop argument nor an initial event argument was found") # this is case 2 -- the first time value for each obs sets the range last <- !duplicated(id) indx2 <- match(unique(id[last]), baseid) if (any(is.na(indx2))) stop("setting the range, and data2 has id values not in data1") if (any(is.na(match(baseid, id)))) stop("setting the range, and data1 has id values not in data2") newdata <- data1[indx2,] tstop <- (args[[1]]$time)[last] } else { if (length(baseid)== length(id) && all(baseid == id)) newdata <- data1 else { # Note: 'id' is the idlist for data 2 indx2 <- match(id, baseid) if (any(is.na(indx2))) stop("setting the range, and data2 has id values not in data1") if (any(is.na(match(baseid, id)))) stop("setting the range, and data1 has id values not in data2") newdata <- data1[indx2,] } } if (any(is.na(tstop))) stop("missing time value, when that variable defines the span") if (missing(tstart)) { indx <- which(tstop <=0) if (length(indx) >0) stop("found an ending time of ", tstop[indx[1]], ", the default starting time of 0 is invalid") tstart <- rep(0, length(tstop)) } if (any(tstart >= tstop)) stop("tstart must be < tstop") newdata[[topt$tstartname]] <- tstart newdata[[topt$tstopname]] <- tstop n <- nrow(newdata) if (any(duplicated(id))) { # sort by time within id indx1 <- match(id, unique(id)) newdata <- newdata[order(indx1, tstop),] } temp <- newdata[[topt$idname]] if (any(tstart >= tstop)) stop("tstart must be < tstop") if (any(newdata$tstop[-n] > newdata$tstart[-1] & temp[-n] == temp[-1])) stop("first call has created overlapping or duplicated time intervals") idmiss <- 0 # the tcount table should have a zero } else { #not a first call idmatch <- match(id, data1[[topt$idname]], nomatch=0) if (any(idmatch==0)) idmiss <- sum(idmatch==0) else idmiss <- 0 } @ Now for the real work. For each additional argument we first match the id/time pairs of the new data to the current data set, and categorize each into a type. If the time value in data2 is NA, then that addition is skipped. Ditto if the value is NA and options narm=TRUE. This is a convenience for the user, who will often be merging in a variable like ``day of first diabetes diagnosis'' which is missing for those who never had that outcome occur. <>= saveid <- id for (ii in seq(along.with=args)) { argi <- args[[ii]] baseid <- newdata[[topt$idname]] dstart <- newdata[[topt$tstartname]] dstop <- newdata[[topt$tstopname]] argcen <- argi$censor # if an event time is missing then skip that obs. Also toss obs that # whose id does not match anyone in data1 etime <- argi$time if (idmiss ==0) keep <- rep(TRUE, length(etime)) else keep <- (idmatch > 0) if (length(etime) != length(saveid)) stop("argument ", argname[ii], " is not the same length as id") if (!is.null(argi$value)) { if (length(argi$value) != length(saveid)) stop("argument ", argname[ii], " is not the same length as id") if (topt$na.rm) keep <- keep & !(is.na(etime) | is.na(argi$value)) else keep <- keep & !is.na(etime) if (!all(keep)) { etime <- etime[keep] argi$value <- argi$value[keep] } } else { keep <- keep & !is.na(etime) etime <- etime[keep] } id <- saveid[keep] # Later steps become easier if we sort the new data by id and time # The match() is critical when baseid is not in sorted order. The # etime part of the sort will change from one ii value to the next. indx <- order(match(id, baseid), etime) id <- id[indx] etime <- etime[indx] if (!is.null(argi$value)) yinc <- argi$value[indx] else yinc <- NULL # indx1 points to the closest start time in the baseline data (data1) # that is <= etime. indx2 to the closest end time that is >=etime. # If etime falls into a (tstart, tstop) interval, indx1 and indx2 # will match # If the "delay" argument is set and this event is of type tdc, then # move any etime that is after the entry time for a subject. if (topt$delay >0 && argclass[ii] %in% c("tdc", "cumtdc")) { mintime <- tapply(dstart, baseid, min) index <- match(id, names(mintime)) etime <- ifelse(etime <= mintime[index], etime, etime+ topt$delay) } indx1 <- neardate(id, baseid, etime, dstart, best="prior") indx2 <- neardate(id, baseid, etime, dstop, best="after") # The event times fall into one of 5 categories # 1. Before the first interval # 2. After the last interval # 3. Outside any interval but with time span, i.e, it falls into # a gap in follow-up # 4. Strictly inside an interval (does't touch either end) # 5. Inside an interval, but touching. itype <- ifelse(is.na(indx1), 1, ifelse(is.na(indx2), 2, ifelse(indx2 > indx1, 3, ifelse(etime== dstart[indx1] | etime== dstop[indx2], 5, 4)))) # Subdivide the events that touch on a boundary # 1: intervals of (a,b] (b,d], new count at b "tied edge" # 2: intervals of (a,b] (c,d] with c>b, new count at c, "front edge" # 3: intervals of (a,b] (c,d] with c>b, new count at b, "back edge" # subtype <- ifelse(itype!=5, 0, ifelse(indx1 == indx2+1, 1, ifelse(etime==dstart[indx1], 2, 3))) tcount[ii,1:7] <- table(factor(itype+subtype, levels=c(1:4, 6:8))) # count ties. id and etime are not necessarily sorted tcount[ii,8] <- sum(tapply(etime, id, function(x) sum(duplicated(x)))) tcount[ii,9] <- idmiss <> } @ A \code{tdc} or \code{cumtdc} operator defines a new time-dependent variable which applies to all future times. Say that we had the following scenario for one subject \begin{center} \begin{tabular}{rr|rr} \multicolumn{2}{c}{current} & \multicolumn{2}{c}{addition} \\ tstart & tstop & time & x \\ 2 & 5 & 1 & 20.2 \\ 6 & 7 & 7 & 11 \\ 7 & 15 & 8 & 17.3 \\ 15 & 30 \\ \end{tabular} \end{center} The resulting data set will have intervals of (2,5), (6,7), (7,8) and (8,15) with covariate values of 20.2, 20.2, 11, and 17.3. Only a covariate change that occurs within an interval causes a new data row. Covariate changes that happen after the last interval are ignored, i.e. at change at time $\ge 30$ in the above example. If instead this had been events at times 1, 7, and 8, the first event would be ignored since it happens outside of any interval, so would an event at exactly time 2. The event at time 7 would be recorded in the (6,7) interval and the one at time 8 in the (7,8) interval: events happen at the ends of intervals. In both cases new rows are only generated for new time values that fall strictly within one of the old intervals. When a subject has two increments on the same day the later one wins. This is correct behavior for cumtdc, a bit odd for cumevent, and the user's problem for tdc and event. We report back the number of ties so that the user can deal with it. Where are we now with the variables? \begin{center} \begin{tabular}{cccc} itype& class & indx1 & indx2 \\ \hline 1 & before & NA & next interval \\ 2 & after & prior interval & NA \\ 3 & in a gap & prior interval & next interval \\ 4 & within interval & containing interval & containing interval \\ 5-1 & on a join & next interval & prior interval \\ 5-2 & front edge & containing & containing \\ 5-3 & back edge & containing & containing \\ \end{tabular} \end{center} If there are any itype 4, start by expanding the data set to add new cut points, which will turn all the 4's into 5-1 types. When expanding, all the event type variables turn into ``censor'' at the newly added times and other variables stay the same. A subject could have more than one new cutpoint added within an interval so we have to count each. In newdata all the rows for a given subject are contiguous and in time order, though the data set may not be in subject order. <>= indx4 <- which(itype==4) n4 <- length(indx4) if (n4 > 0) { # we need to eliminate duplicate times within the same id, but # do so without changing the class of etime: it might # be a Date, an integer, a double, ... # Using unique on a data.frame does the trick icount <- data.frame(irow= indx1[indx4], etime=etime[indx4]) icount <- unique(icount) # the icount data frame will be sorted by second column within first # so rle is faster than table n.add <- rle(icount$irow)$length # number of rows to add for each id # expand the data irep <- rep.int(1L, nrow(newdata)) erow <- unique(indx1[indx4]) # which rows in newdata to be expanded irep[erow] <- 1+ n.add # number of rows in new data jrep <- rep(1:nrow(newdata), irep) #stutter the duplicated rows newdata <- newdata[jrep,] #expand it out dstart <- dstart[jrep] dstop <- dstop[jrep] #fix up times nfix <- length(erow) temp <- vector("list", nfix) iend <- (cumsum(irep))[irep >1] #end row of each duplication set for (j in 1:nfix) temp[[j]] <- -(seq(n.add[j] -1, 0)) + iend[j] newrows <- unlist(temp) dstart[newrows] <- dstop[newrows-1] <- icount$etime newdata[[topt$tstartname]] <- dstart newdata[[topt$tstopname]] <- dstop for (ename in tevent) newdata[newrows-1, ename] <- tcens[[ename]] # refresh indices baseid <- newdata[[topt$idname]] indx1 <- neardate(id, baseid, etime, dstart, best="prior") indx2 <- neardate(id, baseid, etime, dstop, best="after") subtype[itype==4] <- 1 #all the "insides" are now on a tied edge itype[itype==4] <- 5 } @ Now we can add the new variable. The most common is a tdc, so start with it. The C routine returns a set of indices: 0,1,1,2,3,0,4,... would mean that row 1 of the new data happens before the tdc variable, 2 and 3 take values from the first element of yinc, etc. By returning an index, the yinc variable can be of any data type. Using is.na() on the left side below causes the \emph{right} kind of NA to be inserted (this trick was stolen from the merge routine). If this is a first call, don't allow the new variable to overwrite a variable already existing in the data set, we found it leads to problems. (Usually it is a user mistake.) However, tdc calls themselves can stack. <>= # add a tdc variable newvar <- newdata[[argname[ii]]] # prior value (for sequential tmerge calls) if (argclass[ii] %in% c("tdc", "cumtdc")){ if (argname[[ii]] %in% tevent) stop("attempt to turn event variable", argname[[ii]], "into a tdc") if (!(argname[[ii]] %in% tdcvar)){ tdcvar <- c(tdcvar, argname[[ii]]) if (!is.null(newvar) && argclass[ii] == "tdc") { warning(paste0("replacement of variable '", argname[ii], "'")) newvar <- NULL } } } if (argclass[ii] == "tdc") { default <- argi$default # default value if (is.null(default)) default <- topt$tdcstart else if (length(default) !=1) stop("initial tdc value must be of length 1") # id can be any data type; feed integers to the C routine storage.mode(dstart) <- storage.mode(etime) <- "double" #if time is integer uid <- unique(baseid) index <- .Call(Ctmerge2, match(baseid, uid), dstart, match(id, uid), etime) if (!is.null(yinc)) newvar <- NULL # a tdc can't be updated, other than 0/1 if (is.null(newvar)) { if (is.null(yinc)) newvar <- ifelse(index==0, 0L, 1L) #add a 0/1 variable else { newvar <- yinc[pmax(1L, index)] if (any(index==0)) { if (is.na(default)) is.na(newvar) <- (index==0L) else { if (is.numeric(newvar)) newvar[index==0L] <- as.numeric(default) else { if (is.factor(newvar)) { # special case: if default isn't in the set of levels, # add it to the levels if (is.na(match(default, levels(newvar)))) levels(newvar) <- c(levels(newvar), default) } newvar[index== 0L] <- default } } } } } else { # update a 0/1 variable if (is.integer(newvar) && all(newvar==0L | newvar==1L)) newvar[index!=0L] <- 1L else stop("tdc update does not match prior variable type: ", argname[ii]) } tdcvar <- unique(c(tdcvar, argname[[ii]])) } @ Events and cumevents are easy because each affects only one interval. <>= # add events if (argclass[ii] %in% c("cumtdc", "cumevent")) { if (is.null(yinc)) yinc <- rep(1L, length(id)) else if (is.logical(yinc)) yinc <- as.numeric(yinc) # allow cumulative T/F if (!is.numeric(yinc)) stop("invalid increment for cumtdc or cumevent") } if (argclass[ii] == "cumevent"){ ykeep <- (yinc !=0) # ignore the addition of a censoring event yinc <- unlist(tapply(yinc, match(id, baseid), cumsum)) } if (argclass[ii] %in% c("event", "cumevent")) { if (!is.null(newvar)) { if (!argname[ii] %in% tevent) { #warning(paste0("non-event variable '", argname[ii], "' replaced by an event variable")) newvar <- NULL } else if (!is.null(yinc)) { if (class(newvar) != class(yinc)) stop("attempt to update an event variable with a different type") if (is.factor(newvar) && !all(levels(yinc) %in% levels(newvar))) stop("attemp to update an event variable and levels do not match") } } if (is.null(yinc)) yinc <- rep(1L, length(id)) if (is.null(newvar)) { if (is.numeric(yinc)) newvar <- rep(0L, nrow(newdata)) else if (is.factor(yinc)) newvar <- factor(rep(levels(yinc)[1], nrow(newdata)), levels(yinc)) else if (is.character(yinc)) newvar <- rep('', nrow(newdata)) else if (is.logical(yinc)) newvar <- rep(FALSE, nrow(newdata)) else stop("invalid value for a status variable") } keep <- (subtype==1 | subtype==3) # all other events are thrown away if (argclass[ii] == "cumevent") keep <- (keep & ykeep) newvar[indx2[keep]] <- yinc[keep] # add this into our list of 'this is an event type variable' if (!(argname[ii] %in% tevent)) { tevent <- c(tevent, argname[[ii]]) if (is.factor(yinc)) tcens <- c(tcens, list(levels(yinc)[1])) else if (is.logical(yinc)) tcens <- c(tcens, list(FALSE)) else if (is.character(yinc)) tcens <- c(tcens, list("")) else if (is.integer(yinc)) tcens <- c(tcens, list(0L)) else tcens <- c(tcens, list(0)) names(tcens) <- tevent } } else if (argclass[ii] == "cumtdc") { # process a cumtdc variable # I don't have a good way to catch the reverse of this user error if (argname[[ii]] %in% tevent) stop("attempt to turn event variable", argname[[ii]], "into a cumtdc") keep <- itype != 2 # changes after the last interval are ignored indx <- ifelse(subtype==1, indx1, ifelse(subtype==3, indx2+1L, indx2)) # we want to pass the right kind of NA to the C code default <- argi$default if (is.null(default)) default <- as.numeric(topt$tdcstart) else { if (length(default) != 1) stop("tdc initial value must be of length 1") if (!is.numeric(default)) stop("cumtdc initial value must be numeric") } if (is.null(newvar)) { # not overwriting a prior value if (is.null(argi$value)) newvar <- rep(0.0, nrow(newdata)) else newvar <- rep(default, nrow(newdata)) } # the increment must be numeric if (!is.numeric(newvar)) stop("data and starting value do not agree on data type") # id can be any data type; feed integers to the C routine storage.mode(yinc) <- storage.mode(dstart) <- "double" storage.mode(newvar) <- storage.mode(etime) <- "double" newvar <- .Call(Ctmerge, match(baseid, baseid), dstart, newvar, match(id, baseid)[keep], etime[keep], yinc[keep], indx[keep]) } newdata[[argname[ii]]] <- newvar @ Finish up by adding the attributes and the class <>= tm.retain <- list(tname = topt[c("idname", "tstartname", "tstopname")], n= nrow(newdata)) if (length(tevent)) tm.retain$tevent <- list(name = tevent, censor=tcens) if (length(tdcvar)>0) tm.retain$tdcvar <- tdcvar attr(newdata, "tm.retain") <- tm.retain attr(newdata, "tcount") <- rbind(attr(data1, "tcount"), tcount) attr(newdata, "call") <- Call row.names(newdata) <- NULL #These are a mess; kill them off. # Not that it works: R just assigns new row names. class(newdata) <- c("tmerge", "data.frame") newdata @ The summary routine is for checking: it simply prints out the attributes. <>= summary.tmerge <- function(object, ...) { if (!is.null(cl <- attr(object, "call"))) { cat("Call:\n") dput(cl) cat("\n") } print(attr(object, "tcount")) } # This could be smarter: if you only drop variables that are not known # to tmerge then it would be okay. But I currently like the "touch it # and it dies" philosophy "[.tmerge" <- function(x, ..., drop=TRUE){ class(x) <- "data.frame" attr(x, "tm.retain") <- NULL attr(x, "tcount") <- NULL attr(x, "call") <- NULL NextMethod(x) } @ \section{Linear models and contrasts} The primary contrast function is \code{yates}. This function does both simple and population contrasts; the name is a nod to the ``Yates weighted means'' method, the first population contrast that I know of. A second reason for the name is that the word ``contrast'' is already overused in the S/R lexicon. Both \code{yates} and \code{cmatrix} can be used with any model that returns the necessary portions, e.g., lm, coxph, or glm. They were written because I became embroiled in the ``type III'' controversy, and made it a goal to figure out what exactly it is that SAS does. If I had known that that quest would take multiple years would perhaps have never started. Population contrasts can result in some head scratching. It is easy to create the predicted value for any hypothethical subject from a model. A population prediction holds some data values constant and lets the others range over a population, giving a mean predicted value or population average. Population predictions for two treatments are the familiar g-estimates of causal models. We can take sums or differences of these predictions as well, e.g. to ask if they are significantly different. What can't be done is to work backwards from one of these contrasts to the populations, at least for continuous variables. If someone asks for an x contrast of 15-5 is this a sum of two population estimates at 15 and -5, or a difference? It's always hard to guess the mind of a user. Therefore what is needed is a fitted model, the term (covariate) of interest, levels of that covariate, a desired comparison, and a population. First is cmatrix routine. This is called by users to create a contrast matrix for a model, users can also construct their own contrast matrices. The result has two parts: the definition of a set of predicted values and a set of contrasts between those values. The routine requires a fit and a formula. The formula is simply a way to get a set of variable names: all those variables are the fixed ones in the population contrast, and all others form the ``population''. The result will be a matrix or list that has a label attribute containing the name of the term; this is used in printouts in the obvious way. Suppose that our model was \code{coxph(Surv(time, status) ~ age*sex + ph.ecog)}. Someone might want the population matrix for age, sex, ph.ecog, or age+ sex. For the last it doesn't matter if they say age+sex, age*sex, or age:sex. <>= cmatrix <- function(fit, term, test =c("global", "trend", "pairwise", "mean"), levels, assign) { # Make sure that "fit" is present and isn't missing any parts. if (missing(fit)) stop("a fit argument is required") Terms <- try(terms(fit), silent=TRUE) if (inherits(Terms, "try-error")) stop("the fit does not have a terms structure") else Terms <- delete.response(Terms) # y is not needed Tatt <- attributes(Terms) # a flaw in delete.response: it doesn't subset dataClasses Tatt$dataClasses <- Tatt$dataClasses[row.names(Tatt$factors)] test <- match.arg(test) if (missing(term)) stop("a term argument is required") if (is.character(term)) term <- formula(paste("~", term)) else if (is.numeric(term)) { if (all(term == floor(term) & term >0 & term < length(Tatt$term.labels))) term <- formula(paste("~", paste(Tatt$term.labels[term], collapse='+'))) else stop("a numeric term must be an integer between 1 and max terms in the fit") } else if (!inherits(term, "formula")) stop("the term must be a formula or integer") fterm <- delete.response(terms(term)) fatt <- attributes(fterm) user.name <- fatt$term.labels # what the user called it termname <- all.vars(fatt$variables) indx <- match(termname, all.vars(Tatt$variables)) if (any(is.na(indx))) stop("variable ", termname[is.na(indx)], " not found in the formula") # What kind of term is being tested? It can be categorical, continuous, # an interaction of only categorical terms, interaction of only continuous # terms, or a mixed interaction. # Key is a trick to get "zed" from ns(zed, df= dfvar) key <- sapply(Tatt$variables[-1], function(x) all.vars(x)[1]) parts <- names(Tatt$dataClasses)[match(termname, key)] types <- Tatt$dataClasses[parts] iscat <- as.integer(types=="factor" | types=="character") if (length(iscat)==1) termtype <- iscat else termtype <- 2 + any(iscat) + all(iscat) # Were levels specified? If so we either simply accept them (continuous), # or double check them (categorical) if (missing(levels)) { temp <- fit$xlevels[match(parts, names(fit$xlevels), nomatch=0)] if (length(temp) < length(parts)) stop("continuous variables require the levels argument") levels <- do.call(expand.grid, c(temp, stringsAsFactors=FALSE)) } else { #user supplied if (is.list(levels)) { if (is.null(names(levels))) { if (length(termname)==1) names(levels)== termname else stop("levels list requires named elements") } } if (is.data.frame(levels) || is.list(levels)) { index1 <- match(termname, names(levels), nomatch=0) # Grab the cols from levels that are needed (we allow it to have # extra, unused columns) levels <- as.list(levels[index1]) # now, levels = the set of ones that the user supplied (which might # be none, if names were wrong) if (length(levels) < length(termname)) { # add on the ones we don't have, using fit$xlevels as defaults temp <- fit$xlevels[parts[index1==0]] if (length(temp) > 0) { names(temp) <- termname[index1 ==0] levels <- c(levels, temp) } } index2 <- match(termname, names(levels), nomatch=0) if (any(index2==0)) stop("levels information not found for: ", termname[index2==0]) levels <- expand.grid(levels[index2], stringsAsFactors=FALSE) if (any(duplicated(levels))) stop("levels data frame has duplicates") } else if (is.matrix(levels)) { if (ncol(levels) != length(parts)) stop("levels matrix has the wrong number of columns") if (!is.null(dimnames(levels)[[2]])) { index <- match(termname, dimnames(levels)[[2]], nomatch=0) if (index==0) stop("matrix column names do no match the variable list") else levels <- levels[,index, drop=FALSE] } else if (ncol(levels) > 1) stop("multicolumn levels matrix requires column names") if (any(duplicated(levels))) stop("levels matrix has duplicated rows") levels <- data.frame(levels, stringsAsFactors=FALSE) names(levels) <- termname } else if (length(parts) > 1) stop("levels should be a data frame or matrix") else { levels <- data.frame(x=unique(levels), stringsAsFactors=FALSE) names(levels) <- termname } } # check that any categorical levels are legal for (i in which(iscat==1)) { xlev <- fit$xlevels[[parts[i]]] if (is.null(xlev)) stop("xlevels attribute not found for", termname[i]) temp <- match(levels[[i]], xlev) if (any(is.na(temp))) stop("invalid level for term", termname[i]) } rval <- list(levels=levels, termname=termname) # Now add the contrast matrix between the levels, if needed if (test=="global") { <> } else if (test=="pairwise") { <> } else if (test=="mean") { <> } else { <> } # the user can say "age" when the model has "ns(age)", but we need # the more formal label going forward rval <- list(levels=levels, termname=parts, cmat=cmat, iscat=iscat) class(rval) <- "cmatrix" rval } @ The default contrast matrix is a simple test of equality if there is only one term. If the term is the interaction of multiple categorical variables then we do an anova type decomposition. In other cases we currently fail. <>= if (TRUE) { #if (length(parts) ==1) { cmat <- diag(nrow(levels)) cmat[, nrow(cmat)] <- -1 # all equal to the last cmat <- cmat[-nrow(cmat),, drop=FALSE] } else if (termtype== 4) { # anova type stop("not yet done 1") } else stop("not yet done 2") @ The \code{pairwise} option creates a set of contrast matrices for all pairs of a factor. <>= nlev <- nrow(levels) # this is the number of groups being compared if (nlev < 2) stop("pairwise tests need at least 2 groups") npair <- nlev*(nlev-1)/2 if (npair==1) cmat <- matrix(c(1, -1), nrow=1) else { cmat <- vector("list", npair) k <- 1 cname <- rep("", npair) for (i in 1:(nlev-1)) { temp <- double(nlev) temp[i] <- 1 for (j in (i+1):nlev) { temp[j] <- -1 cmat[[k]] <- matrix(temp, nrow=1) temp[j] <- 0 cname[k] <- paste(i, "vs", j) k <- k+1 } } names(cmat) <- cname } @ The mean option compares each to the overall mean. <>= ntest <- nrow(levels) cmat <- vector("list", ntest) for (k in 1:ntest) { temp <- rep(-1/ntest, ntest) temp[k] <- (ntest-1)/ntest cmat[[k]] <- matrix(temp, nrow=1) } names(cmat) <- paste(1:ntest, "vs mean") @ The \code{linear} option is of interest for terms that have more than one column; the two most common cases are a factor variable or a spline. It forms a pair of tests, one for the linear and one for the nonlinear part. For non-linear functions such as splines we need some notion of the range of the data, since we want to be linear over the entire range. <>= cmat <- vector("list", 2) cmat[[1]] <- matrix(1:ntest, 1, ntest) cmat[[2]] <- diag(ntest) attr(cmat, "nested") <- TRUE if (is.null(levels[[1]])) { # a continuous variable, and the user didn't give levels for the test # look up the call and use the knots tcall <- Tatt$predvars[[indx + 1]] # skip the 'call' if (tcall[[1]] == as.name("pspline")) { bb <- tcall[["Boundary.knots"]] levels[[1]] <- seq(bb[1], bb[2], length=ntest) } else if (tcall[[1]] %in% c("ns", "bs")) { bb <- c(tcall[["Boundary.knots"]], tcall[["knots"]]) levels[[1]] <- sort(bb) } else stop("don't know how to do a linear contrast for this term") } @ Here are some helper routines. Formulas are from chapter 5 of Searle. The sums of squares only makes sense within a linear model. <>= gsolve <- function(mat, y, eps=sqrt(.Machine$double.eps)) { # solve using a generalized inverse # this is very similar to the ginv function of MASS temp <- svd(mat, nv=0) dpos <- (temp$d > max(temp$d[1]*eps, 0)) dd <- ifelse(dpos, 1/temp$d, 0) # all the parentheses save a tiny bit of time if y is a vector if (all(dpos)) x <- drop(temp$u %*% (dd*(t(temp$u) %*% y))) else if (!any(dpos)) x <- drop(temp$y %*% (0*y)) # extremely rare else x <-drop(temp$u[,dpos] %*%(dd[dpos] * (t(temp$u[,dpos, drop=FALSE]) %*% y))) attr(x, "df") <- sum(dpos) x } qform <- function(var, beta) { # quadratic form b' (V-inverse) b temp <- gsolve(var, beta) list(test= sum(beta * temp), df=attr(temp, "df")) } @ The next functions do the work. Some bookkeeping is needed for a missing value in beta: we leave that coefficient out of the linear predictor. If there are missing coefs then the variance matrix will not have those columns in any case. The nafun function asks if a linear combination is NA. It treats 0*NA as 0. <>= estfun <- function(cmat, beta, varmat) { nabeta <- is.na(beta) if (any(nabeta)) { k <- which(!nabeta) #columns to keep estimate <- drop(cmat[,k] %*% beta[k]) # vector of predictions evar <- cmat[,k] %*% varmat %*% t(cmat[,k, drop=FALSE]) list(estimate = estimate, var=evar) } else { list(estimate = drop(cmat %*% beta), var = cmat %*% varmat %*% t(cmat)) } } testfun <- function(cmat, beta, varmat, sigma2) { nabeta <- is.na(beta) if (any(nabeta)) { k <- which(!nabeta) #columns to keep estimate <- drop(cmat[,k] %*% beta[k]) # vector of predictions temp <- qform(cmat[,k] %*% varmat %*% t(cmat[,k,drop=FALSE]), estimate) rval <- c(chisq=temp$test, df=temp$df) } else { estimate <- drop(cmat %*% beta) temp <- qform(cmat %*% varmat %*% t(cmat), estimate) rval <- c(chisq=temp$test, df=temp$df) } if (!is.null(sigma2)) rval <- c(rval, ss= unname(rval[1]) * sigma2) rval } nafun <- function(cmat, est) { used <- apply(cmat, 2, function(x) any(x != 0)) any(used & is.na(est)) } @ Now for the primary function. The user may have a list of tests, or a single term. The first part of the function does the usual of grabbing arguments and then checking them. The fit object has to have the standard stuff: terms, assign, xlevels and contrasts. Attributes of the terms are used often enough that we copy them to \code{Tatt} to save typing. We will almost certainly need the model frame and/or model matrix as well. In the discussion below I use x1 to refer to the covariates/terms that are the target, e.g. \code{test='Mask'} to get the mean population values for each level of the Mask variable in the solder data set, and x2 to refer to all the other terms in the model, the ones that we average over. These are also referred to as U and V in the vignette. <>= yates <- function(fit, term, population=c("data", "factorial", "sas"), levels, test =c("global", "trend", "pairwise"), predict="linear", options, nsim=200, method=c("direct", "sgtt")) { Call <- match.call() if (missing(fit)) stop("a fit argument is required") Terms <- try(terms(fit), silent=TRUE) if (inherits(Terms, "try-error")) stop("the fit does not have a terms structure") else Terms <- delete.response(Terms) # y is not needed Tatt <- attributes(Terms) # a flaw in delete.response: it doesn't subset dataClasses Tatt$dataClasses <- Tatt$dataClasses[row.names(Tatt$factors)] if (inherits(fit, "coxphms")) stop("multi-state coxph not yet supported") if (is.list(predict) || is.function(predict)) { # someone supplied their own stop("user written prediction functions are not yet supported") } else { # call the method indx <- match(c("fit", "predict", "options"), names(Call), nomatch=0) temp <- Call[c(1, indx)] temp[[1]] <- quote(yates_setup) mfun <- eval(temp, parent.frame()) } if (is.null(mfun)) predict <- "linear" # we will need the original model frame and X matrix mframe <- fit$model if (is.null(mframe)) mframe <- model.frame(fit) Xold <- model.matrix(fit) if (is.null(fit$assign)) { # glm models don't save assign xassign <- attr(Xold, "assign") } else xassign <- fit$assign nvar <- length(xassign) nterm <- length(Tatt$term.names) termname <- rownames(Tatt$factors) iscat <- sapply(Tatt$dataClasses, function(x) x %in% c("character", "factor")) method <- match.arg(casefold(method), c("direct", "sgtt")) #allow SGTT if (method=="sgtt" && missing(population)) population <- "sas" if (inherits(population, "data.frame")) popframe <- TRUE else if (is.character(population)) { popframe <- FALSE population <- match.arg(tolower(population[1]), c("data", "factorial", "sas", "empirical", "yates")) if (population=="empirical") population <- "data" if (population=="yates") population <- "factorial" } else stop("the population argument must be a data frame or character") test <- match.arg(test) if (popframe || population != "data") weight <- NULL else { weight <- model.extract(mframe, "weights") if (is.null(weight)) { id <- model.extract(mframe, "id") if (!is.null(id)) { # each id gets the same weight count <- c(table(id)) weight <- 1/count[match(id, names(count))] } } } if (method=="sgtt" && (population !="sas" || predict != "linear")) stop("sgtt method only applies if population = sas and predict = linear") beta <- coef(fit, complete=TRUE) nabeta <- is.na(beta) # undetermined coefficients vmat <- vcov(fit, complete=FALSE) if (nrow(vmat) > sum(!nabeta)) { # a vcov method that does not obey the complete argument vmat <- vmat[!nabeta, !nabeta] } # grab the dispersion, needed for the writing an SS in linear models if (class(fit)[1] =="lm") sigma <- summary(fit)$sigma else sigma <- NULL # don't compute an SS column # process the term argument and check its legality if (missing(levels)) contr <- cmatrix(fit, term, test, assign= xassign) else contr <- cmatrix(fit, term, test, assign= xassign, levels = levels) x1data <- as.data.frame(contr$levels) # labels for the PMM values # Make the list of X matrices that drive everything: xmatlist # (Over 1/2 the work of the whole routine) xmatlist <- yates_xmat(Terms, Tatt, contr, population, mframe, fit, iscat) # check rows of xmat for estimability <> # Drop missing coefficients, and use xmatlist to compute the results beta <- beta[!nabeta] if (predict == "linear" || is.null(mfun)) { # population averages of the simple linear predictor <> } else { <> } result$call <- Call class(result) <- "yates" result } @ Models with factor variables may often lead to population predictions that involve non-estimable functions, particularly if there are interactions and the user specifies a factorial population. If there are any missing coefficients we have to do formal checking for this: any given row of the new $X$ matrix, for prediction, must be in the row space of the original $X$ matrix. If this is true then a regression of a new row on the old $X$ will have residuals of zero. It is not possible to derive this from the pattern of NA coefficients alone. Set up a function that returns a true/false vector of whether each row of a matrix is estimable. This test isn't relevant if population=none. <>= if (any(is.na(beta)) && (popframe || population != "none")) { Xu <- unique(Xold) # we only need unique rows, saves time to do so if (inherits(fit, "coxph")) X.qr <- qr(t(cbind(1.0,Xu))) else X.qr <- qr(t(Xu)) # QR decomposition of the row space estimcheck <- function(x, eps= sqrt(.Machine$double.eps)) { temp <- abs(qr.resid(X.qr, t(x))) # apply(abs(temp), 1, function(x) all(x < eps)) # each row estimable all(temp < eps) } estimable <- sapply(xmatlist, estimcheck) } else estimable <- rep(TRUE, length(xmatlist)) @ When the prediction target is $X\beta$ there is a four step process: build the reference population, create the list of X matrices (one prediction matrix for each for x1 value), column means of each X form each row of the contrast matrix Cmat, and then use Cmat to get the pmm values and tests of the pmm values. <>= #temp <- match(contr$termname, colnames(Tatt$factors)) #if (any(is.na(temp))) # stop("term '", contr$termname[is.na(temp)], "' not found in the model") meanfun <- if (is.null(weight)) colMeans else function(x) { colSums(x*weight)/ sum(weight)} Cmat <- t(sapply(xmatlist, meanfun))[,!nabeta] # coxph model: the X matrix is built as though an intercept were there (the # baseline hazard plays that role), but then drop it from the coefficients # before computing estimates and tests. If there was a strata * covariate # interaction there will be many more colums to drop. if (inherits(fit, "coxph")) { nkeep <- length(fit$means) # number of non-intercept columns col.to.keep <- seq(to=ncol(Cmat), length= nkeep) Cmat <- Cmat[,col.to.keep, drop=FALSE] offset <- -sum(fit$means[!nabeta] * beta) # recenter the predictions too } else offset <- 0 # Get the PMM estimates, but only for estimable ones estimate <- cbind(x1data, pmm=NA, std=NA) if (any(estimable)) { etemp <- estfun(Cmat[estimable,,drop=FALSE], beta, vmat) estimate$pmm[estimable] <- etemp$estimate + offset estimate$std[estimable] <- sqrt(diag(etemp$var)) } # Now do tests on the PMM estimates, one by one if (method=="sgtt") { <> } else { if (is.list(contr$cmat)) { test <- t(sapply(contr$cmat, function(x) testfun(x %*% Cmat, beta, vmat, sigma^2))) natest <- sapply(contr$cmat, nafun, estimate$pmm) } else { test <- testfun(contr$cmat %*% Cmat, beta, vmat, sigma^2) test <- matrix(test, nrow=1, dimnames=list("global", names(test))) natest <- nafun(contr$cmat, estimate$pmm) } if (any(natest)) test[natest,] <- NA } if (any(estimable)){ # Cmat[!estimable,] <- NA result <- list(estimate=estimate, test=test, mvar=etemp$var, cmat=Cmat) } else result <- list(estimate=estimate, test=test, mvar=NA) if (method=="sgtt") result$SAS <- Smat @ In the non-linear case the mfun object is either a single function or a list containing two functions \code{predict} and \code{summary}. The predict function is handed a vector $\eta = X\beta$ along with the $X$ matrix, though most methods don't use $X$. The result of predict can be a vector or a matrix. For coxph models we add on an ``intercept coef'' that will center the predictions. <>= xall <- do.call(rbind, xmatlist)[,!nabeta, drop=FALSE] if (inherits(fit, "coxph")) { xall <- xall[,-1, drop=FALSE] # remove the intercept eta <- xall %*% beta -sum(fit$means[!nabeta]* beta) } else eta <- xall %*% beta n1 <- nrow(xmatlist[[1]]) # all of them are the same size index <- rep(1:length(xmatlist), each = n1) if (is.function(mfun)) predfun <- mfun else { # double check the object if (!is.list(mfun) || any(is.na(match(c("predict", "summary"), names(mfun)))) || !is.function(mfun$predic) || !is.function(mfun$summary)) stop("the prediction should be a function, or a list with two functions") predfun <- mfun$predict sumfun <- mfun$summary } pmm <- predfun(eta, xall) n2 <- length(eta) if (!(is.numeric(pmm)) || !(length(pmm)==n2 || nrow(pmm)==n2)) stop("prediction function should return a vector or matrix") pmm <- rowsum(pmm, index, reorder=FALSE)/n1 pmm[!estimable,] <- NA # get a sample of coefficients, in order to create a variance # this is lifted from the mvtnorm code (can't include a non-recommended # package in the dependencies) tol <- sqrt(.Machine$double.eps) if (!isSymmetric(vmat, tol=tol, check.attributes=FALSE)) stop("variance matrix of the coefficients is not symmetric") ev <- eigen(vmat, symmetric=TRUE) if (!all(ev$values >= -tol* abs(ev$values[1]))) warning("variance matrix is numerically not positive definite") Rmat <- t(ev$vectors %*% (t(ev$vectors) * sqrt(ev$values))) bmat <- matrix(rnorm(nsim*ncol(vmat)), nrow=nsim) %*% Rmat bmat <- bmat + rep(beta, each=nsim) # add the mean # Now use this matrix of noisy coefficients to get a set of predictions # and use those to create a variance matrix # Since if Cox we need to recenter each run sims <- array(0., dim=c(nsim, nrow(pmm), ncol(pmm))) if (inherits(fit, 'coxph')) offset <- bmat %*% fit$means[!nabeta] else offset <- rep(0., nsim) for (i in 1:nsim) sims[i,,] <- rowsum(predfun(xall %*% bmat[i,] - offset[i]), index, reorder=FALSE)/n1 mvar <- var(sims[,,1]) # this will be used for the tests estimate <- cbind(x1data, pmm=unname(pmm[,1]), std= sqrt(diag(mvar))) # Now do the tests, on the first column of pmm only if (is.list(contr$cmat)) { test <- t(sapply(contr$cmat, function(x) testfun(x, pmm[,1], mvar[estimable, estimable], NULL))) natest <- sapply(contr$cmat, nafun, pmm[,1]) } else { test <- testfun(contr$cmat, pmm[,1], mvar[estimable, estimable], NULL) test <- matrix(test, nrow=1, dimnames=list(contr$termname, names(test))) natest <- nafun(contr$cmat, pmm[,1]) } if (any(natest)) test[natest,] <- NA if (any(estimable)) result <- list(estimate=estimate,test=test, mvar=mvar) else result <- list(estimate=estimate, test=test, mvar=NA) # If there were multiple columns from predfun, compute the matrix of # results and variances if (ncol(pmm) > 1 && any(estimable)){ pmm <- apply(sims, 2:3, mean) mvar2 <- apply(sims, 2:3, var) # Call the summary function, if present if (is.list(mfun)) result$summary <- sumfun(pmm, mvar2) else { result$pmm <- pmm result$mvar2 <- mvar2 } } @ Build the population data set. If the user provided a data set as the population then the task is fairly straightforward: we manipulate the data set and then call model.frame followed by model.matrix in the usual way. The primary task in that case is to verify that the data has all the needed variables. Otherwise we have to be subtle. \begin{enumerate} \item We have ready access to a model frame, but not to the data. Consider a spline term for instance --- it's not always possible to go backwards and get the data. \item We need to manipulate this model frame, e.g., make everyone treatment=A, then repeat with everyone treatment B. \item We need to do it in a way that makes the frame still look like a correct model frame to R. This requires care. \end{enumerate} For population= factorial we create a population data set that has all the combinations. If there are three adjusters z1, z2 and z3 with 2, 3, and 5 levels, respectively, the new data set will have 30 rows. If the primary model didn't have any z1*z2*z3 terms in it we likely could get by with less, but it's not worth the programming effort to figure that out: predicted values are normally fairly cheap. For population=sas we need a mixture: categoricals are factorial and others are data. Say there were categoricals with 3 and 5 levels, so the factorial data set has 15 obs, while the overall n is 50. We need a data set of 15*50 observations to ensure all combinations of the two categoricals with each continuous line. An issue with data vs model is names. Suppose the original model was \code{lm(y \textasciitilde ns(age,4) + factor(ph.ecog))}. In the data set the variable name is ph.ecog, in the model frame, the xlevels list, and terms structure it is factor(ph.ecog). The data frame has individual columns for the four variables, the model frame is a list with 3 elements, one of which is named ``ns(age, 4)'': notice the extra space before the 4 compared to what was typed. <>= yates_xmat <- function(Terms, Tatt, contr, population, mframe, fit, iscat, weight) { # which variables(s) are in x1 (variables of interest) # First a special case of strata(grp):x, which causes strata(grp) not to # appear as a column if (is.na(match(contr$termname, colnames(Tatt$factors)))) { x1indx <- (contr$termname== rownames(Tatt$factors)) names(x1indx) <- rownames(Tatt$factors) if (!any(x1indx)) stop(paste("variable", contr$termname, "not found")) } else x1indx <- apply(Tatt$factors[,contr$termname,drop=FALSE] >0, 1, any) x2indx <- !x1indx # adjusters if (inherits(population, "data.frame")) pdata <- population #user data else if (population=="data") pdata <- mframe #easy case else if (population=="factorial") pdata <- yates_factorial_pop(mframe, Terms, x2indx, fit$xlevels) else if (population=="sas") { if (all(iscat[x2indx])) pdata <- yates_factorial_pop(mframe, Terms, x2indx, fit$xlevels) else if (!any(iscat[x2indx])) pdata <- mframe # no categoricals else { # mixed population pdata <- yates_factorial_pop(mframe, Terms, x2indx & iscat, fit$xlevels) n2 <- nrow(pdata) pdata <- pdata[rep(1:nrow(pdata), each=nrow(mframe)), ] row.names(pdata) <- 1:nrow(pdata) # fill in the continuous k <- rep(1:nrow(mframe), n2) for (i in which(x2indx & !iscat)) { j <- names(x1indx)[i] if (is.matrix(mframe[[j]])) pdata[[j]] <- mframe[[j]][k,, drop=FALSE] else pdata[[j]] <- (mframe[[j]])[k] attributes(pdata[[j]]) <- attributes(mframe[[j]]) } } } else stop("unknown population") # this should have been caught earlier # Now create the x1 data set, the unique rows we want to test <> xmatlist } @ Build a factorial data set from a model frame. <>= yates_factorial_pop <- function(mframe, terms, x2indx, xlevels) { x2name <- names(x2indx)[x2indx] dclass <- attr(terms, "dataClasses")[x2name] if (!all(dclass %in% c("character", "factor"))) stop("population=factorial only applies if all the adjusting terms are categorical") nvar <- length(x2name) n2 <- sapply(xlevels[x2name], length) # number of levels for each n <- prod(n2) # total number of rows needed pdata <- mframe[rep(1, n), -1] # toss the response row.names(pdata) <- NULL # throw away funny names n1 <- 1 for (i in 1:nvar) { j <- rep(rep(1:n2[i], each=n1), length=n) xx <- xlevels[[x2name[i]]] if (dclass[i] == "factor") pdata[[x2name[i]]] <- factor(j, 1:n2[i], labels= xx) else pdata[[x2name[i]]] <- xx[j] n1 <- n1 * n2[i] } attr(pdata, "terms") <- terms pdata } @ The next section builds a set of X matrices, one for each level of the x1 combination. The following was learned by reading the source code for model.matrix: \begin{itemize} \item If pdata has no terms attribute then model.matrix will call model.frame first, otherwise not. The xlev argument is passed forward to model.frame but is otherwise unused. \item If necessary, it will reorder the columns of pdata to match the terms, though I try to avoid that. \item Toss out the response variable, if present. \item Any character variables are turned into factors. The dataClass attribute of the terms object is not consulted. \item For each column that is a factor \begin{itemize} \item if it alreay has a contrasts attribute, it is left alone. \item otherwise a contrasts attribute is added using a matching element from contrasts.arg, if present, otherwise the global default \item contrasts.arg must be a list, but it does not have to contain all factors \end{itemize} \item Then call the internal C code \end{itemize} If pdata already is a model frame we want to leave it as one, so as to avoid recreating the raw data. If x1data comes from the user though, so we need to do that portion of model.frame processing ourselves, in order to get it into the right form. Always turn characters into factors, since individual elements of \code{xmatlist} will have only a subset of the x1 variables. One nuisance is name matching. Say the model had \code{factor(ph.ecog)} as a term; then \code{fit\$xlevels} will have `factor(ph.ecog)' as a name but the user will likely have created a data set using `ph.ecog' as the name. <>= if (is.null(contr$levels)) stop("levels are missing for this contrast") x1data <- as.data.frame(contr$levels) # in case it is a list x1name <- names(x1indx)[x1indx] for (i in 1:ncol(x1data)) { if (is.character(x1data[[i]])) { if (is.null(fit$xlevels[[x1name[i]]])) x1data[[i]] <- factor(x1data[[i]]) else x1data[[i]] <- factor(x1data[[i]], fit$xlevels[[x1name[i]]]) } } xmatlist <- vector("list", nrow(x1data)) if (is.null(attr(pdata, "terms"))) { np <- nrow(pdata) k <- match(x1name, names(pdata), nomatch=0) if (any(k>0)) pdata <- pdata[, -k, drop=FALSE] # toss out yates var for (i in 1:nrow(x1data)) { j <- rep(i, np) tdata <- cbind(pdata, x1data[j,,drop=FALSE]) # new data set xmatlist[[i]] <- model.matrix(Terms, tdata, xlev=fit$xlevels, contrast.arg= fit$contrasts) } } else { # pdata is a model frame, convert x1data # if the name and the class agree we go forward simply index <- match(names(x1data), names(pdata), nomatch=0) if (all(index >0) && identical(lapply(x1data, class), lapply(pdata, class)[index]) & identical(sapply(x1data, ncol) , sapply(pdata, ncol)[index])) { # everything agrees for (i in 1:nrow(x1data)) { j <- rep(i, nrow(pdata)) tdata <- pdata tdata[,names(x1data)] <- x1data[j,] xmatlist[[i]] <- model.matrix(Terms, tdata, contrasts.arg= fit$contrasts) } } else { # create a subset of the terms structure, for x1 only # for instance the user had age=c(75, 75, 85) and the term was ns(age) # then call model.frame to fix it up x1term <- Terms[which(x1indx)] x1name <- names(x1indx)[x1indx] attr(x1term, "dataClasses") <- Tatt$dataClasses[x1name] # R bug x1frame <- model.frame(x1term, x1data, xlev=fit$xlevels[x1name]) for (i in 1:nrow(x1data)) { j <- rep(i, nrow(pdata)) tdata <- pdata tdata[,names(x1frame)] <- x1frame[j,] xmatlist[[i]] <- model.matrix(Terms, tdata, xlev=fit$xlevels, contrast.arg= fit$contrasts) } } } @ The decompostion based algorithm for SAS type 3 tests. Ignore the set of contrasts cmat since the algorithm can only do a global test. We mostly mimic the SAS GLM algorithm. For the generalized Cholesky decomposition $LDL' = X'X$, where $L$ is lower triangular with $L_{ii}=1$ and $D$ is diagonal, the set of contrasts $L'\beta$ gives the type I sequential sums of squares, partitioning the rows of $L$ into those for term 1, term 2, etc. If $X$ is the design matrix for a balanced factorial design then it is also true that $L_{ij}=0$ unless term $j$ includes term $i$, e.g., x1:x2 includes x1. These blocks of zeros mean that changing the order of the terms in the model simply rearranges $L$, and individual tests are unchanged. This is precisely the definition of a type III contrast in SAS. With a bit of reading between the lines the ``four types of estimable functions'' document suggests the following algorithm: \begin{enumerate} \item Start with an $X$ matrix in standard order of intercept, main effects, first order interactions, etc. Code any categorical variable with $k$ levels as $k$ 0/1 columns. An interaction of two categoricals with $k$ and $l$ levels will have $kl$ columns, etc. \item Create the dependency matrix $D = (X'X)^-(X'X)$. If column $i$ of $X$ can be written as a linear combination of prior columns, then column $i$ of $D$ contains that combination. Other columns of $D$ match the identity matrix. \item Intitialize $L = D$. \item For any row $i$ and $j$ such that $i$ is contained in $j$, make $L_i$ orthagonal to $L_j$. \end{enumerate} The algorithm appears to work in almost all cases, an exception is when the type 3 test has fewer degrees of freedom that we would expect. Continuous variables are not orthagonalized in the SAS type III approach, nor any interaction that contains a continuous variable as one of its parts. To find the nested terms first note which rows of \code{factors} refer to categorical variables (the \code{iscat} variable); columns of \code{factors} that are non-zero only in categorical rows are the ``categorical'' columns. A term represented by one column in \code{factors} ``contains'' the term represented in some other column iff it's non-zero elements are a superset. We have to build a new X matrix that is the expanded SAS coding, and are only able to do that for models that have an intercept, and use contr.treatement or contr.SAS coding. <>= # It would be simplest to have the contrasts.arg to be a list of function names. # However, model.matrix plays games with the calling sequence, and any function # defined at this level will not be seen. Instead create a list of contrast # matrices. temp <- sapply(fit$contrasts, function(x) (is.character(x) && x %in% c("contr.SAS", "contr.treatment"))) if (!all(temp)) stop("yates sgtt method can only handle contr.SAS or contr.treatment") temp <- vector("list", length(fit$xlevels)) names(temp) <- names(fit$xlevels) for (i in 1:length(fit$xlevels)) { cmat <- diag(length(fit$xlevels[[i]])) dimnames(cmat) <- list(fit$xlevels[[i]], fit$xlevels[[i]]) if (i>1 || Tatt$intercept==1) { if (fit$contrasts[[i]] == "contr.treatment") cmat <- cmat[, c(2:ncol(cmat), 1)] } temp[[i]] <- cmat } sasX <- model.matrix(formula(fit), data=mframe, xlev=fit$xlevels, contrasts.arg=temp) sas.assign <- attr(sasX, "assign") # create the dependency matrix D. The lm routine is unhappy if it thinks # the right hand and left hand sides are the same, fool it with I(). # We do this using the entire X matrix even though only categoricals will # eventually be used; if a continuous variable made it NA we need to know. D <- coef(lm(sasX ~ I(sasX) -1)) dimnames(D)[[1]] <- dimnames(D)[[2]] #get rid if the I() names zero <- is.na(D[,1]) # zero rows, we'll get rid of these later D <- ifelse(is.na(D), 0, D) # make each row orthagonal to rows for other terms that contain it # Containing blocks, if any, will always be below # this is easiest to do with the transposed matrix # Only do this if both row i and j are for a categorical variable if (!all(iscat)) { # iscat marks variables in the model frame as categorical # tcat marks terms as categorical. For x1 + x2 + x1:x2 iscat has # 2 entries and tcat has 3. tcat <- (colSums(Tatt$factors[!iscat,,drop=FALSE]) == 0) } else tcat <- rep(TRUE, max(sas.assign)) # all vars are categorical B <- t(D) dimnames(B)[[2]] <- paste0("L", 1:ncol(B)) # for the user if (ncol(Tatt$factors) > 1) { share <- t(Tatt$factors) %*% Tatt$factors nc <- ncol(share) for (i in which(tcat[-nc])) { j <- which(share[i,] > 0 & tcat) k <- j[j>i] # terms that I need to regress out if (length(k)) { indx1 <- which(sas.assign ==i) indx2 <- which(sas.assign %in% k) B[,indx1] <- resid(lm(B[,indx1] ~ B[,indx2])) } } } # Cut B back down to the non-missing coefs of the original fit Smat <- t(B)[!zero, !zero] Sassign <- xassign[!nabeta] @ Although the SGTT does test for all terms, we only want to print out the ones that were asked for. <>= keep <- match(contr$termname, colnames(Tatt$factors)) if (length(keep) > 1) { # more than 1 term in the model test <- t(sapply(keep, function(i) testfun(Smat[Sassign==i,,drop=FALSE], beta, vmat, sigma^2))) rownames(test) <- contr$termname } else { test <- testfun(Smat[Sassign==keep,, drop=FALSE], beta, vmat, sigma^2) test <- matrix(test, nrow=1, dimnames=list(contr$termname, names(test))) } @ The print routine places the population predicted values (PPV) alongside the tests on those values. Defaults are copied from printCoefmat. <>= print.yates <- function(x, digits = max(3, getOption("digits") -2), dig.tst = max(1, min(5, digits-1)), eps=1e-8, ...) { temp1 <- x$estimate temp1$pmm <- format(temp1$pmm, digits=digits) temp1$std <- format(temp1$std, digits=digits) # the spaces help separate the two parts of the printout temp2 <- cbind(test= paste(" ", rownames(x$test)), data.frame(x$test), stringsAsFactors=FALSE) row.names(temp2) <- NULL temp2$Pr <- format.pval(pchisq(temp2$chisq, temp2$df, lower.tail=FALSE), eps=eps, digits=dig.tst) temp2$chisq <- format(temp2$chisq, digits= dig.tst) temp2$df <- format(temp2$df) if (!is.null(temp2$ss)) temp2$ss <- format(temp2$ss, digits=digits) if (nrow(temp1) > nrow(temp2)) { dummy <- temp2[1,] dummy[1,] <- "" temp2 <- rbind(temp2, dummy[rep(1, nrow(temp1)-nrow(temp2)),]) } if (nrow(temp2) > nrow(temp1)) { # get rid of any factors before padding for (i in which(sapply(temp1, is.factor))) temp1[[i]] <- as.character(temp1[[i]]) dummy <- temp1[1,] dummy[1,] <- "" temp1 <- rbind(temp1, dummy[rep(1, nrow(temp2)- nrow(temp1)),]) } print(cbind(temp1, temp2), row.names=FALSE) invisible(x) } @ Routines to allow yates to interact with other models. Each is called with the fitted model and the type of prediction. It should return NULL when the type is a linear predictor, since the parent routine has a very efficient approach in that case. Otherwise it returns a function that will be applied to each value $\eta$, from each row of a prediction matrix. <>= yates_setup <- function(fit, ...) UseMethod("yates_setup", fit) yates_setup.default <- function(fit, type, ...) { if (!missing(type) && !(type %in% c("linear", "link"))) warning("no yates_setup method exists for a model of class ", class(fit)[1], " and estimate type ", type, ", linear predictor estimate used by default") NULL } yates_setup.glm <- function(fit, predict = c("link", "response", "terms", "linear"), ...) { type <- match.arg(predict) if (type == "link" || type== "linear") NULL # same as linear else if (type == "response") { finv <- family(fit)$linkinv function(eta, X) finv(eta) } else if (type == "terms") stop("type terms not yet supported") } @ For the coxph routine, we are making use of the R environment by first defining the baseline hazard and then defining the predict and summary functions. This means that those functions have access to the baseline. <>= yates_setup.coxph <- function(fit, predict = c("lp", "risk", "expected", "terms", "survival", "linear"), options, ...) { type <- match.arg(predict) if (type=="lp" || type == "linear") NULL else if (type=="risk") function(eta, X) exp(eta) else if (type == "survival") { # If there are strata we need to do extra work # if there is an interaction we want to suppress a spurious warning suppressWarnings(baseline <- survfit(fit, censor=FALSE)) if (missing(options) || is.null(options$rmean)) rmean <- max(baseline$time) # max death time else rmean <- options$rmean if (!is.null(baseline$strata)) stop("stratified models not yet supported") cumhaz <- c(0, baseline$cumhaz) tt <- c(diff(c(0, pmin(rmean, baseline$time))), 0) predict <- function(eta, ...) { c2 <- outer(exp(drop(eta)), cumhaz) # matrix of values surv <- exp(-c2) meansurv <- apply(rep(tt, each=nrow(c2)) * surv, 1, sum) cbind(meansurv, surv) } summary <- function(surv, var) { bsurv <- t(surv[,-1]) std <- t(sqrt(var[,-1])) chaz <- -log(bsurv) zstat <- -qnorm((1-baseline$conf.int)/2) baseline$lower <- exp(-(chaz + zstat*std)) baseline$upper <- exp(-(chaz - zstat*std)) baseline$surv <- bsurv baseline$std.err <- std/bsurv baselinecumhaz <- chaz baseline } list(predict=predict, summary=summary) } else stop("type expected is not supported") } @ \section{The cox.zph function} The simplest test of proportional hazards is to use a time dependent coefficient $\beta(t) = a + bt$. Then $\beta(t) x = ax + b*(tx)$, and the extended coefficients $a$ and $b$ can be obtained from a Cox model with an extra 'fake' covariate $tx$. More generally, replace $t$ with some function $g(t)$, which gives rise to an entire family of tests. An efficient assessment of this extended model can be done using a score test. \begin{itemize} \item Augment the original variables $x_1, \ldots x_k$ with $k$ new ones $g(t)x_1, \ldots, g(t)x_k$ \item Compute the first and second derivatives $U$ and $H$ of the Cox model at the starting estimate of $(\hat\beta, 0)$; prior covariates at their prior values, and the new covariates at 0. No iteration is done. This can be done efficiently with a modified version of the primary C routines for coxph. \item By design, the first $k$ elements of $U$ will be zero. Thus the first iteration of the new coefficients, and the score tests for them, are particularly easy. \end{itemize} The information or Hessian matrix for a Cox model is $$ \sum_{j \in deaths} V(t_j) = \sum_jV_j$$ where $V_j$ is the variance matrix of the weighted covariate values, over all subjects at risk at time $t_j$. Then the expanded information matrix for the score test is \begin{align*} H &= \left(\begin{array}{cc} H_1 & H_2 \\ H_2' & H_3 \end{array} \right) \\ H_1 &= \sum V(t_j) \\ H_2 &= \sum V(t_j) g(t_j) \\ H_3 &= \sum V(t_j) g^2(t_j) \end{align*} The inverse of the matrix will be more numerically stable if $g(t)$ is centered at zero, and this does not change the test statistic. In the usual case $V(t)$ is close to constant in time --- the variance of $X$ does not change rapidly --- and then $H_2$ is approximately zero. The original cox.zph used an approximation, which is to assume that $V(t)$ is exactly constant. In that case $H_2=0$ and $H_3= \sum V(t_j) \sum g^2(t_j)$ and the test is particularly easy to compute. This assumption of identical components can fail badly for models with a covariate by strata interaction, and for some models with covariate dependent censoring. Multi-state models finally forced a change. The newer version of the routine has two separate tracks: for the formal test and another for the residuals. <>= cox.zph <- function(fit, transform='km', terms=TRUE, singledf =FALSE, global=TRUE) { Call <- match.call() if (!inherits(fit, "coxph") && !inherits(fit, "coxme")) stop ("argument must be the result of Cox model fit") if (inherits(fit, "coxph.null")) stop("there are no score residuals for a Null model") if (!is.null(attr(terms(fit), "specials")[["tt"]])) stop("function not defined for models with tt() terms") if (inherits(fit, "coxme")) { # drop all mention of the random effects, before getdata fit$formula <- fit$formula$fixed fit$call$formula <- fit$formula } cget <- coxph.getdata(fit, y=TRUE, x=TRUE, stratax=TRUE, weights=TRUE) y <- cget$y ny <- ncol(y) event <- (y[,ny] ==1) if (length(cget$strata)) istrat <- as.integer(cget$strata) - 1L # number from 0 for C else istrat <- rep(0L, nrow(y)) # if terms==FALSE the singledf argument is moot, but setting a value # leads to a simpler path through the code if (!terms) singledf <- FALSE <> <> <> <> rval$transform <- tname rval$call <- Call class(rval) <- "cox.zph" return(rval) } print.cox.zph <- function(x, digits = max(options()$digits - 4, 3), signif.stars=FALSE, ...) { invisible(printCoefmat(x$table, digits=digits, signif.stars=signif.stars, P.values=TRUE, has.Pvalue=TRUE, ...)) } @ The user can use $t$ or $g(t)$ as the multiplier of the covariates. The default is to use the KM, only because that seems to be best at avoiding edge cases. <>= times <- y[,ny-1] if (is.character(transform)) { tname <- transform ttimes <- switch(transform, 'identity'= times, 'rank' = rank(times), 'log' = log(times), 'km' = { temp <- survfitKM(factor(rep(1L, nrow(y))), y, se.fit=FALSE) # A nuisance to do left continuous KM indx <- findInterval(times, temp$time, left.open=TRUE) 1.0 - c(1, temp$surv)[indx+1] }, stop("Unrecognized transform")) } else { tname <- deparse(substitute(transform)) if (length(tname) >1) tname <- 'user' ttimes <- transform(times) } gtime <- ttimes - mean(ttimes[event]) # Now get the U, information, and residuals if (ny==2) { ord <- order(istrat, y[,1]) -1L resid <- .Call(Czph1, gtime, y, X, eta, cget$weights, istrat, fit$method=="efron", ord) } else { ord1 <- order(-istrat, -y[,1]) -1L # reverse time for zph2 ord <- order(-istrat, -y[,2]) -1L resid <- .Call(Czph2, gtime, y, X, eta, cget$weights, istrat, fit$method=="efron", ord1, ord) } @ The result has a score vector of length $2p$ where $p$ is the number of variables and an information matrix that is $2p$ by $2p$. This is done with C code that is a simple variation on iteration 1 for a coxph model. If \code{singledf} is TRUE then treat each term as a single degree of freedom test, otherwise as a multi-degree of freedom. If terms=FALSE test each covariate individually. If all the variables are univariate this is a moot point. The survival routines return Splus style assign components, that is a list with one element per term, each element an integer vector of coefficient indices. The asgn vector is our main workhorse: loop over asgn to process term by term. \begin{itemize} \item if term=FALSE, set make a new asgn with one coef per term \item if a coefficient is NA, remove it from the relevant asgn vector \item frailties and penalized coxme coefficients are ignored: remove their element from the asgn list \end{itemize} For random effects models, including both frailty and coxme results, the random effect is included in the linear.predictors component of the fit. This allows us to do score tests for the other terms while effectively holding the random effect fixed. If there are any NA coefficients these are redundant variables. It's easiest to simply get rid of them at the start by fixing up X, varnames, asgn, and nvar. <>= eta <- fit$linear.predictors X <- cget$x varnames <- names(fit$coefficients) nvar <- length(varnames) if (!terms) { # create a fake asgn that has one value per coefficient asgn <- as.list(1:nvar) names(asgn) <- names(fit$coefficients) } else if (inherits(fit, "coxme")) { asgn <- attrassign(cget$x, terms(fit)) # allow for a spelling inconsistency in coxme, later fixed if (is.null(fit$linear.predictors)) eta <- fit$linear.predictor fit$df <- NULL # don't confuse later code } else asgn <- fit$assign if (!is.list(asgn)) stop ("unexpected assign component") frail <- grepl("frailty(", names(asgn), fixed=TRUE) if (any(frail)) { dcol <- unlist(asgn[frail]) # remove these columns from X X <- X[, -dcol, drop=FALSE] asgn <- asgn[!frail] # frailties don't appear in the varnames, so no change there } nterm <- length(asgn) termname <- names(asgn) if (any(is.na(fit$coefficients))) { keep <- !is.na(fit$coefficients) varnames <- varnames[keep] X <- X[,keep] # fix up assign new <- unname(unlist(asgn))[keep] # the ones to keep asgn <- sapply(asgn, function(x) { i <- match(x, new, nomatch=0) i[i>0]}) asgn <- asgn[sapply(asgn, length)>0] # drop any that were lost termname <- names(asgn) nterm <- length(asgn) # asgn will be a list nvar <- length(new) } @ The zph1 and zph2 functions do not consider penalties, so we need to add those back in after the call. Nothing needs to be done wrt the first derivative: we already ignore the first ncoef elements of the returned first derivative (u) vector, which would have had a penalty. The second portion of u is for beta=0, and all of the penalties that currently are implemented have first derivative 0 at 0. For the second derivative, the current penalties (frailty, rigde, pspline) have a second derivative penalty that is independent of beta-hat. The coxph result contains the numeric value of the penalty at the solution, and we use a score test that would penalize the new time*pspline() term in the same way as the pspline term was penalized. If no coefficients were missing then allvar will be 1:n, otherwise it will have holes. <>= test <- double(nterm+1) df <- rep(1L, nterm+1) u0 <- rep(0, nvar) if (!is.null(fit$coxlist2)) { # there are penalized terms pmat <- matrix(0., 2*nvar, 2*nvar) # second derivative penalty pmat[1:nvar, 1:nvar] <- fit$coxlist2$second pmat[1:nvar + nvar, 1:nvar + nvar] <- fit$coxlist2$second imatr <- resid$imat + pmat } else imatr <- resid$imat for (ii in 1:nterm) { jj <- asgn[[ii]] kk <- c(1:nvar, jj+nvar) imat <- imatr[kk, kk] u <- c(u0, resid$u[jj+nvar]) if (singledf && length(jj) >1) { vv <- solve(imat)[-(1:nvar), -(1:nvar)] t1 <- sum(fit$coef[jj] * resid$u[jj+nvar]) test[ii] <- t1^2 * (fit$coef[jj] %*% vv %*% fit$coef[jj]) df[ii] <- 1 } else { test[ii] <- drop(solve(imat,u) %*% u) if (is.null(fit$df)) df[ii] <- length(jj) else df[ii] <- fit$df[ii] } } #Global test if (global) { u <- c(u0, resid$u[-(1:nvar)]) test[nterm+1] <- solve(imatr, u) %*% u if (is.null(fit$df)) df[nterm+1] <- nvar else df[nterm+1] <- sum(fit$df) tbl <- cbind(test, df, pchisq(test, df, lower.tail=FALSE)) dimnames(tbl) <- list(c(termname, "GLOBAL"), c("chisq", "df", "p")) } else { tbl <- cbind(test, df, pchisq(test, df, lower.tail=FALSE))[1:nterm,, drop=FALSE] dimnames(tbl) <- list(termname, c("chisq", "df", "p")) } # The x, y, residuals part is sorted by time within strata; this is # what the C routine zph1 and zph2 return indx <- if (ny==2) ord +1 else rev(ord) +1 # return to 1 based subscripts indx <- indx[event[indx]] # only keep the death times rval <- list(table=tbl, x=unname(ttimes[indx]), time=unname(y[indx, ny-1])) if (length(cget$strata)) rval$strata <- cget$strata[indx] @ The matrix of scaled Schoenfeld residuals is created one stratum at a time. The ideal for the residual $r(t_i)$, contributed by an event for subject $i$ at time $t_i$ is to use $r_iV^{-1}(t_i)$, the inverse of the variance matrix of $X$ at that time and for the relevant stratum. What is returned as \code{resid\$imat} is $\sum_i V(t_i)$. One option would have been to return all the individual $\hat V_i$ matrices, but that falls over when the number at risk is too small and it cannot be inverted. Option 2 would be to use a per stratum averge of the $V_i$, but that falls flat for models with a large number of strata, a nested case-control model for instance. We take a different average that may not be the best, but seems to be good enough and doesn't seem to fail. \begin{enumerate} \item The \code{resid\$used} matrix contains the number of deaths for each strata (row) that contributed to the sum for each variable (column). The value is either 0 or the number of events in the stratum, zero for those variables that are constant within the stratum. From this we can get the number of events that contributed to each element of the \code{imat} total. Dividing by this gives a per-element average \code{vmean}. \item For a given stratum, some of the covariates may have been unused. For any of those set the scaled Schoenfeld residual to NA, and use the other rows/columns of the \code{vmean} matrix to scale the rest. \end{enumerate} Now if some variable $x_1$ has a large variance at some time points and a small variance at others, or a large variance in one stratum and a small variance in another, the above smoothing won't catch that subtlety. However we expect such an issue to be rare. The common problem of strata*covariate interactions is the target of the above manipulations. <>= # Watch out for a particular edge case: there is a factor, and one of the # strata happens to not use one of its levels. The element of resid$used will # be zero, but it really should not. used <-resid$used for (i in asgn) { if (length(i) > 1 && any(used[,i] ==0)) used[,i] <- apply(used[,i,drop=FALSE], 1, max) } # Make the weight matrix wtmat <- matrix(0, nvar, nvar) for (i in 1:nrow(used)) wtmat <- wtmat + outer(used[i,], used[i,], pmin) # with strata*covariate interactions (multi-state models for instance) the # imatr matrix will be block diagonal. Don't divide these off diagonal zeros # by a wtmat value of zero. vmean <- imatr[1:nvar, 1:nvar, drop=FALSE]/ifelse(wtmat==0, 1, wtmat) sresid <- resid$schoen if (terms && any(sapply(asgn, length) > 1)) { # collase multi-column terms temp <- matrix(0, ncol(sresid), nterm) for (i in 1:nterm) { j <- asgn[[i]] if (length(j) ==1) temp[j, i] <- 1 else temp[j, i] <- fit$coefficients[j] } sresid <- sresid %*% temp vmean <- t(temp) %*% vmean %*% temp used <- used[, sapply(asgn, function(x) x[1]), drop=FALSE] } dimnames(sresid) <- list(signif(rval$time, 4), termname) # for each stratum, rescale the Schoenfeld residuals in that stratum sgrp <- rep(1:nrow(used), apply(used, 1, max)) for (i in 1:nrow(used)) { k <- which(used[i,] > 0) if (length(k) >0) { # there might be no deaths in the stratum j <- which(sgrp==i) if (length(k) ==1) sresid[j,k] <- sresid[j,k]/vmean[k,k] else sresid[j, k] <- t(solve(vmean[k, k], t(sresid[j, k, drop=FALSE]))) sresid[j, -k] <- NA } } # Add in beta-hat. For a term with multiple columns we are testing zph for # the linear predictor X\beta, which always has a coefficient of 1 for (i in 1:nterm) { j <- asgn[[i]] if (length(j) ==1) sresid[,i] <- sresid[,i] + fit$coefficients[j] else sresid[,i] <- sresid[,i] +1 } rval$y <- sresid rval$var <- solve(vmean) @ <>= "[.cox.zph" <- function(x, ..., drop=FALSE) { i <- ..1 if (!is.null(x$strata)) { y2 <- x$y[,i,drop=FALSE] ymiss <- apply(is.na(y2), 1, all) if (any(ymiss)) { # some deaths played no role in these coefficients # due to a strata * covariate interaction, drop unneeded rows z<- list(table=x$table[i,,drop=FALSE], x=x$x[!ymiss], time= x$time[!ymiss], strata = x$strata[!ymiss], y = y2[!ymiss,,drop=FALSE], var=x$var[i,i, drop=FALSE], transform=x$transform, call=x$call) } else z<- list(table=x$table[i,,drop=FALSE], x=x$x, time= x$time, strata = x$strata, y = y2, var=x$var[i,i, drop=FALSE], transform=x$transform, call=x$call) } else z<- list(table=x$table[i,,drop=FALSE], x=x$x, time= x$time, y = x$y[,i,drop=FALSE], var=x$var[i,i, drop=FALSE], transform=x$transform, call=x$call) class(z) <- class(x) z } @ \bibliographystyle{plain} \bibliography{refer} \end{document} survival/noweb/tail0000644000176200001440000000007613537676563014143 0ustar liggesusers\bibliographystyle{plain} \bibliography{refer} \end{document} survival/noweb/msurvnew0000644000176200001440000003452013722523107015056 0ustar liggesusers\subsubsection{C-code} (This is set up as a separate file in the source code directory since it is easier to make emacs stay in C-mode if the file has a .nw extension.) <>= #include "survS.h" #include "survproto.h" #include SEXP survfitci(SEXP ftime2, SEXP sort12, SEXP sort22, SEXP ntime2, SEXP status2, SEXP cstate2, SEXP wt2, SEXP id2, SEXP p2, SEXP i02, SEXP sefit2) { <> <> <> } @ Arguments to the routine are the following. For an R object ``zed'' I use the convention of [[zed2]] to refer to the object and [[zed]] to the contents of the object. \begin{description} \item[ftime] A two column matrix containing the entry and exit times for each subject. \item[sort1] Order vector for the entry times. The first element of sort1 points to the first entry time, etc. \item[sort2] Order vector for the event times. \item[ntime] Number of unique event time values. This fixes the size of the output arrays. \item[status] Status for each observation. 0= censored \item[cstate] The initial state for each subject, which will be updated during computation to always be the current state. \item[wt] Case weight for each observation. \item[id] The subject id for each observation. \item[p] The initial distribution of states. This will be updated during computation to be the current distribution. \item[i0] The initial influence matrix, number of subjects by number of states \item[sefit] If 1 then do the se compuatation, if 2 also return the full influence matrix upon which it is based, if 0 the se is not needed. \end{description} Note that code is called with id and not cluster: there is a basic premise that each id is a single subject and thus has a unique "current state" at any given time point. The history of this is that before the survcheck routine, we did not have a good way for a user to normalize the 'current state' variable for a subject, so this routine takes care of that tracking process. When multi-state Cox models were added we became more formal about this, and users can now have data sets with quite odd patterns of transitions and current state, ones that survcheck calls a teleport. At some point this routine should be updated as well. Cumulative hazard estimates make at least some sense when a subject has a hole, though P(state |t) curves do not. Declare all of the variables. <>= int i, j, k, kk; /* generic loop indices */ int ck, itime, eptr; /*specific indices */ double ctime; /*current time of interest, in the main loop */ int oldstate, newstate; /*when changing state */ double temp, *temp2; /* scratch double, and vector of length nstate */ double *dptr; /* reused in multiple contexts */ double *p; /* current prevalence vector */ double **hmat; /* hazard matrix at this time point */ double **umat=0; /* per subject leverage at this time point */ int *atrisk; /* 1 if the subject is currently at risk */ int *ns; /* number curently in each state */ int *nev; /* number of events at this time, by state */ double *ws; /* weighted count of number state */ double *wtp; /* case weights indexed by subject */ double wevent; /* weighted number of events at current time */ int nstate; /* number of states */ int n, nperson; /*number of obs, subjects*/ double **chaz; /* cumulative hazard matrix */ /* pointers to the R variables */ int *sort1, *sort2; /*sort index for entry time, event time */ double *entry,* etime; /*entry time, event time */ int ntime; /* number of unique event time values */ int *status; /*0=censored, 1,2,... new states */ int *cstate; /* current state for each subject */ int *dstate; /* the next state, =cstate if not an event time */ double *wt; /* weight for each observation */ double *i0; /* initial influence */ int *id; /* for each obs, which subject is it */ int sefit; /* returned objects */ SEXP rlist; /* the returned list and variable names of same */ const char *rnames[]= {"nrisk","nevent","ncensor", "p", "cumhaz", "std", "influence.pstate", ""}; SEXP setemp; double **pmat, **vmat=0, *cumhaz, *usave=0; /* =0 to silence -Wall warning */ int *ncensor, **nrisk, **nevent; @ Now set up pointers for all of the R objects sent to us. The two that will be updated need to be replaced by duplicates. <>= ntime= asInteger(ntime2); nperson = LENGTH(cstate2); /* number of unique subjects */ n = LENGTH(sort12); /* number of observations in the data */ PROTECT(cstate2 = duplicate(cstate2)); cstate = INTEGER(cstate2); entry= REAL(ftime2); etime= entry + n; sort1= INTEGER(sort12); sort2= INTEGER(sort22); status= INTEGER(status2); wt = REAL(wt2); id = INTEGER(id2); PROTECT(p2 = duplicate(p2)); /*copy of initial prevalence */ p = REAL(p2); nstate = LENGTH(p2); /* number of states */ i0 = REAL(i02); sefit = asInteger(sefit2); /* allocate space for the output objects ** Ones that are put into a list do not need to be protected */ PROTECT(rlist=mkNamed(VECSXP, rnames)); setemp = SET_VECTOR_ELT(rlist, 0, allocMatrix(INTSXP, ntime, nstate)); nrisk = imatrix(INTEGER(setemp), ntime, nstate); /* time by state */ setemp = SET_VECTOR_ELT(rlist, 1, allocMatrix(INTSXP, ntime, nstate)); nevent = imatrix(INTEGER(setemp), ntime, nstate); /* time by state */ setemp = SET_VECTOR_ELT(rlist, 2, allocVector(INTSXP, ntime)); ncensor = INTEGER(setemp); /* total at each time */ setemp = SET_VECTOR_ELT(rlist, 3, allocMatrix(REALSXP, ntime, nstate)); pmat = dmatrix(REAL(setemp), ntime, nstate); setemp = SET_VECTOR_ELT(rlist, 4, allocMatrix(REALSXP, nstate*nstate, ntime)); cumhaz = REAL(setemp); if (sefit >0) { setemp = SET_VECTOR_ELT(rlist, 5, allocMatrix(REALSXP, ntime, nstate)); vmat= dmatrix(REAL(setemp), ntime, nstate); } if (sefit >1) { /* the max space is larger for a matrix than a vector ** This is pure sneakiness: if I allocate a vector then n*nstate*(ntime+1) ** may overflow, as it is an integer argument. Using the rows and cols of ** a matrix neither overflows. But once allocated, I can treat setemp ** like a vector since usave is a pointer to double, which is bigger than ** integer and won't overflow. */ setemp = SET_VECTOR_ELT(rlist, 6, allocMatrix(REALSXP, n*nstate, ntime+1)); usave = REAL(setemp); } /* allocate space for scratch vectors */ ws = (double *) R_alloc(2*nstate, sizeof(double)); /*weighted number in state */ temp2 = ws + nstate; ns = (int *) R_alloc(2*nstate, sizeof(int)); nev = ns + nstate; atrisk = (int *) R_alloc(2*nperson, sizeof(int)); dstate = atrisk + nperson; wtp = (double *) R_alloc(nperson, sizeof(double)); hmat = (double**) dmatrix((double *)R_alloc(nstate*nstate, sizeof(double)), nstate, nstate); chaz = (double**) dmatrix((double *)R_alloc(nstate*nstate, sizeof(double)), nstate, nstate); if (sefit >0) umat = (double**) dmatrix((double *)R_alloc(nperson*nstate, sizeof(double)), nstate, nperson); /* R_alloc does not zero allocated memory */ for (i=0; i>= if (sefit ==1) { dptr = i0; for (j=0; j1) { /* copy influence, and save it */ dptr = i0; for (j=0; j>= itime =0; /*current time index, for output arrays */ eptr = 0; /*index to sort1, the entry times */ for (i=0; i> <> /* Take the current events and censors out of the risk set */ for (; i0) cstate[id[j]] = status[j]-1; /*new state */ atrisk[id[j]] =0; } else break; } itime++; } @ The key variables for the computation are the matrix $H$ and the current prevalence vector $P$. $H$ is created anew at each unique time point. Row $j$ of $H$ concerns everyone in state $j$ just before the time point, and contains the transitions at that time point. So the $jk$ element is the (weighted) fraction who change from state $j$ to state $k$, and the $jj$ element the fraction who stay put. Each row of $H$ by definition sums to 1. If no one is in the state then the $jj$ element is set to 1. A second version which we call H2 has 1 subtracted from each diagonal giving row sums are 0, we go back and forth depending on which is needed at the moment. If there are no events at this time point $P$ and $U$ do not update. <>= for (j=0; j0) { newstate = status[k] -1; /* 0 based subscripts */ oldstate = cstate[id[k]]; if (oldstate != newstate) { /* A "move" to the same state does not count */ dstate[id[k]] = newstate; nev[newstate]++; wevent += wt[k]; hmat[oldstate][newstate] += wt[k]; } } else ncensor[itime]++; } else break; } if (wevent > 0) { /* there was at least one move with weight > 0 */ /* finish computing H */ for (j=0; j0) { temp =0; for (k=0; k0) { <> } <> } @ The most complicated part of the code is the update of the per subject influence matrix $U$. The influence for a subject is the derivative of the current estimates wrt the case weight of that subject. Since $p$ is a vector the influence $U$ is easily represented as a matrix with one row per subject and one column per state. Refer to equation \eqref{ci} for the derivation. Let $m$ and $n$ be the old and new states for subject $i$, and $n_m$ the sum of weights for all subjects at risk in state $m$. Then \begin{equation*} U_{ij}(t) = \sum_k \left[ U_{ik}(t-)H_{kj}\right] + p_m(t-)(I_{n=j} - H_{mj})/ n_m \end{equation*} \begin{enumerate} \item The first term above is simple matrix multiplication. \item The second adds a vector with mean zero. \end{enumerate} If standard errors are not needed we can skip this calculation. <>= /* Update U, part 1 U = U %*% H -- matrix multiplication */ for (j=0; j>= /* Finally, update chaz and p. */ for (j=0; j>= /* store into the matrices that will be passed back */ for (j=0; j0) { temp =0; for (k=0; k 1) for (k=0; k>= /* return a list */ UNPROTECT(3); return(rlist); @ survival/noweb/survfitKM.Rnw0000644000176200001440000012025313745413560015674 0ustar liggesusers\subsection{Kaplan-Meier} This routine has been rewritten more times than any other in the package, as we trade off simplicty of the code with execution speed. This version does all of the oranizational work in S and calls a C routine for each separate curve. The first code did everything in C but was too hard to maintain and the most recent prior function did nearly everything in S. Introduction of robust variance prompted a movement of more of the code into C since that calculation is computationally intensive. <>= survfitKM <- function(x, y, weights=rep(1.0,length(x)), stype=1, ctype=1, se.fit=TRUE, conf.int= .95, conf.type=c('log', 'log-log', 'plain', 'none', 'logit', "arcsin"), conf.lower=c('usual', 'peto', 'modified'), start.time, id, cluster, robust, influence=FALSE, type) { if (!missing(type)) { if (!is.character(type)) stop("type argument must be character") # older style argument is allowed temp <- charmatch(type, c("kaplan-meier", "fleming-harrington", "fh2")) if (is.na(temp)) stop("invalid value for 'type'") type <- c(1,3,4)[temp] } else { if (!(ctype %in% 1:2)) stop("ctype must be 1 or 2") if (!(stype %in% 1:2)) stop("stype must be 1 or 2") type <- as.integer(2*stype + ctype -2) } conf.type <- match.arg(conf.type) conf.lower<- match.arg(conf.lower) if (is.logical(conf.int)) { # A common error is for users to use "conf.int = FALSE" # it's not correct, but allow it if (!conf.int) conf.type <- "none" conf.int <- .95 } if (!is.Surv(y)) stop("y must be a Surv object") if (attr(y, 'type') != 'right' && attr(y, 'type') != 'counting') stop("Can only handle right censored or counting data") ny <- ncol(y) # Will be 2 for right censored, 3 for counting # The calling routine has used 'strata' on x, so it is a factor with # no unused levels. But just in case a user called this... if (!is.factor(x)) stop("x must be a factor") xlev <- levels(x) # Will supply names for the curves x <- as.integer(x) # keep the integer index if (missing(start.time)) time0 <- min(0, y[,ny-1]) else time0 <- start.time # The user can call with cluster, id, robust, or any combination # Default for robust: if cluster or any id with > 1 event or # any weights that are not 0 or 1, then TRUE # If only id, treat it as the cluster too has.cluster <- !(missing(cluster) || length(cluster)==0) has.id <- !(missing(id) || length(id)==0) has.rwt<- (!missing(weights) && any(weights != floor(weights))) #has.rwt <- FALSE # we are rethinking this has.robust <- !missing(robust) && !is.null(robust) if (has.id) id <- as.factor(id) if (missing(robust) || is.null(robust)) { if (influence) { robust <- TRUE if (!(has.cluster || has.id)) { cluster <- seq(along=x) has.cluster <- TRUE } } else if (has.cluster || has.rwt || (has.id && anyDuplicated(id[y[,ncol(y)]==1]))) robust <- TRUE else robust <- FALSE } if (!is.logical(robust)) stop("robust must be TRUE/FALSE") if (has.cluster) { if (!robust) { warning("cluster specified with robust=FALSE, cluster ignored") ncluster <- 0 clname <- NULL } else { if (is.factor(cluster)) { clname <- levels(cluster) cluster <- as.integer(cluster) } else { clname <- sort(unique(cluster)) cluster <- match(cluster, clname) } ncluster <- length(clname) } } else if (robust) { if (has.id) { # treat the id as both identifier and clustering clname <- levels(id) cluster <- as.integer(id) ncluster <- length(clname) } else if (ncol(y)==2 || !has.robust) { # create our own clustering n <- nrow(y) cluster <- 1:n ncluster <- n clname <- 1:n } else stop("id or cluster option required") } else ncluster <- 0 if (is.logical(influence)) { # TRUE/FALSE is treated as all or nothing if (!influence) influence <- 0L else influence <- 3L } else if (!is.numeric(influence)) stop("influence argument must be numeric or logical") if (!(influence %in% 0:3)) stop("influence argument must be 0, 1, 2, or 3") else influence <- as.integer(influence) if (!robust && influence >0) { warning("robust=FALSE implies influence=FALSE") influence <- 0L } if (!se.fit) { # if the user asked for no standard error, skip any robust computation ncluster <- 0L influence <- 0L } # if start.time was set, delete obs if necessary keep <- y[,ny-1] >= time0 if (!all(keep)) { y <- y[keep,] if (length(id) >0) id <- id[keep] if (length(cluster) >0) cluster <- cluster[keep] x <- x[keep] weights <- weights[keep] } <> <> } @ At each event time we have \begin{itemize} \item n(t) = number at risk = sum of weigths for those at risk \item d(t) = number of events = sum of weights for the deaths \item e(t) = unweighted number of events \end{itemize} From this we can calculate the Kapan-Meier and Nelson-Aalen estimates. The Fleming-Harrington estimate is the analog of the Efron approximation in a Cox model. When there are no case weights the FH idea is quite simple. Assume that the real data is not tied, but we saw a coarsened version. If we see 3 events out of 10 subjects at risk the NA increment is 3/10 but the FH is 1/10 + 1/9 + 1/8, it is what we would have seen with the uncoarsened data. If there are case weights we give each of the 3 terms a 1/3 chance of being the first, second, or third event \begin{align*} KM(t) &= KM(t-) (1- d(t)/n(t) \\ NA(t) &= NA(t-) + d(t)/n(t) \\ FH(t) &= FH(t-) + \sum_{i=1}^{3} \frac{(d(t)/3}{n(t)- d(t)(i-1)/3} \end{align*} When one of these 3 subjects has an event but continues, which can happen with start/stop data, then this gets trickier: the second $d$ in the last equation above should include only the other 2. The idea is that each of those will certainly be present for the first event, has 2/3 chance of being present for the second, and 1/3 for the third. If we think of the size of the denominator as a random variable $Z$, an exact solution would use $E(1/Z)$, the FH uses $1/E(Z)$ and the NA uses $1/\max(Z)$ as the denominator for each of the 3 deaths. One problem with survival is near ties in Y: table, unique, ==, etc. can do different things in this case. Luckily, the parent survfit routine has dealt with that by using the \code{aeqSurv} function. The underlying C code allows the sort1/sort2 vectors to be a different length than y, weights, and cluster. When there is only one curve we use that to our advantage to avoid creating a new copy of the last 3, passing in the original data. When there are multiple curves I had an internal debate about efficiency. Is is better to make a subset of y for each curve = more memory, or keep the original y and address a different subset in each C call = worse memory cache performance? I don't know the answer. In either case the cluster vector needs to be re-done for each group. Say that curve 1 uses subjects 1-10 and curve 2 uses 11-n: we don't want the first curve to compute or keep the zero influence values for all the subjects who are not in it. Especially when returning the influence matrix, which can get too large for memory. If ny==3 and has.id is true, then do some extra setup work, which is to create a position vector of 1=first obs for the subject, 2 = last, 3=both, 0= other, for each set of back to back times. This is used to prevent counting a subject with data of (0,10], (10,15] in both the censored at 10 and entered at 10 totals. We assume the data has been vetted to prevent overlapping intervals, so that it suffices to sort by ending time. If a subject has holes in their timeline they will have more than one first and last indicator. <>= if (ny==3 & has.id) position <- survflag(y, id) else position <- integer(0) if (length(xlev) ==1) {# only one group if (ny==2) { sort1 <- NULL sort2 <- order(y[,1]) } else { sort2 <- order(y[,2]) sort1 <- order(y[,1]) } toss <- (y[sort2, ny-1] < time0) if (any(toss)) { # Some obs were removed by the start.time argument sort2 <- sort2[!toss] if (ny ==3) { index <- match(which(toss), sort1) sort1 <- sort1[-index] } } n.used <- length(sort2) if (ncluster > 0) cfit <- .Call(Csurvfitkm, y, weights, sort1-1L, sort2-1L, type, cluster-1L, ncluster, position, influence) else cfit <- .Call(Csurvfitkm, y, weights, sort1-1L, sort2-1L, type, 0L, 0L, position, influence) } else { # multiple groups ngroup <- length(xlev) cfit <- vector("list", ngroup) n.used <- integer(ngroup) if (influence) clusterid <- cfit # empty list of group id values for (i in 1:ngroup) { keep <- which(x==i & y[,ny-1] >= time0) if (length(keep) ==0) next; # rare case where all are < start.time ytemp <- y[keep,] n.used[i] <- nrow(ytemp) if (ny==2) { sort1 <- NULL sort2 <- order(ytemp[,1]) } else { sort2 <- order(ytemp[,2]) sort1 <- order(ytemp[,1]) } # Cluster is a nuisance: every curve might have a different set # We need to relabel them from 1 to "number of unique clusters in this # curve for the C routine if (ncluster > 0) { c2 <- cluster[keep] c.unique <- sort(unique(c2)) nc <- length(c.unique) c2 <- match(c2, c.unique) # renumber them if (influence >0) { clusterid[[i]] <-c.unique } } if (ncluster > 0) cfit[[i]] <- .Call(Csurvfitkm, ytemp, weights[keep], sort1 -1L, sort2 -1L, type, c2 -1L, length(c.unique), position, influence) else cfit[[i]] <- .Call(Csurvfitkm, ytemp, weights[keep], sort1 -1L, sort2 -1L, type, 0L, 0L, position, influence) } } @ <>= # create the survfit object if (length(n.used) == 1) { rval <- list(n= length(x), time= cfit$time, n.risk = cfit$n[,4], n.event= cfit$n[,5], n.censor=cfit$n[,6], surv = cfit$estimate[,1], std.err = cfit$std[,1], cumhaz = cfit$estimate[,2], std.chaz = cfit$std[,2]) } else { strata <- sapply(cfit, function(x) if (is.null(x$n)) 0L else nrow(x$n)) names(strata) <- xlev # we need to collapse the curves rval <- list(n= as.vector(table(x)), time = unlist(lapply(cfit, function(x) x$time)), n.risk= unlist(lapply(cfit, function(x) x$n[,4])), n.event= unlist(lapply(cfit, function(x) x$n[,5])), n.censor=unlist(lapply(cfit, function(x) x$n[,6])), surv = unlist(lapply(cfit, function(x) x$estimate[,1])), std.err =unlist(lapply(cfit, function(x) x$std[,1])), cumhaz =unlist(lapply(cfit, function(x) x$estimate[,2])), std.chaz=unlist(lapply(cfit, function(x) x$std[,2])), strata=strata) if (ny==3) rval$n.enter <- unlist(lapply(cfit, function(x) x$n[,8])) } if (ny ==3) { rval$n.enter <- cfit$n[,8] rval$type <- "counting" } else rval$type <- "right" if (se.fit) { rval$logse = (ncluster==0 || (type==2 || type==4)) # se(log S) or se(S) rval$conf.int = conf.int rval$conf.type= conf.type if (conf.lower != "usual") rval$conf.lower = conf.lower if (conf.lower == "modified") { nstrat = length(n.used) events <- rval$n.event >0 if (nstrat ==1) events[1] <- TRUE else events[1 + cumsum(c(0, rval$strata[-nstrat]))] <- TRUE zz <- 1:length(events) n.lag <- rep(rval$n.risk[events], diff(c(zz[events], 1+max(zz)))) # # n.lag = the # at risk the last time there was an event (or # the first time of a strata) # } std.low <- switch(conf.lower, 'usual' = rval$std.err, 'peto' = sqrt((1-rval$surv)/ rval$n.risk), 'modified' = rval$std.err * sqrt(n.lag/rval$n.risk)) if (conf.type != "none") { ci <- survfit_confint(rval$surv, rval$std.err, logse=rval$logse, conf.type, conf.int, std.low) rval <- c(rval, list(lower=ci$lower, upper=ci$upper)) } } else { # for consistency don't return the se if std.err=FALSE rval$std.err <- NULL rval$std.chaz <- NULL } # Add the influence, if requested by the user # remember, if type= 3 or 4, the survival influence has to be constructed. if (influence > 0) { if (type==1 | type==2) { if (influence==1 || influence ==3) { if (length(xlev)==1) { rval$influence.surv <- cfit$influence1 row.names(rval$influence.surv) <- clname } else { temp <- vector("list", ngroup) for (i in 1:ngroup) { temp[[i]] <- cfit[[i]]$influence1 row.names(temp[[i]]) <- clname[clusterid[[i]]] } rval$influence.surv <- temp } } if (influence==2 || influence==3) { if (length(xlev)==1) { rval$influence.chaz <- cfit$influence2 row.names(rval$influence.chaz) <- clname } else { temp <- vector("list", ngroup) for (i in 1:ngroup) { temp[[i]] <- cfit[[i]]$influence2 row.names(temp[[i]]) <- clname[clusterid[[i]]] } rval$influence.chaz <- temp } } } else { # everything is derived from the influence of the cumulative hazard if (length(xlev) ==1) { temp <- cfit$influence2 row.names(temp) <- clname } else { temp <- vector("list", ngroup) for (i in 1:ngroup) { temp[[i]] <- cfit[[i]]$influence2 row.names(temp[[i]]) <- clname[clusterid[[i]]] } } if (influence==2 || influence ==3) rval$influence.chaz <- temp if (influence==1 || influence==3) { # if an obs moves the cumulative hazard up, then it moves S down if (length(xlev) ==1) rval$influence.surv <- -temp * rep(rval$surv, each=nrow(temp)) else { for (i in 1:ngroup) temp[[i]] <- -temp[[i]] * rep(cfit[[i]]$estimate[,1], each=nrow(temp[[i]])) rval$influence.surv <- temp } } } } if (!missing(start.time)) rval$start.time <- start.time rval @ Now for the real work using C routines. My standard for a variable named ``zed'' is to use zed2 for the S object and zed for the data part of the object; the latter is what the C code works with. <>= #include #include "survS.h" #include "survproto.h" SEXP survfitkm(SEXP y2, SEXP weight2, SEXP sort12, SEXP sort22, SEXP type2, SEXP id2, SEXP nid2, SEXP position2, SEXP influence2) { int i, i1, i2, j, k, person1, person2; int nused, nid, type, influence; int ny, ntime; double *tstart=0, *stime, *status, *wt; double v1, v2, dtemp, haz; double temp, dtemp2, dtemp3, frac, btemp; int *sort1=0, *sort2, *id=0; static const char *outnames[]={"time", "n", "estimate", "std.err", "influence1", "influence2", ""}; SEXP rlist; double *gwt=0, *inf1=0, *inf2=0; /* =0 to silence -Wall */ int *gcount=0; int n1, n2, n3, n4; int *position=0, hasid; double wt1, wt2, wt3, wt4; /* output variables */ double *n[10], *dtime, *kvec, *nvec, *std[2], *imat1=0, *imat2=0; /* =0 to silence -Wall*/ double km, nelson; /* current estimates */ /* map the input data */ ny = ncols(y2); /* 2= ordinary survival 3= start,stop data */ nused = nrows(y2); if (ny==3) { tstart = REAL(y2); stime = tstart + nused; sort1 = INTEGER(sort12); } else stime = REAL(y2); status= stime +nused; wt = REAL(weight2); sort2 = INTEGER(sort22); nused = LENGTH(sort22); type = asInteger(type2); nid = asInteger(nid2); if (LENGTH(position2) > 0) { hasid =1; position = INTEGER(position2); } else hasid=0; influence = asInteger(influence2); /* nused was used for two things just above. The first was the length of the input data y, only needed for a moment to set up tstart, stime, and status. The second is the number of these observations we will actually use, which is the length of sort2. This routine can be called multiple times with sort1/sort2 pointing to different subsets of the data while y, wt, id and position can remain unchanged */ if (length(id2)==0) nid =0; /* no robust variance */ else id = INTEGER(id2); /* pass 1, get the number of unique times, needed for memory allocation Number of xval groups (unique id values) has been supplied Data is sorted by time */ ntime =1; temp = stime[sort2[0]]; for (i=1; i>= /* Allocate memory for the output n has 6 columns for number at risk, events, censor, then the 3 weighted versions of the same, then optionally two more for number added to the risk set (when ny=3) */ PROTECT(rlist = mkNamed(VECSXP, outnames)); dtime = REAL(SET_VECTOR_ELT(rlist, 0, allocVector(REALSXP, ntime))); if (ny==2) j=7; else j=9; n[0] = REAL(SET_VECTOR_ELT(rlist, 1, allocMatrix(REALSXP, ntime, j))); for (i=1; i0 ) { /* robust variance */ gcount = (int *) R_alloc(nid, sizeof(int)); if (type <3) { /* working vectors for the influence */ gwt = (double *) R_alloc(3*nid, sizeof(double)); inf1 = gwt + nid; inf2 = inf1 + nid; for (i=0; i< nid; i++) { gwt[i] =0.0; gcount[i] = 0; inf1[i] =0; inf2[i] =0; } } else { gwt = (double *) R_alloc(2*nid, sizeof(double)); inf2 = gwt + nid; for (i=0; i< nid; i++) { gwt[i] =0.0; gcount[i] = 0; inf2[i] =0; } } /* these are not accumulated, so do not need to be zeroed */ if (type <3) { if (influence==1 || influence ==3) imat1 = REAL(SET_VECTOR_ELT(rlist, 4, allocMatrix(REALSXP, nid, ntime))); if (influence==2 || influence==3) imat2 = REAL(SET_VECTOR_ELT(rlist, 5, allocMatrix(REALSXP, nid, ntime))); } else if (influence !=0) imat2 = REAL(SET_VECTOR_ELT(rlist, 5, allocMatrix(REALSXP, nid, ntime))); } <> <> UNPROTECT(1); return(rlist); } @ Pass 2 goes from the last time to the first and fills in the \code{n} matrix. <>= R_CheckUserInterrupt(); /*check for control-C */ /* ** person1, person2 track through sort1 and sort2, respectively ** likewise with i1 and i2 */ person1 = nused-1; person2 = nused-1; n1=0; wt1=0; for (k=ntime-1; k>=0; k--) { dtime[k] = stime[sort2[person2]]; /* current time point */ n2=0; n3=0; wt2=0; wt3=0; for (; person2 >=0; person2--) { i2= sort2[person2]; if (stime[i2] != dtime[k]) break; n1++; /* number at risk */ wt1 += wt[i2]; /* weighted number at risk */ if (status[i2] ==1) { n2++; /* events */ wt2 += wt[i2]; } else if (hasid==0 || (position[i2]& 2)) { /* if there are no repeated obs for a subject (hasid=0) ** or this is the last of a string (a,b](b,c](c,d].. for ** a subject (position[i2]=2 or 3), then it is a 'real' censor */ n3++; wt3 += wt[i2]; } } if (ny==3) { /* remove any with start time >=dtime*/ n4 =0; wt4 =0; for (; person1 >=0; person1--) { i1 = sort1[person1]; if (tstart[i1] < dtime[k]) break; n1--; wt1 -= wt[i1]; if (hasid==0 || (position[i1] & 1)) { /* if there are no repeated id (hasid=0) or this is the ** first of a string of (a,b](b,c](c,d] for a subject, then ** this is a 'real' entry */ n4++; wt4 += wt[i1]; } } if (n4>0) { n[6][k+1] = n4; n[7][k+1] = wt4; } } n[0][k] = n1; n[1][k]=n2; n[2][k]=n3; n[3][k] = wt1; n[4][k]=wt2; n[5][k]=wt3; } if (ny ==3) { /* fill in number entered for the initial interval */ n4=0; wt4=0; for (; person1>=0; person1--) { i1 = sort1[person1]; if (hasid==0 || (position[i1] & 1)) { n4++; wt4 += wt[i1]; } } n[6][0] = n4; n[7][0] = wt4; } @ The rest of the code is identical for simple survival or start-stop data. The cumulative hazard estimates are the Nelson-Aalen-Breslow (same estimate, three different papers) or the Fleming-Harrington. \begin{align*} \Lambda_A(t) &\ \sum{u_j \le t} d_j/r_j \\ \Lambda_{FH}(t) &= \sum{u_j \le t} \frac{d_j} {f_j \sum_{k=0}^{f_j-1} (r_j - kd_j/f_j)} \end{align*} To understand the Fleming-Harrington estimate, suppose that at some time point we had three deaths out of 10 at risk. The Aalen estimate gives a hazard estimate of 3/10. The FH estimate assumes that the deaths didn't actually all happen at once, even though rounding in the data collection process makes it appear that way, so the better estimate is 1/10 + 1/9 + 1/8. The third person to die, whoever that was, would have had only 8 at risk when their event happened. The estimate of survival is either the Kaplan-Meier or the exponential of the hazard. \begin{equation*} KM(t) = \prod_{u_j \le t} \frac{r_j - d_j}{r_j} \end{equation*} The third pass goes from smallest time to largest. 99 times out of 100 the user will choose type=1, so we try to avoid testing those expression n times. <>= R_CheckUserInterrupt(); /*check for control-C */ nelson =0.0; km=1.0; v1=0; v2=0; if (nid==0) { /* simple variance */ if (type==1 || type==3) { /* Nelson-Aalen hazard */ for (i=0; i0 && n[4][i]>0) { /* at least one event with wt>0*/ nelson += n[4][i]/n[3][i]; v2 += n[4][i]/(n[3][i]*n[3][i]); } nvec[i] = nelson; std[0][i] = sqrt(v2); std[1][i] = sqrt(v2); } } else { /* Fleming hazard */ for (i=0; i0 && n[4][i]>0) { /* at least one event */ km *= (n[3][i]-n[4][i])/n[3][i]; v1 += n[4][i]/(n[3][i] * (n[3][i] - n[4][i])); /* Greenwood */ } kvec[i] = km; std[0][i] = sqrt(v1); } } else { /* exp survival */ for (i=0; i< ntime; i++) { kvec[i] = exp(-nvec[i]); std[0][i] = std[1][i]; } } } else { /* infinitesimal jackknife variance */ <> } @ The robust variance is based on an infinitesimal jackknife (IJ). Let $S_{-i}(t)$ be the survival curve without subject $i$ and $J_i(t) = S_i(t) - S_{-i}(t)$ be the change in the survival curve from adding subject $i$ back in. Then the jackknife estimate of variance is $$ \sigma^2_J(t) = \sum \left( J_i(t) - \overline J(t) \right)^2 $$ The IJ estimate instead uses the linear approximation to the jackknife, since it is normally less work to compute the derivative than a whole new estimate. Notice that if all the weights were doubled the expression below will stay the same since the derivative will drop by 1/2. \begin{align*} \sigma^2_{IJ}(t_k) & = \sum_i w_i U_{ik}^2 \\ U_{ik} & = \frac{\partial S(t_k)}{\partial w_i} \end{align*} The big problem with the IJ estimate is that a first derivative matrix $U$ will have one row per subject and one column per event time. Since the number of unique event times tends to grow with $n$, this matrix very rapidly becomes too large to manage. Instead use a grouped jackknife with $g$ groups, $g$ will often be on the order of 20--50. The 0/1 design matrix $B$ has $n$ rows and $g$ columns, one column per group, marking which subject is in each group. The grouped jackknife can be written as \begin{align*} U'WBB'W U &= V'V \end{align*} Our goal is to accumulate and use $V$ instead of $U$. The working vectors \code{inf1} and \code{inf2} contain the current estimate for the survival S and cumulative hazard H, at a given time. They are saved into the \code{imat} array for users, if desired. First work this out for the cumulative hazard, which is simpler, and a single subject $k$. \begin{align} H(t) &= \sum_{s\le t} \frac{\sum w_i dN_i(s)}{\sum w_i Y_i(s)} \nonumber\\ &= \sum _{s\le t} h(s) \nonumber \\ U_k(t) &= U_k(t-) + \frac{\partial h(t)}{\partial w_k} \nonumber \\ &= U_k(t-) + \frac{1}{\sum w_i Y_i(t)} \left(dN_k(t) - Y_k(t)h(t) \right) \label{Una} \\ \sum_k w_k U_k(t) &= 0 \nonumber \end{align} using the counting process notation of $N(t)$ for events and $Y(t)$ for at risk. The weighted sum of the first derivatives is zero, so we don't need a mean when computing the variance estimate. (This is true for all IJ estimators.) $V$ involves the weighted sum of this over groups, for the increment to each row of $V$ the rightmost term of \eqref{Una} is replaced by the weighted sum over each group. Since $h$ is the same for every subject at risk, we only need accumulate the sum of subjects in and sum of events in each group. The first can be kept as a running sum with $O(n)$ effort. When using the FH2 estimate tied deaths are different. Say that subject $i$ dies at some time $t$ where there are 2 other tied deaths. Let $w_i$ for $i=1,2,3$ be the weight of those who die and $s$ the sum of weights for all the others. The contribution to the cumulative hazard and derivative at this time point is \begin{align*} h &= \frac{w_1+w_2+w_3}{3} \frac{1}{s+w_1+w_2+w_3} + \frac{w_1+w_2+w_3}{3} \frac{1}{s+ 2(w_1+w_2+w_3)/3} + \frac{w_1+w_2+w_3}{3} \frac{1}{s+ (w_1 + w_2 + w_3)/3} \\ &\equiv a(b_1 + b_2 + b_3) \frac{\partial h}{\partial w_i} &= &= \left\{ \begin{array}{cl} \frac{b_1 + b_2 + b_3}{3} - a b_1^2 - (2/3)a b_2^2 - (1/3)a b_3^2 & i\le 3 \\ -a(b_1^2 + b_2^2 + b_3^2) & i> 3 \end{array} \right . \end{align*} The idea is that if the data had been gathered with more precision, then there would not be ties. The first death has 1/3 chance of being subject 1,2, or 3 and all are in the denominator. The second also has 1/3 chance of being 1--3, and each of these has 2/3 chance of still being in the denominator, etc. The standard variance will be $ab_1^2 + ab_2^2 + ab_3^2$. For the Kaplan-Meier we have \begin{align} KM(t) &= KM(t-) [1 - h(t)] \nonumber\\ U_k(t) &= \frac{\partial KM(t)}{\partial w_k} \nonumber\\ &= U_k(t-) [1- h(t)] - KM(t-)\frac{\partial h(t)}{\partial w_k} \label{Ukm} \end{align} The $V$ matrix is again a weighted sum. The first term of \eqref{Ukm} does not change, it multiplies the current value times $1-h$. The second term involves the same summation as the cumulative hazard. When using $\exp(-H)$ as the survival estimate then \begin{align*} \frac{partial S(t)}{\partial w_k} &= \frac{\partial e^{-H(t)}}{\partial w_k}\\ &= e^{-H(t)} \partial{H(t)}{\partial w_k} \end{align*} so in this case only the robust variance for the cumulative hazard $H$ is needed, and the parent R routine can fill in the rest. The variance for a given survival time is $\sum V^2$, which is always returned. The code keeps the current $V$ vector for the hazard $H$ in \code{inf2}, and if necessary that for the KM in \code{inf1}. A last step is to add up the squares of all of these, so the algorithm is $O(gp)$ where $p$ is the number of unique event times and $g$ is the number of groups. The sum of weights for each group is kept in a vector \code{gwt}, which is updated as subjects enter and leave. Say that a study had n= 10 million subjects in g=100 groups with d = 1 million deaths. At each death we update the 'hazard' part of the influence for all 100 groups, which is O(gd). The deaths at that time point have a second increment, to whichever group each is in, but since time is sorted that adds O(n) for indexing and O(d) for the work. The most important thing is to avoid doing anything that would be O(ng) or O(nd). For method=3 the hazard part of the increment is also different for a death, the solution is to do an ordinary increment for everyone in the O(gd) step, then correct it when doing the O(d) update. <>= v1=0; v2 =0; km=1; nelson =0; person2=0; if (ny==3) { person1 =0; } else { /* at the start, everyone is at risk */ for (i=0; i< nused; i++) { i2 = id[i]; gcount[i2]++; gwt[i2] += wt[i]; } } if (type==1) { for (i=0; i< ntime; i++) { if (ny==3) { /* add in new subjects */ for (; person1 < nused; person1++) { /* add in those whose start time is < dtime */ i1 = sort1[person1]; if (tstart[i1] >= dtime[i]) break; gcount[id[i1]]++; gwt[id[i1]] += wt[i1]; } } if (n[1][i] > 0 && n[4][i]>0) { /* need to update the sums */ haz = n[4][i]/n[3][i]; for (k=0; k< nid; k++) { inf1[k] = inf1[k] *(1.0 -haz) + gwt[k]*km*haz/n[3][i]; inf2[k] -= gwt[k] * haz/n[3][i]; } for (; person2 dtime[i]) break; /* those at this time */ if (status[i2]==1) { inf1[id[i2]] -= km* wt[i2]/n[3][i]; inf2[id[i2]] += wt[i2]/n[3][i]; } gcount[id[i2]] --; if (gcount[id[i2]] ==0) gwt[id[i2]] = 0.0; else gwt[id[i2]] -= wt[i2]; } km *= (1-haz); nelson += haz; v1=0; v2=0; for (k=0; k dtime[i]) break; gcount[id[i2]] --; if (gcount[id[i2]] ==0) gwt[id[i2]] = 0.0; else gwt[id[i2]] -= wt[i2]; } } kvec[i] = km; nvec[i] = nelson; std[0][i] = sqrt(v1); std[1][i] = sqrt(v2); if (influence==1 || influence ==3) for (k=0; k= dtime[i]) break; gcount[id[i1]]++; gwt[id[i1]] += wt[i1]; } } if (n[1][i] > 0 && n[4][i] >0) { /* need to update the sums */ dtemp =0; /* the working denominator */ dtemp2=0; /* sum of squares */ dtemp3=0; temp = n[3][i] - n[4][i]; /* sum of weights for the non-deaths */ for (k=n[1][i]; k>0; k--) { frac = k/n[1][i]; btemp = 1/(temp + frac*n[4][i]); /* "b" in the math */ dtemp += btemp; dtemp2 += btemp*btemp*frac; dtemp3 += btemp*btemp; /* non-death deriv */ } dtemp /= n[1][i]; /* average denominator */ if (n[4][i] != n[1][i]) { /* case weights */ dtemp2 *= n[4][i]/ n[1][i]; dtemp3 *= n[4][i]/ n[1][i]; } nelson += n[4][i]*dtemp; haz = n[4][i]/n[3][i]; for (k=0; k< nid; k++) { inf1[k] = inf1[k] *(1.0 -haz) + gwt[k]*km*haz/n[3][i]; if (gcount[k]>0) inf2[k] -= gwt[k] * dtemp3; } for (; person2 dtime[i]) break; if (status[i2]==1) { inf1[id[i2]] -= km* wt[i2]/n[3][i]; inf2[id[i2]] += wt[i2] *(dtemp + dtemp3 - dtemp2); } gcount[id[i2]] --; if (gcount[id[i2]] ==0) gwt[id[i2]] = 0.0; else gwt[id[i2]] -= wt[i2]; } km *= (1-haz); v1=0; v2=0; for (k=0; k dtime[i]) break; gcount[id[i2]] --; if (gcount[id[i2]] ==0) gwt[id[i2]] = 0.0; else gwt[id[i2]] -= wt[i2]; } } kvec[i] = km; nvec[i] = nelson; std[0][i] = sqrt(v1); std[1][i] = sqrt(v2); if (influence==1 || influence ==3) for (k=0; k= dtime[i]) break; gcount[id[i1]]++; gwt[id[i1]] += wt[i1]; } } if (n[1][i] > 0 && n[4][i]>0) { /* need to update the sums */ haz = n[4][i]/n[3][i]; for (k=0; k< nid; k++) { inf2[k] -= gwt[k] * haz/n[3][i]; } for (; person2 dtime[i]) break; if (status[i2]==1) { inf2[id[i2]] += wt[i2]/n[3][i]; } gcount[id[i2]] --; if (gcount[id[i2]] ==0) gwt[id[i2]] = 0.0; else gwt[id[i2]] -= wt[i2]; } nelson += haz; v2=0; for (k=0; k dtime[i]) break; gcount[id[i2]] --; if (gcount[id[i2]] ==0) gwt[id[i2]] = 0.0; else gwt[id[i2]] -= wt[i2]; } } kvec[i] = exp(-nelson); nvec[i] = nelson; std[1][i] = sqrt(v2); std[0][i] = sqrt(v2); if (influence>0) for (k=0; k= dtime[i]) break; gcount[id[i1]]++; gwt[id[i1]] += wt[i1]; } } if (n[1][i] > 0 && n[4][i] >0) { /* need to update the sums */ dtemp =0; /* the working denominator */ dtemp2=0; /* sum of squares */ dtemp3=0; temp = n[3][i] - n[4][i]; /* sum of weights for the non-deaths */ for (k=n[1][i]; k>0; k--) { frac = k/n[1][i]; btemp = 1/(temp + frac*n[4][i]); /* "b" in the math */ dtemp += btemp; dtemp2 += btemp*btemp*frac; dtemp3 += btemp*btemp; /* non-death deriv */ } dtemp /= n[1][i]; /* average denominator */ if (n[4][i] != n[1][i]) { /* case weights */ dtemp2 *= n[4][i]/ n[1][i]; dtemp3 *= n[4][i]/ n[1][i]; } nelson += n[4][i]*dtemp; for (k=0; k< nid; k++) { if (gcount[k]>0) inf2[k] -= gwt[k] * dtemp3; } for (; person2 dtime[i]) break; if (status[i2]==1) { inf2[id[i2]] += wt[i2] *(dtemp + dtemp3 - dtemp2); } gcount[id[i2]] --; if (gcount[id[i2]] ==0) gwt[id[i2]] = 0.0; else gwt[id[i2]] -= wt[i2]; } v2=0; for (k=0; k dtime[i]) break; gcount[id[i2]] --; if (gcount[id[i2]] ==0) gwt[id[i2]] = 0.0; else gwt[id[i2]] -= wt[i2]; } } kvec[i] = exp(-nelson); nvec[i] = nelson; std[1][i] = sqrt(v2); std[0][i] = sqrt(v2); if (influence>0) for (k=0; k>= survexp <- function(formula, data, weights, subset, na.action, rmap, times, method=c("ederer", "hakulinen", "conditional", "individual.h", "individual.s"), cohort=TRUE, conditional=FALSE, ratetable=survival::survexp.us, scale=1, se.fit, model=FALSE, x=FALSE, y=FALSE) { <> <> <> <> } @ The first few lines are standard. Keep a copy of the call, then manufacture a call to [[model.frame]] that contains only the arguments relevant to that function. <>= Call <- match.call() # keep the first element (the call), and the following selected arguments indx <- match(c('formula', 'data', 'weights', 'subset', 'na.action'), names(Call), nomatch=0) if (indx[1] ==0) stop("A formula argument is required") tform <- Call[c(1,indx)] # only keep the arguments we wanted tform[[1L]] <- quote(stats::model.frame) # change the function called Terms <- if(missing(data)) terms(formula, 'ratetable') else terms(formula, 'ratetable',data=data) @ The function works with two data sets, the user's data on an actual set of %' subjects and the reference ratetable. This leads to a particular nuisance, that the variable names in the data set may not match those in the ratetable. For instance the United States overall death rate table [[survexp.us]] expects 3 variables, as shown by [[summary(survexp.us)]] \begin{itemize} \item age = age in days for each subject at the start of follow-up \item sex = sex of the subject, ``male'' or ``female'' (the routine accepts any unique abbreviation and is case insensitive) \item year = date of the start of follow-up \end{itemize} Up until the most recent revision, the formula contained any necessary mapping between the variables in the data set and the ratetable. For instance \begin{verbatim} survexp( ~ sex + ratetable(age=age*365.25, sex=sex, year=entry.dt), data=mydata, ratetable=survexp.us) \end{verbatim} In this case the user's data set has a variable `age' containing age in years, along with sex and an entry date. This had to be changed for two reasons. The primary one is that the data in a [[ratetable]] call had to be converted into a matrix in order to ``pass through'' the model.frame logic. With the recent updates to coxph so that it remembers factor codings correctly in new data sets, it is advantageous to keep factors as factors. The second is that a coxph model with a large number of covariates induces a very long ratetable clause; at about 40 variable it caused one of the R internal routines to fail due to a long expression. A third reason, perhaps the most pressing in reality, is that I've always %' felt that the prior code was confusing since it used the same term 'ratetable' for two different tasks. The new process adds the [[rmap]] argument, an example would be [[rmap=list(age =age*365.25, year=entry.dt)]]. Any variables in the ratetable that are not found in [[rmap]] are assumed to not need a mapping, this would be [[sex]] in the above example. For backwards compatability we allow the old style argument, converting it into the new style. The [[rmap]] argument needs to be examined without evaluating it; we then add the appropriate extra variables into a temporary formula so that the model frame has all that is required. The ratetable variables then can be retrieved from the model frame. The [[pyears]] routine uses the same rmap argument; this segment of the code is given its own name so that it can be included there as well. <>= rate <- attr(Terms, "specials")$ratetable if(length(rate) > 1) stop("Can have only 1 ratetable() call in a formula") <> mf <- eval(tform, parent.frame()) @ <>= if(length(rate) == 1) { if (!missing(rmap)) stop("The ratetable() call in a formula is depreciated") stemp <- untangle.specials(Terms, 'ratetable') rcall <- as.call(parse(text=stemp$var)[[1]]) # as a call object rcall[[1]] <- as.name('list') # make it a call to list(.. Terms <- Terms[-stemp$terms] # remove from the formula } else if (!missing(rmap)) { rcall <- substitute(rmap) if (!is.call(rcall) || rcall[[1]] != as.name('list')) stop ("Invalid rcall argument") } else rcall <- NULL # A ratetable, but no rcall argument # Check that there are no illegal names in rcall, then expand it # to include all the names in the ratetable if (is.ratetable(ratetable)) { varlist <- names(dimnames(ratetable)) if (is.null(varlist)) varlist <- attr(ratetable, "dimid") # older style } else if(inherits(ratetable, "coxph") && !inherits(ratetable, "coxphms")) { ## Remove "log" and such things, to get just the list of # variable names varlist <- all.vars(delete.response(ratetable$terms)) } else stop("Invalid rate table") temp <- match(names(rcall)[-1], varlist) # 2,3,... are the argument names if (any(is.na(temp))) stop("Variable not found in the ratetable:", (names(rcall))[is.na(temp)]) if (any(!(varlist %in% names(rcall)))) { to.add <- varlist[!(varlist %in% names(rcall))] temp1 <- paste(text=paste(to.add, to.add, sep='='), collapse=',') if (is.null(rcall)) rcall <- parse(text=paste("list(", temp1, ")"))[[1]] else { temp2 <- deparse(rcall) rcall <- parse(text=paste("c(", temp2, ",list(", temp1, "))"))[[1]] } } @ The formula below is used only in the call to [[model.frame]] to ensure that the frame has both the formula and the ratetable variables. We don't want to modify the original formula, since we use it to create the $X$ matrix and the response variable. The non-obvious bit of code is the addition of an environment to the formula. The [[model.matrix]] routine has a non-standard evaluation - it uses the frame of the formula, rather than the parent.frame() argument below, along with the [[data]] to look up variables. If a formula is long enough deparse() will give two lines, hence the extra paste call to re-collapse it into one. <>= # Create a temporary formula, used only in the call to model.frame newvar <- all.vars(rcall) if (length(newvar) > 0) { temp <- paste(paste(deparse(Terms), collapse=""), paste(newvar, collapse='+'), sep='+') tform$formula <- as.formula(temp, environment(Terms)) } @ If the user data has 0 rows, e.g. from a mistaken [[subset]] statement that eliminated all subjects, we need to stop early. Otherwise the .C code fails in a nasty way. <>= n <- nrow(mf) if (n==0) stop("Data set has 0 rows") if (!missing(se.fit) && se.fit) warning("se.fit value ignored") weights <- model.extract(mf, 'weights') if (length(weights) ==0) weights <- rep(1.0, n) if (class(ratetable)=='ratetable' && any(weights !=1)) warning("weights ignored") if (any(attr(Terms, 'order') >1)) stop("Survexp cannot have interaction terms") if (!missing(times)) { if (any(times<0)) stop("Invalid time point requested") if (length(times) >1 ) if (any(diff(times)<0)) stop("Times must be in increasing order") } @ If a response variable was given, we only need the times and not the status. To be correct, computations need to be done for each of the times given in the [[times]] argument as well as for each of the unique y values. This ends up as the vector [[newtime]]. If a [[times]] argument was given we will subset down to only those values at the end. For a population rate table and the Ederer method the times argument is required. <>= Y <- model.extract(mf, 'response') no.Y <- is.null(Y) if (no.Y) { if (missing(times)) { if (is.ratetable(ratetable)) stop("either a times argument or a response is needed") } else newtime <- times } else { if (is.matrix(Y)) { if (is.Surv(Y) && attr(Y, 'type')=='right') Y <- Y[,1] else stop("Illegal response value") } if (any(Y<0)) stop ("Negative follow up time") # if (missing(npoints)) temp <- unique(Y) # else temp <- seq(min(Y), max(Y), length=npoints) temp <- unique(Y) if (missing(times)) newtime <- sort(temp) else newtime <- sort(unique(c(times, temp[temp>= ovars <- attr(Terms, 'term.labels') # rdata contains the variables matching the ratetable rdata <- data.frame(eval(rcall, mf), stringsAsFactors=TRUE) if (is.ratetable(ratetable)) { israte <- TRUE if (no.Y) { Y <- rep(max(times), n) } rtemp <- match.ratetable(rdata, ratetable) R <- rtemp$R } else if (inherits(ratetable, 'coxph')) { israte <- FALSE Terms <- ratetable$terms # if (!is.null(attr(Terms, 'offset'))) # stop("Cannot deal with models that contain an offset") # strats <- attr(Terms, "specials")$strata # if (length(strats)) # stop("survexp cannot handle stratified Cox models") # if (any(names(mf[,rate]) != attr(ratetable$terms, 'term.labels'))) stop("Unable to match new data to old formula") } else if (inherits(ratetable, "coxphms")) stop("survexp not defined for multi-state coxph models") else stop("Invalid ratetable") @ Now for some calculation. If cohort is false, then any covariates on the right hand side (other than the rate table) are irrelevant, the function returns a vector of expected values rather than survival curves. <>= if (substring(method, 1, 10) == "individual") { #individual survival if (no.Y) stop("for individual survival an observation time must be given") if (israte) temp <- survexp.fit (1:n, R, Y, max(Y), TRUE, ratetable) else { rmatch <- match(names(data), names(rdata)) if (any(is.na(rmatch))) rdata <- cbind(rdata, data[,is.na(rmatch)]) temp <- survexp.cfit(1:n, rdata, Y, 'individual', ratetable) } if (method == "individual.s") xx <- temp$surv else xx <- -log(temp$surv) names(xx) <- row.names(mf) na.action <- attr(mf, "na.action") if (length(na.action)) return(naresid(na.action, xx)) else return(xx) } @ Now for the more commonly used case: returning a survival curve. First see if there are any grouping variables. The results of the [[tcut]] function are often used in person-years analysis, which is somewhat related to expected survival. However tcut results aren't relevant here and we put in a check for the %' confused user. The strata command creates a single factor incorporating all the variables. <>= if (length(ovars)==0) X <- rep(1,n) #no categories else { odim <- length(ovars) for (i in 1:odim) { temp <- mf[[ovars[i]]] ctemp <- class(temp) if (!is.null(ctemp) && ctemp=='tcut') stop("Can't use tcut variables in expected survival") } X <- strata(mf[ovars]) } #do the work if (israte) temp <- survexp.fit(as.numeric(X), R, Y, newtime, method=="conditional", ratetable) else { temp <- survexp.cfit(as.numeric(X), rdata, Y, method, ratetable, weights) newtime <- temp$time } @ Now we need to package up the curves properly All the results can be returned as a single matrix of survivals with a common vector of times. If there was a times argument we need to subset to selected rows of the computation. <>= if (missing(times)) { n.risk <- temp$n surv <- temp$surv } else { if (israte) keep <- match(times, newtime) else { # The result is from a Cox model, and it's list of # times won't match the list requested in the user's call # Interpolate the step function, giving survival of 1 # for requested points that precede the Cox fit's # first downward step. The code is like summary.survfit. n <- length(temp$time) keep <- approx(temp$time, 1:n, xout=times, yleft=0, method='constant', f=0, rule=2)$y } if (is.matrix(temp$surv)) { surv <- (rbind(1,temp$surv))[keep+1,,drop=FALSE] n.risk <- temp$n[pmax(1,keep),,drop=FALSE] } else { surv <- (c(1,temp$surv))[keep+1] n.risk <- temp$n[pmax(1,keep)] } newtime <- times } newtime <- newtime/scale if (is.matrix(surv)) { dimnames(surv) <- list(NULL, levels(X)) out <- list(call=Call, surv= drop(surv), n.risk=drop(n.risk), time=newtime) } else { out <- list(call=Call, surv=c(surv), n.risk=c(n.risk), time=newtime) } @ Last do the standard things: add the model, x, or y components to the output if the user asked for them. (For this particular routine I can't think of %' a reason they every would.) Copy across summary information from the rate table computation if present, and add the method and class to the output. <>= if (model) out$model <- mf else { if (x) out$x <- X if (y) out$y <- Y } if (israte && !is.null(rtemp$summ)) out$summ <- rtemp$summ if (no.Y) out$method <- 'Ederer' else if (conditional) out$method <- 'conditional' else out$method <- 'cohort' class(out) <- c('survexp', 'survfit') out @ survival/noweb/coxsurv3.Rnw0000644000176200001440000003164414041423076015534 0ustar liggesusers\subsubsection{Multi-state models} Survival curves after a multi-state Cox model are more challenging, particularly the variance. <>= survfit.coxphms <- function(formula, newdata, se.fit=TRUE, conf.int=.95, individual=FALSE, stype=2, ctype, conf.type=c("log", "log-log", "plain", "none", "logit", "arcsin"), censor=TRUE, start.time, id, influence=FALSE, na.action=na.pass, type, p0=NULL, ...) { Call <- match.call() Call[[1]] <- as.name("survfit") #nicer output for the user object <- formula #'formula' because it has to match survfit se.fit <- FALSE #still to do if (missing(newdata)) stop("multi-state survival requires a newdata argument") if (!missing(id)) stop("using a covariate path is not supported for multi-state") temp <- object$stratum_map["(Baseline)",] baselinecoef <- rbind(temp, coef= 1.0) if (any(duplicated(temp))) { # We have shared hazards # Find rows of cmap with "ph(a:b)" type labels to find out which # ones have proportionality rname <- rownames(object$cmap) phbase <- grepl("ph(", rname, fixed=TRUE) for (i in which(phbase)) { ctemp <- object$cmap[i,] index <- which(ctemp >0) baselinecoef[2, index] <- exp(object$coef[ctemp[index]]) } } else phbase <- rep(FALSE, nrow(object$cmap)) # process options, set up Y and the model frame, deal with start.time <> <> istate <- model.extract(mf, "istate") if (!missing(start.time)) { if (!is.numeric(start.time) || length(start.time) !=1 || !is.finite(start.time)) stop("start.time must be a single numeric value") toss <- which(Y[,ncol(Y)-1] <= start.time) if (length(toss)) { n <- nrow(Y) if (length(toss)==n) stop("start.time has removed all observations") Y <- Y[-toss,,drop=FALSE] X <- X[-toss,,drop=FALSE] weights <- weights[-toss] oldid <- oldid[-toss] istate <- istate[-toss] } } # expansion of the X matrix with stacker, set up shared hazards <> # risk scores, mf2, and x2 <> <> <> <> cifit$call <- Call class(cifit) <- c("survfitms", "survfit") cifit } @ The third line \code{as.name('survfit')} causes the printout to say `survfit' instead of `survfit.coxph'. %' Notice that setup is almost completely shared with survival for single state models. The major change is that we use survfitCI (non-Cox) to do all the legwork wrt the tabulation values (number at risk, etc.), while for the computation proper it is easier to make use of the same expanded data set that coxph used for a multi-state fit. <>= # Rebuild istate using the survcheck routine mcheck <- survcheck2(Y, oldid, istate) transitions <- mcheck$transitions if (is.null(istate)) istate <- mcheck$istate if (!identical(object$states, mcheck$states)) stop("failed to rebuild the data set") # Let the survfitCI routine do the work of creating the # overall counts (n.risk, etc). The rest of this code then # replaces the surv and hazard components. if (missing(start.time)) start.time <- min(Y[,2], 0) # If the data has absorbing states (ones with no transitions out), then # remove those rows first since they won't be in the final output. t2 <- transitions[, is.na(match(colnames(transitions), "(censored)")), drop=FALSE] absorb <- row.names(t2)[rowSums(t2)==0] if (is.null(weights)) weights <- rep(1.0, nrow(Y)) if (is.null(strata)) tempstrat <- rep(1L, nrow(Y)) else tempstrat <- strata if (length(absorb)) droprow <- istate %in% absorb else droprow <- FALSE # Let survfitCI fill in the n, number at risk, number of events, etc. portions # We will replace the pstate and cumhaz estimate with correct ones. if (any(droprow)) { j <- which(!droprow) cifit <- survfitCI(as.factor(tempstrat[j]), Y[j,], weights[j], id =oldid[j], istate= istate[j], se.fit=FALSE, start.time=start.time, p0=p0) } else cifit <- survfitCI(as.factor(tempstrat), Y, weights, id= oldid, istate = istate, se.fit=FALSE, start.time=start.time, p0=p0) # For computing the actual estimates it is easier to work with an # expanded data set. # Replicate actions found in the coxph-multi-X chunk, cluster <- model.extract(mf, "cluster") xstack <- stacker(object$cmap, object$stratum_map, as.integer(istate), X, Y, as.integer(strata), states= object$states) if (length(position) >0) position <- position[xstack$rindex] # id was required by coxph X <- xstack$X Y <- xstack$Y strata <- strata[xstack$rindex] # strat in the model, other than transitions transition <- xstack$transition istrat <- xstack$strata if (length(offset)) offset <- offset[xstack$rindex] if (length(weights)) weights <- weights[xstack$rindex] if (length(cluster)) cluster <- cluster[xstack$rindex] oldid <- oldid[xstack$rindex] if (robust & length(cluster)==0) cluster <- oldid @ The survfit.coxph-setup3 chunk, shared with single state Cox models, has created an mf2 model frame and an x2 matrix. For multi-state, we ignore any strata variables in mf2. Create a matrix of risk scores, number of subjects by number of transitions. Different transitions often have different coefficients, so there is a risk score vector per transition. <>= if (has.strata && !is.null(mf2[[stangle$vars]])){ mf2 <- mf2[is.na(match(names(mf2), stangle$vars))] mf2 <- unique(mf2) x2 <- unique(x2) } temp <- coef(object, matrix=TRUE)[!phbase,,drop=FALSE] # ignore missing coefs risk2 <- exp(x2 %*% ifelse(is.na(temp), 0, temp) - xcenter) @ At this point we have several parts to keep straight. The data set has been expanded into a new X and Y. \begin{itemize} \item \code{strata} contains any strata that were specified by the user in the original fit. We do completely separate computations for each stratum: the time scale starts over, nrisk, etc. Each has a separate call to the multihaz function. \item \code{transtion} contains the transition to which each observation applies \item \code{istrat} comes from the xstack routine, and marks each strata * basline hazard combination. \item \code{baselinecoef} maps from baseline hazards to transitions. It has one column per transition, which hazard it points to, and a multiplier. Most multipliers will be 1. \item \code{hfill} is constructed below. It contains the row/column to which each column of baselinecoef is mapped, within the H matrix used to compute P(state). \end{itemize} The coxph routine fits all strata and transitions at once, since the loglik is a sum over strata. This routine does each stratum separately. <>= # make the expansion map. # The H matrices we will need are nstate by nstate, at each time, with # elements that are non-zero only for observed transtions. states <- object$states nstate <- length(states) notcens <- (colnames(object$transitions) != "(censored)") trmat <- object$transitions[, notcens, drop=FALSE] from <- row(trmat)[trmat>0] from <- match(rownames(trmat), states)[from] # actual row of H to <- col(trmat)[trmat>0] to <- match(colnames(trmat), states)[to] # actual col of H hfill <- cbind(from, to) if (individual) { stop("time dependent survival curves are not supported for multistate") } ny <- ncol(Y) if (is.null(strata)) { fit <- multihaz(Y, X, position, weights, risk, istrat, ctype, stype, baselinecoef, hfill, x2, risk2, varmat, nstate, se.fit, cifit$p0, cifit$time) cifit$pstate <- fit$pstate cifit$cumhaz <- fit$cumhaz } else { if (is.factor(strata)) ustrata <- levels(strata) else ustrata <- sort(unique(strata)) nstrata <- length(cifit$strata) itemp <- rep(1:nstrata, cifit$strata) timelist <- split(cifit$time, itemp) ustrata <- names(cifit$strata) tfit <- vector("list", nstrata) for (i in 1:nstrata) { indx <- which(strata== ustrata[i]) # divides the data tfit[[i]] <- multihaz(Y[indx,,drop=F], X[indx,,drop=F], position[indx], weights[indx], risk[indx], istrat[indx], ctype, stype, baselinecoef, hfill, x2, risk2, varmat, nstate, se.fit, cifit$p0[i,], timelist[[i]]) } # do.call(rbind) doesn't work for arrays, it loses a dimension ntime <- length(cifit$time) cifit$pstate <- array(0., dim=c(ntime, dim(tfit[[1]]$pstate)[2:3])) cifit$cumhaz <- array(0., dim=c(ntime, dim(tfit[[1]]$cumhaz)[2:3])) rtemp <- split(seq(along=cifit$time), itemp) for (i in 1:nstrata) { cifit$pstate[rtemp[[i]],,] <- tfit[[i]]$pstate cifit$cumhaz[rtemp[[i]],,] <- tfit[[i]]$cumhaz } } cifit$newdata <- mf2 @ Finally, a routine that does all the actual work. \begin{itemize} \item The first 5 variables are for the data set that the Cox model was built on: y, x, position, risk score, istrat. Position is a flag for each obs. Is it the first of a connected string such as (10, 12) (12,19) (19,21), the last of such a string, both, or neither. 1*first + 2*last. This affects whether an obs is labeled as censored or not, nothing else. \item x2 and risk2 are the covariates and risk scores for the predicted values. These do not involve any ph(a:b) coefficients. \item baselinecoef and hfill control mapping from fittes hazards to transitions and probabilities \item p0 will be NULL if the user did not specifiy it. \item vmat is only needed for standard errors \item utime is the set of time points desired \end{itemize} <>= # Compute the hazard and survival functions multihaz <- function(y, x, position, weight, risk, istrat, ctype, stype, bcoef, hfill, x2, risk2, vmat, nstate, se.fit, p0, utime) { if (ncol(y) ==2) { sort1 <- seq.int(0, nrow(y)-1L) # sort order for a constant y <- cbind(-1.0, y) # add a start.time column, -1 in case # there is an event at time 0 } else sort1 <- order(istrat, y[,1]) -1L sort2 <- order(istrat, y[,2]) -1L ntime <- length(utime) # this returns all of the counts we might desire. storage.mode(weight) <- "double" #failsafe # for Surv(time, status), position is 2 (last) for all obs if (length(position)==0) position <- rep(2L, nrow(y)) fit <- .Call(Ccoxsurv2, utime, y, weight, sort1, sort2, position, istrat, x, risk) cn <- fit$count # 1-3 = at risk, 4-6 = events, 7-8 = censored events # 9-10 = censored, 11-12 = Efron, 13-15 = entry if (ctype ==1) { denom1 <- ifelse(cn[,4]==0, 1, cn[,3]) denom2 <- ifelse(cn[,4]==0, 1, cn[,3]^2) } else { denom1 <- ifelse(cn[,4]==0, 1, cn[,11]) denom2 <- ifelse(cn[,4]==0, 1, cn[,12]) } temp <- matrix(cn[,5] / denom1, ncol = fit$ntrans) hazard <- temp[,bcoef[1,]] * rep(bcoef[2,], each=nrow(temp)) if (se.fit) { temp <- matrix(cn[,5] / denom2, ncol = fit$ntrans) varhaz <- temp[,bcoef[1,]] * rep(bcoef[2,]^2, each=nrow(temp)) } # Expand the result, one "hazard set" for each row of x2 nx2 <- nrow(x2) h2 <- array(0, dim=c(nrow(hazard), nx2, ncol(hazard))) if (se.fit) v2 <- h2 S <- double(nstate) # survival at the current time S2 <- array(0, dim=c(nrow(hazard), nx2, nstate)) H <- matrix(0, nstate, nstate) if (stype==2) { H[hfill] <- colMeans(hazard) diag(H) <- diag(H) -rowSums(H) esetup <- survexpmsetup(H) } for (i in 1:nx2) { h2[,i,] <- apply(hazard %*% diag(risk2[i,]), 2, cumsum) if (se.fit) { d1 <- fit$xbar - rep(x[i,], each=nrow(fit$xbar)) d2 <- apply(d1*hazard, 2, cumsum) d3 <- rowSums((d2%*% vmat) * d2) # v2[jj,] <- (apply(varhaz[jj,],2, cumsum) + d3) * (risk2[i])^2 } S <- p0 for (j in 1:ntime) { H[,] <- 0.0 H[hfill] <- hazard[j,] *risk2[i,] if (stype==1) { diag(H) <- pmax(0, 1.0 - rowSums(H)) S <- as.vector(S %*% H) # don't keep any names } else { diag(H) <- 0.0 - rowSums(H) #S <- as.vector(S %*% expm(H)) # dgeMatrix issue S <- as.vector(S %*% survexpm(H, 1, esetup)) } S2[j,i,] <- S } } rval <- list(time=utime, xgrp=rep(1:nx2, each=nrow(hazard)), pstate=S2, cumhaz=h2) if (se.fit) rval$varhaz <- v2 rval } @ survival/noweb/statefig.Rnw0000644000176200001440000003112313570767322015551 0ustar liggesusers\section{State space figures} The statefig function was written to do ``good enough'' state space figures quickly and easily. There are certainly figures it can't draw and many figures that can be drawn better, but it accomplishes its purpose. The key argument \code{layout}, the first, is a vector of numbers. The value (1,3,4,2) for instance has a single state, then a column with 3 states, then a column with 4, then a column with 2. If \code{layout} is instead a 1 column matrix then do the same from top down. If it is a 2 column matrix then they provided their own spacing. <>= statefig <- function(layout, connect, margin=.03, box=TRUE, cex=1, col=1, lwd=1, lty=1, bcol= col, acol=col, alwd = lwd, alty= lty, offset=0) { # set up an empty canvas frame(); # new environment par(usr=c(0,1,0,1)) if (!is.numeric(layout)) stop("layout must be a numeric vector or matrix") if (!is.matrix(connect) || nrow(connect) != ncol(connect)) stop("connect must be a square matrix") nstate <- nrow(connect) dd <- dimnames(connect) if (!is.null(dd[[1]])) statenames <- dd[[1]] else if (is.null(dd[[2]])) stop("connect must have the state names as dimnames") else statenames <- dd[[2]] # expand out all of the graphical parameters. This lets users # use a vector of colors, line types, etc narrow <- sum(connect!=0) acol <- rep(acol, length=narrow) alwd <- rep(alwd, length=narrow) alty <- rep(alty, length=narrow) bcol <- rep(bcol, length=nstate) lty <- rep(lty, length=nstate) lwd <- rep(lwd, length=nstate) col <- rep(col, length=nstate) # text colors <> <> <> dimnames(cbox) <- list(statenames, c("x", "y")) invisible(cbox) } <> @ The drawing region is always (0,1) by (0,1). A user can enter their own matrix of coordinates. Otherwise the free space is divided with one portion on each end and 2 portions between boxes. If there were 3 columns for instance they will have x coordinates of 1/6, 1/6 + 1/3, 1/6 + 2/3. Ditto for dividing up the y coordinate. The primary nuisance is that we want to count down from the top instead of up from the bottom. A 1 by 1 matrix is treated as a column matrix. <>= if (is.matrix(layout) && ncol(layout)==2 && nrow(layout) > 1) { # the user provided their own if (any(layout <0) || any(layout >1)) stop("layout coordinates must be between 0 and 1") if (nrow(layout) != nstate) stop("layout matrix should have one row per state") cbox <- layout } else { if (any(layout <=0 | layout != floor(layout))) stop("non-integer number of states in layout argument") space <- function(n) (1:n -.5)/n # centers of the boxes if (sum(layout) != nstate) stop("number of boxes != number of states") cbox <- matrix(0, ncol=2, nrow=nstate) #coordinates will be here n <- length(layout) ix <- rep(seq(along=layout), layout) if (is.vector(layout) || ncol(layout)> 1) { #left to right cbox[,1] <- space(n)[ix] for (i in 1:n) cbox[ix==i,2] <- 1 -space(layout[i]) } else { # top to bottom cbox[,2] <- 1- space(n)[ix] for (i in 1:n) cbox[ix==i,1] <- space(layout[i]) } } @ Write the text out. Compute the width and height of each box. Then compute the margin. The only tricky thing here is that we want the area around the text to \emph{look} the same left-right and up-down, which depends on the geometry of the plotting region. <>= text(cbox[,1], cbox[,2], statenames, cex=cex, col=col) # write the labels textwd <- strwidth(statenames, cex=cex) textht <- strheight(statenames, cex=cex) temp <- par("pin") #plot region in inches dx <- margin * temp[2]/mean(temp) # extra to add in the x dimension dy <- margin * temp[1]/mean(temp) # extra to add in y if (box) { drawbox <- function(x, y, dx, dy, lwd, lty, col) { lines(x+ c(-dx, dx, dx, -dx, -dx), y+ c(-dy, -dy, dy, dy, -dy), lwd=lwd, lty=lty, col=col) } for (i in 1:nstate) drawbox(cbox[i,1], cbox[i,2], textwd[i]/2 + dx, textht[i]/2 + dy, col=bcol[i], lwd=lwd[i], lty=lty[i]) dx <- 2*dx; dy <- 2*dy # move arrows out from the box } @ Now for the hard part, which is drawing the arrows. The entries in the connection matrix are 0= no connection or $1+d$ for $-1 < d < 1$. The connection is an arc that passes from the center of box 1 to the center of box 2, and through a point that is $dz$ units above the midpoint of the line from box 1 to box 2, where $2z$ is the length of that line. For $d=1$ we get a half circle to the right (with respect to traversing the line from A to B) and for $d= -1$ we get a half circle to the left. If $d=0$ it is a straight line. If A and B are the starting and ending points then AB is the chord of a circle. Draw radii from the center to A, B, and through the midpoint $c$ of AB. This last has length $dz$ above the chord and $r- dz$ below where $r$ is the radius. Then we have \begin{align*} r^2 & = z^2 + (r-dz)^2 \\ 2rdz &= z^2 + (dz)^2 \\ r &= \left[z (1+ d^2) \right ]/ 2d \end{align*} Be careful with negative $d$, which is used to denote left-hand arcs. The angle $\theta$ from A to B is the arctan of $B-A$, and the center of the circle is at $C = (A+B)/2 + (r - dz)(\sin \theta, -\cos \theta)$. We then need to draw the arc $C + r(\cos \phi, \sin \phi)$ for some range of angles $\phi$. The angles to the centers of the boxes are $\arctan(A-C)$ and $\arctan(B-C)$, but we want to start and end outside the box. It turned out that this is more subtle than I thought. The solution below uses two helper functions \code{statefigx} and \code{statefigy}. The first accepts $C$, $r$, the range of $\phi$ values, and a target $y$ value. It returns the angles, within the range, such that the endpoint of the arc has horizontal coordinate $x$, or an empty vector if none such exists. For an arc there are sometimes two solutions. First calculate the angles for which the arc will strike the horizontal line. If the arc is too short to reach the line then there is no intersection. The return legal angles. <>= statefigx <- function(x, C, r, a1, a2) { temp <-(x - C[1])/r if (abs(temp) >1) return(NULL) # no intersection of the arc and x phi <- acos(temp) # this will be from 0 to pi pi <- 3.1415926545898 # in case someone has a variable "pi" if (x > C[1]) phi <- c(phi, pi - phi) else phi <- -c(phi, pi - phi) # Add reflection about the X axis, in both forms phi <- c(phi, -phi, 2*pi - phi) amax <- max(a1, a2) amin <- min(a1, a2) phi[phi amin] } statefigy <- function(y, C, r, a1, a2) { pi <- 3.1415926545898 # in case someone has a variable named "pi" amax <- max(a1, a2) amin <- min(a1, a2) temp <-(y - C[2])/r if (abs(temp) >1) return(NULL) # no intersection of the arc and y phi <- asin(temp) # will be from -pi/2 to pi/2 phi <- c(phi, sign(phi)*pi -phi) # reflect about the vertical phi <- c(phi, phi + 2*pi) phi[phi amin] } @ <>= phi <- function(x1, y1, x2, y2, d, delta1, delta2) { # d = height above the line theta <- atan2(y2-y1, x2-x1) # angle from center to center if (abs(d) < .001) d=.001 # a really small arc looks like a line z <- sqrt((x2-x1)^2 + (y2 - y1)^2) /2 # half length of chord ab <- c((x1 + x2)/2, (y1 + y2)/2) # center of chord r <- abs(z*(1 + d^2)/ (2*d)) if (d >0) C <- ab + (r - d*z)* c(-sin(theta), cos(theta)) # center of arc else C <- ab + (r + d*z)* c( sin(theta), -cos(theta)) a1 <- atan2(y1-C[2], x1-C[1]) # starting angle a2 <- atan2(y2-C[2], x2-C[1]) # ending angle if (abs(a2-a1) > pi) { # a1= 3 and a2=-3, we don't want to include 0 # nor for a1=-3 and a2=3 if (a1>0) a2 <- a2 + 2 *pi else a1 <- a1 + 2*pi } if (d > 0) { #counterclockwise phi1 <- min(statefigx(x1 + delta1[1], C, r, a1, a2), statefigx(x1 - delta1[1], C, r, a1, a2), statefigy(y1 + delta1[2], C, r, a1, a2), statefigy(y1 - delta1[2], C, r, a1, a2), na.rm=TRUE) phi2 <- max(statefigx(x2 + delta2[1], C, r, a1, a2), statefigx(x2 - delta2[1], C, r, a1, a2), statefigy(y2 + delta2[2], C, r, a1, a2), statefigy(y2 - delta2[2], C, r, a1, a2), na.rm=TRUE) } else { # clockwise phi1 <- max(statefigx(x1 + delta1[1], C, r, a1, a2), statefigx(x1 - delta1[1], C, r, a1, a2), statefigy(y1 + delta1[2], C, r, a1, a2), statefigy(y1 - delta1[2], C, r, a1, a2), na.rm=TRUE) phi2 <- min(statefigx(x2 + delta2[1], C, r, a1, a2), statefigx(x2 - delta2[1], C, r, a1, a2), statefigy(y2 + delta2[2], C, r, a1, a2), statefigy(y2 - delta2[2], C, r, a1, a2), na.rm=TRUE) } list(center=C, angle=c(phi1, phi2), r=r) } @ Now draw the arrows, one at a time. I arbitrarily declare that 20 segments is enough for a smooth curve. <>= arrow2 <- function(...) arrows(..., angle=20, length=.1) doline <- function(x1, x2, d, delta1, delta2, lwd, lty, col) { if (d==0 && x1[1] ==x2[1]) { # vertical line if (x1[2] > x2[2]) # downhill arrow2(x1[1], x1[2]- delta1[2], x2[1], x2[2] + delta2[2], lwd=lwd, lty=lty, col=col) else arrow2(x1[1], x1[2]+ delta1[2], x2[1], x2[2] - delta2[2], lwd=lwd, lty=lty, col=col) } else if (d==0 && x1[2] == x2[2]) { # horizontal line if (x1[1] > x2[1]) # right to left arrow2(x1[1]-delta1[1], x1[2], x2[1] + delta2[1], x2[2], lwd=lwd, lty=lty, col=col) else arrow2(x1[1]+delta1[1], x1[2], x2[1] - delta2[1], x2[2], lwd=lwd, lty=lty, col=col) } else { temp <- phi(x1[1], x1[2], x2[1], x2[2], d, delta1, delta2) if (d==0) { arrow2(temp$center[1] + temp$r*cos(temp$angle[1]), temp$center[2] + temp$r*sin(temp$angle[1]), temp$center[1] + temp$r*cos(temp$angle[2]), temp$center[2] + temp$r*sin(temp$angle[2]), lwd=lwd, lty=lty, col=col) } else { # approx the curve with 21 segments # arrowhead on the last one phi <- seq(temp$angle[1], temp$angle[2], length=21) lines(temp$center[1] + temp$r*cos(phi), temp$center[2] + temp$r*sin(phi), lwd=lwd, lty=lty, col=col) arrow2(temp$center[1] + temp$r*cos(phi[20]), temp$center[2] + temp$r*sin(phi[20]), temp$center[1] + temp$r*cos(phi[21]), temp$center[2] + temp$r*sin(phi[21]), lwd=lwd, lty=lty, col=col) } } } @ The last arrow bit is the offset. If offset $\ne 0$ and there is a bidirectional arrow between two boxes, and the arc for both of them is identical, then move each arrow just a bit, orthagonal to a segment connecting the middle of the two boxes. If the line goes from (x1, y1) to (x2, y2), then the normal to the line at (x1, x2) is (y2-y1, x1-x2), normalized to length 1. The -1 below (\code{-offset}) makes the shift obey a left-hand rule: looking down a line segement towards the arrow head, we shift to the left. This makes two horizontal arrows stack in the normal typographical order for chemical reactions, the right facing one above the left facing. A user can use a negative value for offset to reverse this if they wish. <>= k <- 1 for (j in 1:nstate) { for (i in 1:nstate) { if (i != j && connect[i,j] !=0) { if (connect[i,j] == 2-connect[j,i] && offset!=0) { #add an offset toff <- c(cbox[j,2] - cbox[i,2], cbox[i,1] - cbox[j,1]) toff <- -offset *toff/sqrt(sum(toff^2)) doline(cbox[i,]+toff, cbox[j,]+toff, connect[i,j]-1, delta1 = c(textwd[i]/2 + dx, textht[i]/2 + dy), delta2 = c(textwd[j]/2 + dx, textht[j]/2 + dy), lty=alty[k], lwd=alwd[k], col=acol[k]) } else doline(cbox[i,], cbox[j,], connect[i,j]-1, delta1 = c(textwd[i]/2 + dx, textht[i]/2 + dy), delta2 = c(textwd[j]/2 + dx, textht[j]/2 + dy), lty=alty[k], lwd=alwd[k], col=acol[k]) k <- k +1 } } } @ survival/noweb/main.Rnw0000644000176200001440000000542314024431427014660 0ustar liggesusers\documentclass{article} \usepackage{noweb} \usepackage{amsmath} \usepackage{fancyvrb} \usepackage{graphicx} \addtolength{\textwidth}{1in} \addtolength{\oddsidemargin}{-.5in} \setlength{\evensidemargin}{\oddsidemargin} \newcommand{\myfig}[1]{\includegraphics[width=\textwidth]{figures/#1.pdf}} \newcommand{\code}[1]{\texttt{#1}} \newcommand{\xbar}{\overline{x}} \newcommand{\sign}{{\rm sign}} \noweboptions{breakcode} \title{Survival Package Functions} \author{Terry Therneau} \begin{document} \maketitle \tableofcontents \section{Introduction} \begin{quotation} Let us change or traditional attitude to the construction of programs. Instead of imagining that our main task is to instruct a \emph{computer} what to do, let us concentrate rather on explaining to \emph{humans} what we want the computer to do. (Donald E. Knuth, 1984). \end{quotation} This is the definition of a coding style called \emph{literate programming}. I first made use of it in the \emph{coxme} library and have become a full convert. For the survival library only selected objects are documented in this way; as I make updates and changes I am slowly converting the source code. The first motivation for this is to make the code easier for me, both to create and to maintain. As to maintinance, I have found that whenver I need to update code I spend a lot of time in the ``what was I doing in these x lines?'' stage. The code never has enough documentation, even for the author. (The survival library is already better than the majority of packages in R, whose comment level is abysmal. In the pre-noweb source code about 1 line in 6 has a comment, for the noweb document the documentation/code ratio is 2:1.) I also find it helps in creating new code to have the real documentation of intent --- formulas with integrals and such --- closely integrated. The second motivation is to leave code that is well enough explained that someone else can take it over. The source code is structured using \emph{noweb}, one of the simpler literate programming environments. The source code files look remakably like Sweave, and the .Rnw mode of emacs works perfectly for them. This is not too surprising since Sweave was also based on noweb. Sweave is not sufficient to process the files, however, since it has a different intention: it is designed to \emph{execute} the code and make the results into a report, while noweb is designed to \emph{explain} the code. We do this using the \code{noweb} library in R, which contains the \code{noweave} and \code{notangle} functions. (It would in theory be fairly simple to extend \code{knitr} to do this task, which is a topic for further exploration one day. A downside to noweb is that like Sweave it depends on latex, which has an admittedly steep learning curve, and markdown is thus attractive.) survival/noweb/Makefile0000644000176200001440000000436414110732403014701 0ustar liggesusersPARTS = main.Rnw \ coxph.Rnw \ exact.nw \ agreg.Rnw \ coxsurv.Rnw \ coxsurv2.Rnw \ coxsurv3.Rnw \ finegray.Rnw \ predict.coxph.Rnw \ concordance.Rnw \ survexp.Rnw \ parse.Rnw \ pyears.Rnw pyears2.Rnw \ residuals.survfit.Rnw \ residuals.survreg.Rnw \ survfit.Rnw \ survfitKM.Rnw \ survfitCI.Rnw \ msurv.nw \ survfitms.Rnw \ survexpm.Rnw\ plot.Rnw \ statefig.Rnw\ tmerge.Rnw\ yates.Rnw yates2.Rnw\ zph.Rnw \ tail # coxdetail.nw SFUN = agreg.fit.R \ agsurv.R \ concordance.R \ coxph.R \ coxsurvfit.R \ finegray.R \ model.matrix.coxph.R \ parsecovar.R \ plot.survfit.R \ predict.coxph.R \ pyears.R \ print.pyears.R \ residuals.survfit.R \ residuals.survreg.R\ statefig.R \ survexp.R \ survexpm.R \ survfit.R \ survfitCI.R \ survfitKM.R \ survfit.coxph.R \ survfit.coxphms.R \ survfitms.R\ tmerge.R \ yates.R \ cox.zph.R CFUN = agfit4.c \ agsurv4.c agsurv5.c \ cdecomp.c \ concordance3.c coxcount1.c \ coxexact.c \ survfitci.c \ survfitkm.c \ # coxdetail2.c RDIR = ../R RFUN = $(SFUN:%=$(RDIR)/%) CFUN2= $(CFUN:%=../src/%) DOCDIR= ../inst/doc all: noweb.sty doc fun doc: code.pdf code.pdf: code.tex noweb.sty pdflatex code.tex pdflatex code.tex code.nw: $(PARTS) cat $(PARTS) > code.nw code.tex: code.nw echo "library(noweb); noweave('code.nw')" | R --slave $(SFUN): code.nw $(CFUN): code.nw $(CFUN2): code.nw $(RFUN): code.nw .PHONY: fun clean doc all fun: $(RFUN) $(CFUN2) noweb.sty test: $(RFUN) echo $(RFUN) %.R: echo "# Automatically generated from the noweb directory" > $@ echo "require(noweb); notangle('code.nw', target='$(*F)', out='z$(@F)')" | R --slave cat z$(@F) >> $@ rm z$(@F) %.S: echo "# Automatically generated from the noweb directory" > $@ echo "require(noweb); notangle('code.nw', target='$(*F)', out='z$(@F)')" | R --slave cat z$(@F) >> $@ rm z$(@F) %.c: echo "/* Automatically generated from the noweb directory */" > $@ echo "require(noweb); notangle('code.nw', target='$(*F)', out='z$(@F)')" | R --slave cat z$(@F) >> $@ rm z$(@F) clean: -rm -f code.nw code.log code.aux code.toc code.tex code.bbl code.blg code.out -rm -f noweb.sty noweb.sty: echo 'library(noweb); data(noweb); cat(noweb.sty, sep="\n", file="noweb.sty")' | R --slave survival/noweb/refer.bib0000644000176200001440000016526513537676563015064 0ustar liggesusers@string{annals= {Annals of Stat.}} @string{applstat= {Applied Stat.}} @string{bioj = {Biometrical J.}} @string{biok = {Biometrika}} @string{commstata = {Comm. Stat. Theory Methods}} @string{biom = {Biometrics}} @string{jap = {J. Applied Probability}} @string{jasa = {J. Amer. Stat. Assoc.}} @string{jrssa= {J. Royal Stat. Soc. A}} @string{jrssb= {J. Royal Stat. Soc. B}} @string{jrssc= {J. Royal Stat. Soc. C}} @string{jscs = {J Stat. Comput. Simul.}} @string{lifetime = {Lifetime Data Analysis}} @string{NEJM = {New England J. Medicine}} @string{scand = {Scandinavian J. Stat.}} @string{statmed= {Stat. in Medicine}} @string{statsci = {Stat. Science}} @book{Andersen93, author={Andersen, P. K. and Borgan, {\O}. and Gill, R. D. and Keiding, N.}, title= {Statistical Models Based on Counting Processes}, publisher={Springer-Verlag}, address={New York}, year= {1993} } @article{Andersen00, author= {Andersen, P. K. and Esbjerg, S. and S{\o}rensen, T.I.A.} , title= {Multi-state models for bleeding episodes and morality in lever cirrhosis}, year= {2000}, journal=statmed, volume={19}, pages={587--599} } @article{Anderson82, author= {J. R. Anderson and L. Bernstein and M. C. Pike}, year= {1982}, title= {Approximate confidence intervals for probabilities of survival and quantiles in life-table analysis}, journal=biom}, volume={38}, pages= {407--416}, } @book{Anderson58, author= {V. E Anderson and H. O. Goodman and S. Reed}, title= {Variables Related to Human Breast Cancer}, year = 1958, publisher={University of Minnesota Press}, address={Minneapolis} } @article{Barlow88, author= { Barlow, W. E. and Prentice, R. L.}, year = {1988}, title= {Residuals for relative risk regression}, journal= {Biometrika}, volume={75}, pages={65--74} } @article{Barlow94, author= {Barlow, W. E.}, year ={1994}, title= {Robust variance estimation for the case-cohort design}, journal= {Biometrics}, volume={50}, pages= {1064--1072} } @article{Bartolucci77, author = {Bartolucci, A. A. and Fraser, M. D.}, year = 1977, title = {Comparative step-up and composite tests for selecting prognostic indicators associated with survival}, journal = {Biometrical J.}, volume = 19, pages = {437-448} } @book{Becker84, author={Becker, R. A. and Chambers, J. M.}, title= {S: {A}n Interactive Environment for Data Analysis and Graphics}, publisher={Wadsworth}, address={Belmont, CA}, year= {1984} } @techreport{Bergstralh88, author= {Bergstralh, E. J. and Offord, K. P.}, year = {1988}, title = {Conditional probabilities used in calculating cohort expected survival}, number = {37}, institution= {Department of Health Sciences Research, Mayo Clinic} } @article{Berry83, author= {Berry, G.}, year = {1983}, title= {The analysis of mortality by the subject years method}, journal= biok, volume={39}, pages={173--184} } @book{Bickel77, author={Bickel, P. J. and Doksum,K. J. }, title= { Mathematical Statistics: Basic Ideas and Selected Topics}, publisher={Holden-Day}, address={San Francisco}, year= {1977} } @article{Binder92, author= {Binder, D. A.}, year= {1992}, title= {Fitting {C}ox's proportional hazards models from survey data}, journal={Biometrika}, volume={79}, pages={139--147} } @ARTICLE{Blackstone86, author = {Blackstone, E. H. and Naftel, D. C. and Turner, M. E.}, year = 1986, title = {The decomposition of time-varying hazard into phases, each incorporating a separate stream of concomitant information}, journal = JASA, volume = 81, pages = {615-624}, annote = {parametric survival models; non PH} } @article{Bonsel90, author= {Bonsel, G. J. and Klompmaker, I. J. and {van't Veer}, F. and Habbema, J. D. F. and Slooff, M. J. H.}, year= {1990}, title= {Use of prognostic models for assessment of value of liver transplantation in primary biliary cirrhosis}, journal={Lancet}, volume={335}, pages={493--497} } @article{Borgan95, author= {Borgan, \O. and Goldstein, L. and Langholz, B.}, year= {1995}, title= {Methods for the analysis of sampled cohort data in the {C}ox proportional hazards model}, journal=annals, volume={23}, pages={1749--1778} } @article{Breslow93, author= {Breslow, N. E. and Clayton, D. G.}, year= {1993}, title= {Approximate inference in generalized linear mixed models}, journal=jasa, volume={88}, pages={9--25} } @article{Cai95, author= {Cai, J. and Prentice, R. G.}, year= {1995}, title= {Estimating equations for hazard ratio parameters based on correlated failure time data}, journal={Biometrika}, volume={82}, pages={151--164} } @article{Cain84, author={Cain, K. C. and Lange, N. T.}, year= {1984}, title= {Approximate case influence for the proportional hazards regression model with censored data}, journal={Biometrics}, volume={40}, pages={493--499} } @book{Chambers83, author= {Chambers, J. M. and Cleveland, W. S. and Kleiner, B. and Tukey, P. A.}, year= {1983}, title={Graphical Methods for Data Analysis}, publisher ={Wasdworth}, address ={Belmont, CA} } @book{Chambers91, author= {Chambers, J. M. and Hastie, T. J.}, year= {1993}, title={Statistical Models in {S}}, publisher ={Chapman and Hall}, address ={New York} } @article{Chappell92, author= {Chappell, R.}, year= {1992}, title= {A note on linear rank tests and {G}ill and {S}chumacher's tests of proportionality}, journal={Biometrika}, volume={79}, pages= {199--201} } @article{Chen91, author= {Chen, C. H. and Wang, P. C.}, year= {1991}, title= {Diagnostic plots in {C}ox's regression model}, journal={Biometrics}, volume={47}, pages= {841--850} } @article{Clegg99, author= {Clegg, L. X. and Cai, J. and Sen, P. K.}, year= {1999}, title= {A marginal mixed baseline hazards model for multivariate failure time data}, journal=jasa, volume={55}, pages= {805--812} } @book{Cook82, author= {Cook, R. D. and Weisberg, S.}, year= {1982}, title= {Residuals and Influence in Regression}, publisher={Chapman and Hall}, address= {London} } @article{Cox72, author= {Cox, D. R.}, year= {1972}, title= {Regression models and life-tables (with discussion)}, journal=jrssb, volume={34}, pages= {187--220} } @book{Cox84, author= {Cox, D. R. and Oakes, D. O.}, year= {1984}, title= {Analysis of Survival Data}, publisher= {Chapman and Hall}, address= {London} } @book{Cochran76, author= {Cochran, W.G.}, year= {1976}, title={Sampling Techniques, third edition}, publisher ={Wiley}, address ={New York} } @article{Crowley77, author= {Crowley, J. and Hu, M.}, year= {1977}, title= {Covariance analysis of heart transplant survival data}, journal=jasa, volume={72}, pages= {27--36} } @article{Dempster77, author= {Dempster, A. P. and Laird, N. M. and Rubin, D. B.}, year= {1977}, title= {Maximum likelihood from incomplete data via the {EM} algorithm (with discussion)}, journal=jrssb, volume={39}, pages= {1--38} } @techreport{Deng95, author= {Deng, Y. and Quigley, J.M. and Van Order, R.}, year= {1995}, title= {Mortgage Terminations}, institution={Institute of Business and Economic Research, University of California at Berkeley}, type= {Working Paper}, number={95-230}, } @article{Dickson89, author= {Dickson, E. R. and Grambsch, P. M. and Fleming, T. R and Fisher, L. D. and Langworthy, A.}, year= {1989}, title= {Prognosis in primary biliary cirrhosis: Model for decision making}, journal ={Hepatology}, volume={10}, pages ={1--7} } @article{Ederer61, author= {Ederer, F. and Axtell, L. M. and Cutler, S. J.}, year= {1961}, title= {The relative survival rate: A statistical methodology}, journal ={National Cancer Inst. Monographs}, volume={6}, pages ={101--121} } @techreport{Ederer77, author= {Ederer, F. and Heise, H.}, year= {1977}, title= {Instructions to {IBM} 650 programmers in processing survival computations}, institution={End Results Evaluation Section, National Cancer Institute}, type={Methodological Note}, number={No. 10}, pages ={101--121} } @article{Edmonson79, author= {Edmonson, J. H. and Fleming, T. R. and Decker, D. G. and Malkasian, G. D. and Jorgensen, E. O. and Jefferies, J. A. and Webb, M. J. and Kvols, L. K.}, year= {1979}, title= {Different chemotherapeutic sensitivities and host factors affecting prognosis in advanced ovarian carcinoma versus minimal residual disease }, journal ={Cancer Treatment Reports}, volume={63}, pages ={241--247} } @article{Efron77, author= {Efron, B.}, year= {1977}, title ={The efficiency of {C}ox's likelihood function for censored data}, journal=jasa, volume={72}, pages= {557--565} } @book{Efron82, author= {Efron, B.}, year= {1982}, title ={The Jackknife, the Bootstrap and Other Resampling Plans}, publisher={SIAM}, address ={Philadelphia} } @article{Ezekiel24, author= {Ezekiel, M.}, year= {1924}, title ={A method for handling curvilinear correlation for any number of variables}, journal=jasa, volume={19}, pages= {431--453} } @article{Fleming81, author= {Fleming, T. R. and Harrington, D. P.}, year= {1981}, title ={A class of hypothesis tests for one and two sample censored survival data}, journal=commstata, volume={10}, pages= {763--794} } @article{Fleming84, author= {Fleming, T. R. and Harrington, D. P.}, year= {1984}, title ={Nonparametric estimation of the survival distribution in censored data}, journal=commstata, volume={13}, pages= {2469--2486} } @book{Fleming91, author= {Fleming, T. R. and Harrington, D. P.}, year= {1991}, title= {Counting Processes and Survival Analysis}, publisher= {Wiley}, address= {New York} } @article{Eilers96, author= {Eilers, P. H. C. and Marx, B. D.}, year= {1996}, title ={Flexible smoothing with {B}-splines and penalties}, journal={Stat. Science}, volume={11}, pages= {89--121} } @article{Gail81, author= {Gail, M. H. and Lubin, J. H. and Rubinstein, L. V.}, year = {1981}, title = {Likelihood Calculations for Matched Case-Control Studies and Survival Studies with Tied Death Times}, journal=biok, volume=68, pages={703--707} } @article{Gail86, author= {Gail, M. H. and Byar, D. P.}, year= {1986}, title= {Variance calculations for direct adjusted survival curves, with applications to testing for no treatment effect}, journal=bioj, volume={28}, pages= {587--599} } @article{Gill87, author= {Gill, R. and Schumacher, M.}, year= {1987}, title= {A simple test of the proportional hazards assumption}, journal={Biometrika}, volume={74}, pages= {289--300} } @article{Grambsch94, author= {Grambsch, P. M. and Therneau, T. M.}, year= {1994}, title ={Proportional hazards tests and diagnostics based on weighted residuals}, journal={Biometrika}, volume={81}, pages= {515--526} } @article{Grambsch95, author= {Grambsch, P. M. and Therneau, T. M. and Fleming, T. R.}, year= {1995}, title= {Diagnostic plots to reveal functional form for covariates in multiplicative intensity models}, journal={Biometrics}, volume={51}, pages= {1469-1482} } @article{Gray92, author= {Gray, R. J.}, year = {1992}, title= {Flexible methods for analyzing survival data using splines, with applications to breast cancer prognosis}, journal=jasa, volume={87}, pages = {942--951} } @article{Gray94, author= {Gray, R. J.}, year = {1994}, title= {Spline-based tests in survival analysis}, journal=biom, volume={50}, pages = {640--652} } @article{Green84, author={Green, P.J.}, year = {1984}, title = {Iteratively reweighted least squares for maximum likelihood estimation, and some robust and resistant alternatives (with discussion).}, journal=jrssb, volume=46, pages={149--192} } @techreport{Hall95, author= {Hall, C. B. and Zeger, S. L. and Bandeen-Roche, K. J.}, year= {1995}, title= {Adjusted variable plots for {C}ox's proportional hazards regression model}, institution={The John's Hopkins University, School of Hygiene and Public Health, Department of Biostatistics} } @incollection{Harrell86, author= {Harrell, F.}, year= {1986}, title= {The PHGLM procedure}, booktitle= {SAS Supplemental Library User's Guide, Version 5}, address= {Cary, NC}, publisher= {SAS Institute Inc} } @article{Hakama77, author= {Hakama, M. and Hakulinen, T.}, year= {1977}, title= {Estimating the expectation of life in cancer survival studies with incomplete follow-up information}, journal={J Chronic Diseases}, volume={30}, pages= {585--597} } @article{Hakulinen82, author= {Hakulinen, T.}, year= {1982}, title= {Cancer survival corrected for heterogeneity in patient withdrawal}, journal=biom, volume={38}, pages= {933--942} } @article{Hakulinen85, author= {Hakulinen, T. and Abeywickrama, K. H.}, year= {1985}, title= {A computer program package for relative survival analysis}, journal={Computer Programs in Biomedicine}, volume={19}, pages= {197--207} } @article{Hakulinen77, author= {Hakulinen, T.}, year= {1977}, title= {On long term relative survival rates}, journal={J Chronic Diseases}, volume={30}, pages= {431--443} } @article{Harrington82, author= {Harrington, D. P. and Fleming, T. R.}, year= {1982}, title= {A class of rank test procedures for censored survival data}, journal=biok, volume={69}, pages= {553-566} } @book{Hastie90, author= {Hastie, T. J. and Tibshirani, R. J.}, year= {1990}, title ={Generalized Additive Models}, publisher ={Chapman and Hall}, address= {London} } @article{Hastie96, author= {Hastie, T. J.}, year= {1996}, title= {Pseudosplines}, journal=jrssb, volume={58}, pages= {379--396} } @article{Heit99, author= {Heit, J. A. and M. D. Siverstein and D. N. Mohr and T. M. Petterson and W. M. O'Fallon and L. J. Melton III}, year= {1999}, title= {Predictors of survival after deep vein thrombosis and pulmonary embolism}, journal={Arch Internal Med}, volume={159}, pages= {445--453} } @techreport{Hodges99, author= {J. S. Hodges and D. J. Sargent}, year= {1998}, title= {Counting degrees of freedom in hierarchical and other richly-parameterized models}, institution={University of Minnesota, Division of Biostatistics}, type={Research Report}, number={98-004} } @book{Hougaard00, author={Hougaard, P.}, title= {Analysis of Multivariate Survival Data}, publisher={Springer-Verlag}, address={New York}, year= {2000} } @inproceedings{Huber67, author= {Huber, P. J.}, year= {1967}, title= {The behavior of maximum likelihood estimates under non-standard conditions}, booktitle= {Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability}, volume={1}, pages= {221--233} } @article{Huffer91, author= {Huffer, F.W. and McKeague, I.W}, title= {Weigthed least squares estimation for {A}alen's additive risk model}, year= {1991}, journal=jasa, volume={86}, pages={114--129} } @article{Hurvich98, author= {Hurvich, C. M. and Simonoff, J. S. and Tsai, C.-L.}, year= {1998}, title= {Smoothing parameter selection in nonparametric regression using an improved {A}kaike information criterion}, journal=jrssb, volume={60}, pages= {271--293} } @article{Islam94, author= {Islam, M. A.}, year= {1994}, title= {Multistate survival models for transitions and reverse transitions: an application to contraceptive use data}, journal=jrssa, volume={157}, pages= {441--455} } @article{Jones90, author={Jones, M. P. and Crowley, J.}, title={Asymptotic proporties of a general class of nonparametric tests for survival analysis}, year={1990}, journal=annals, volume={18},pages={1203--1220} } @book{Kalbfleisch80, author= {Kalbfleisch, J. D. and Prentice, R. L.}, year= {1980}, title= {The Statistical Analysis of Failure Time Data}, publisher= {Wiley}, address ={New York} } @article{Klein91, author= {Kay, R.}, year= {1983}, title ={The analysis of transition times in a multistate stochastic process using proportional hazard regression models}, journal=commstata, volume={11}, pages= {1743--1756} } @article{Kay83, author= {Korn, Edward L. and Graubard, Barry I. and Midthune, Douglas}, year= {1997}, title ={Time-to-event analysis if longitudinal follow-up of a survey: Choice of the time scale}, journal={Am J of Epidemiology}, volume={145}, pages= {72--80} } @article{Korn97, author= {Klein, J. P.}, year= {1991}, title ={Small sample moments of some estimators of the variance of the {K}aplan--{Meier} and {N}elson--{A}alen estimators}, journal=scand, volume={18}, pages= {333--340} } @article{Lagakos84, author={Lagakos, S. W. and Schoenfeld, D. A.}, title={Properties of proportional-hazards score tests under misspecified regression models}, year={1984}, journal=biom, volume={40}, pages={1037--1048} } @article{Laird81, author= {Laird, N. and Olivier, D.}, year= {1981}, title= {Covariance analysis of censored survival data using log-linear analysis techniques}, journal=jasa, volume={76}, pages= {231--240} } @article{Langholz91, author= {Langholz, B. and Thomas, D.C.}, year= {1991}, title= {Efficiency of cohort sampling designs: some surprising resluts}, journal={Biometrics}, volume={47}, pages= {1563--1572} } @article{Langholz96, author= {Langholz, B. and Goldstein, L.}, year= {1996}, title= {Risk set sampling in epidemiologic cohort studies}, journal={Statistical Science}, volume={11}, pages= {35--53} } @article{Laurie89, author ={Laurie, J. A. and Moertel, C. G. and Fleming, T. R. and Wieand, H. S. and Leigh, J. E. and Rubin, J. and McCormack, G. W. and Gerstner, J. B. and Krook, J. E. and Malliard, J.}, year= {1989}, title= {Surgical adjuvant therapy of large-bowel carcinoma: {A}n evaluation of levamisole and the combination of levamisole and fluorouracil: The {N}orth {C}entral {C}ancer {T}reatment {G}roup and the {M}ayo {C}linic}, journal={J. Clinical Oncology}, volume={7}, pages= {1447--1456} } @incollection{Lee92, author= {Lee, E. W. and Wei, L. J. and Amato, D.}, year= {1992}, title= {{C}ox-type regression analysis for large number of small groups of correlated failure time observations}, editor= {Klein, J. P. and Goel, P. K.}, booktitle= {Survival Analysis, State of the Art}, pages= {237--247}, publisher= {Kluwer}, address= {Netherlands} } @article{Lindor94, author= {Lindor, K. D. and Dickson, E. R. and Baldus, W. P. and Jorgensen, R. A. and Ludwig, J. and Murtaugh, P. A. and Harrison, J. M. and Wiesner, R. H. and Anderson, M. L. and Lange, S. M. and LeSage, G. and Rossi, S. S. and Hofman, A. F.}, year= {1994}, title= {Ursodeoxycholic acid in the treatment of primary biliary cirrhosis}, journal={Gastroenterology}, volume={106}, pages= {1284--1290} } @article{Liang86, author= {Zeger, S. L. and Liang, K. Y.}, year= {1986}, title= {Longitudinal data analysis for discrete and continuous outcomes}, journal={Biometrics}, volume={42}, pages= {121--130} } @article{Liang86b, author= {Liang, K. Y. and Zeger, S. L.}, year= {1986}, title= {Longitudinal data analysis using generalized linear models}, journal=biok, volume={73}, pages= {13--22} } @article{Liang88, author= {Zeger, S. L. and Liang, K. Y. and Albert, P. S.}, year= {1988}, title= {Models for longitudinal data: A generalized estimating equation approach}, journal=biom, volume={44}, pages= {1049--1060} } @article{Lin91, author= {Lin, D. Y.}, year= {1991}, title= {Goodness-of-fit analysis for the {C}ox regression model based on a class of parameter estimators}, journal=jasa, volume={86}, pages= {725--728} } @article{Lin89, author= {Lin, D. Y. and Wei, L. J.}, year= {1989}, title= {The robust inference for the {C}ox proportional hazards model}, journal=jasa, volume={84}, pages= {1074--1078} } @article{Lin91b, author= {Lin, D. Y. and Wei, L. J.}, year= {1991}, title= {Goodness-of-fit tests for the general {C}ox regression model}, journal= {Statistica Sinica}, volume={1}, pages= {1--17} } @article{Lin93, author= {Lin, D. Y. and Wei, L. J. and Ying, Z.}, year= {1993}, title= {Checking the {C}ox model with cumulative sums of martingale-based residuals}, journal={Biometrika}, volume={80}, pages= {557--572} } @article{Lin93b, author= {Lin, D. Y. and Ying, Z.}, year= {1993}, title= {Cox regression with incomplete covariate measurements}, journal= jasa, volume={88}, pages= {1341--1349} } @article{Lin94, author= {Lin, D. Y.}, year= {1994}, title= {Cox regression analysis of multivariate failure time data: the marginal approach}, journal= statmed, volume={13}, pages= {2233--2247} } @article{Link84, author= {C. L. Link}, year= {1984}, title= {Confidence intervals for the survival function using {C}ox's proportional-hazard model with covariates}, journal=biom, volume={40}, pages= {601--610} } @article{Link86, author= {C. L. Link}, year= {1986}, title= {Response to {J}. {O'Quigley}, correspondence section}, journal=biom, volume={42}, pages= {219--220} } @article{Lipsitz90, author= {Lipsitz, S. R. and Laird, N. M. and Harrington, D. P.}, year= {1990}, title= {Using the jackknife to estimate the variance of regression estimators from repeated measures studies}, journal=commstata, volume={19}, pages= {821--845} } @article{Lipsitz94, author= {Lipsitz, S. R. and Dear, K. B .G. and Zhao, L.}, year= {1994}, title= {Jackknife estimators of variance for parameter estimates from estimating equations with applications to clustered survival data}, journal= biom, volume={50}, pages= {842--846} } @article{Lunn95, author= {Lunn, M. and McNeil, D.}, year= {1995}, title= {Applying {C}ox regression to competing risks}, journal={Biometrics}, volume={51}, pages= {524--532} } @article{Mallows86, author= {Mallows, C. L.}, year= {1986}, title= {Augmented partial residuals}, journal={Technometrics}, volume={28}, pages= {313--319} } @article{Mantel66, author= {Mantel, N.}, year= {1966}, title= {Evaluation of survival data and two new rank order statistics arising in its consideration}, journal={Cancer Chemotherapy Reports}, volume={50}, pages= {163--166} } @article{Mantel77, author= {Mantel, N. and Bohidar, N. R. and Ciminera, J. L.}, year= {1977}, title= {Mantel--{H}aenszel analyses of litter-matched time-to-response data with modifications for recovery of interlitter information}, journal={Cancer Research}, volume={37}, pages= {3863--3868} } @book{glim, author= {McCullagh, P. and Nelder, J.A.}, year= {1983}, title= {Generalized Linear Models}, publisher= {Chapman and Hall} } @article{McGilchrist91, author= {McGilchrist, C. A. and Aisbett, C. W.}, year= {1991}, title= {Regression with frailty in survival analysis}, journal={Biometrics}, volume={47}, pages= {461--466} } @article{McGilchrist93, author= {McGilchrist, C. A.}, year= {1993}, title= {{REML} estimation for survival models with frailty}, journal={Biometrics}, volume={49}, pages= {221--225} } @article{McGilchrist95, author= {McGilchrist, C. A. and Yau, K. K. W.}, year= {1995}, title= {The derivation of {BLUP}, {ML} and {REML} estimation methods for generalised linear mixed models}, journal= commstata, volume={24}, pages= {2963--2980} } @book{Miller81, author= {Miller, Jr., R. G.}, year= {1981}, title ={Survival Analysis}, publisher= {Wiley}, address= {New York} } @article{Moertel90, author= {Moertel, C.G. and Fleming, T.R. and McDonald, J.S. and Haller, D.G. and Laurie, J.A. and Goodman, P.J. and Ungerleider, J.S. and Emerson, W.A. and Tormey, D.C. and Glick, J.H. and Veeder, M.H. and Mailliard, J.A.}, year= {1990}, title= {Levamisole and fluorouracil for adjucant therapy of resected colon carcinoma.}, journal= NEJM, volume={332}, pages= {352--358} } @article{Moreau85, author= {Moreau, T. and O'Quigley, J. and Mesbah, M.}, year= {1985}, title= { A global goodness-of-fit statistic for the proportional hazards model}, journal= {Applied Stat.}, volume={34}, pages= {212--218} } @article{Moss83, author= {Moss, A. J. and {the Multicenter Postinfarction Research Group}}, year= {1983}, title= {Risk stratification and survival after myocardial infarction}, journal= {New England J. Medicine}, volume={309}, pages= {331--336} } @article{Moss88, author= {Moss, A. J. and {the Multicenter Diltiazem Postinfarction Trial Research Group}}, year= {1988}, title= {The effect of diltiazem on mortality and reinfarction after myocardial infarction}, journal= {New England J. Medicine}, volume={319}, pages= {385--392} } @book{Mosteller77, author= {Mosteller, F. and Tukey, J. W.}, year= {1977}, title= {Data Analysis and Regression}, publisher= {Addison-Wesley}, address={Reading, MA} } @article{Nagelkerke84, author= {Nagelkerke, N. J. D. and Oosting, J. and Hart, A. A. M.}, year= {1984}, title ={A simple test for goodness of fit of {C}ox's proportional hazards model}, journal={Biometrics}, volume={40}, pages= {483--486} } @article{Neuberger86, author= {Neuberger, J. and Altman, D. G. and Christensen, E. and Tygstrup, N. and Williams, R.}, year= {1986}, title ={Use of a prognostic index in evaluation of liver transplantation for primary biliary cirrhosis}, journal={Transplantation}, volume={41}, pages= {713--716} } @article{Nielsen92, author= {Nielsen, G. G. and Gill, R. D. and Andersen, P. K. and S{\o}rensen, T. I.}, year= {1992}, title ={A counting process approach to maximum likelihood estimation of frailty models}, journal=scand, volume={19}, pages= {25--43} } article{Oakes93, author={Oakes, D. and A.J. Moss and J.T. Fleiss and J.T. Bigger, Jr. and T.M. Therneau and S.W. Eberly and M.P. McDermott and A. Manatunga and E. Carleen and J. Benhorin, and {the Multicenter Diltiazem Post-Infarction Research Group}}, year = {1993}, title = {Use of compliance measures in and analysis of the effect of {D}iltiazem on mortality and reinfarction after myocardial infarction}, journal=jasa, volume={88}, pages = {44-49} } @incollection{Oakes92, author= {Oakes, D.}, year= {1992}, title ={Frailty models for multiple event times}, editor= {Klein, J. P. and Goel, P. K.}, booktitle= {Survival Analysis, State of the Art}, publisher= {Kluwer}, address= {Netherlands} } @article{Omori93, author={Omori,Y. and Johnson,R. A.}, title={The influence of random effects on the unconditional hazard rate and survival functions}, year={1993}, journal=biok,volume={80}, pages={910--914} } @phdthesis{Parner96, author={Parner, E.}, title={Inference in semiparametric frailty models}, year ={1997}, school={University of Aarhus, Denmark} } @article{Pettitt90, author={Pettitt, A. N. and Bin Daud, I.}, title={Investigating time dependence in {C}ox's proportional hazards model}, year= {1990}, journal=applstat, volume={39},pages={313--329} } @article{Prentice86, author= {Prentice, R. L.}, year= {1986}, title ={A case-cohort design for epidemilogic cohort studies and disease prevention trials}, journal={Biometrika}, volume={73}, pages ={1--11} } @article{Prentice92, author= {Prentice, R. L. and Cai, J.}, year= {1992}, title ={Covariance and survivor function estimation using censored multivariate failure time data}, journal={Biometrika}, volume={79}, pages ={495--512} } @incollection{Prentice91, author= {Prentice, R. L. and Cai, J.}, year= {1991}, title ={Marginal and conditional models for the analysis of multivariate failure time data}, pages ={393--406}, editor= {Klein, J. P and Goel, P. K.}, booktitle= {Survival Analysis, State of the Art}, publisher= {Kluwer Academic Publishers}, address= {Netherlands} } @article{Prentice81, author= {Prentice, R. L. and Williams, B. J. and Peterson, A. V.}, year= {1981}, title= {On the regression analysis of multivariate failure time data}, journal={Biometrika}, volume={68}, pages= {373--379} } @book{Press88, author= {Press, W.H. and Teukolsky, S.A. and Vetterling, W.T. and Flannery, B.P.}, year= {1988}, title= {Numerical Recipes in {C}}, publisher= {Cambridge University Press}, address ={Cambridge} } @article{Quantin96, author= {Quantin, C. and Moreau, T. and Asselaiin B. and Maccario, J. and Lelloucj, J. } , title= {A regression survival model for testing the proportional hazards hypothesis }, year= {1996}, journal=biom, volume={52}, pages={874--885} } @article{Quigley89, author= {O'Quigley, J. and Pessione, F.}, year= {1989}, title= {Score tests for homogeneity of regression effect in the proportional hazards model}, journal={Biometrics}, volume={45}, pages ={135--144} } @article{Reid85, author= {Reid, N. and Cr{\'{e}}peau, H.}, year= {1985}, title= {Influence functions for proportional hazards regression}, journal={Biometrika}, volume={72}, pages= {1--9} } @article{Ricci97, author= {Ricci, P. and Therneau, T. M. and Malinchoc, M. and Benson, J. T. and Petz, J. L. and Klintmalm, G. B. and Crippin, J. S. and Wiesner, R. H. and Steers, J. L. and Rakela, J. and Starzl, T. E. and Dickson, E. R.}, year= {1997}, title= {A prognostic model for the outcome of liver transplantation in patients with cholestatic liver disease}, journal={Hepatology}, volume={25}, pages= {672--677} } @manual{SAS96, author={{SAS Institute Inc.}}, year={1996}, title={{SAS/STAT} Software: Changes and Enhancements through Release 6.11}, organization={{SAS} Institute, Inc.}, address={Cary, N.C.}, note={Chapter 8: The PHREG procedure} } @article{Sastry97, author= {Sastry, N.}, title={A nested frailty model for survival data, with an application to the study of child survival in northesat Brazil}, year={1997}, journal=jasa, volume={92}, pages = {426--435} } @article{Schoenfeld80, author= {Schoenfeld, D.}, year= {1980}, title= {Chi-squared goodness-of-fit tests for the proportional hazards regression model}, journal={Biometrika}, volume={67}, pages= {145--153} } @article{Schoenfeld81, author={Schoenfeld, D.}, title={The asymptotic properties of nonparametric tests for comparing survival distributions}, year={1981}, journal=biok, volume={68}, pages={316--319} } @article{Schoenfeld83, author={Schoenfeld, D. A.}, title={Sample-size formula for the proportional-hazards regression model}, year={1983}, journal=biom, volume={39}, pages={499--503} } @article{Schum87, author={Schumacher, M. and Olschewski, M. and Schmoor,C.}, title={The impact of heterogeneity on the comparison of survival times}, year={1987}, journal=statmed, volume={6},pages={773-784} } @article{Segal93, author= {Segal, M.R. and Neuhaus, J. M.}, year= {1993}, title= {Robust inference for multivariate survival data}, journal=statmed, volume={12}, pages= {1019--1031} } @article{Segal94, author= {Segal, M.R. and Baccheti, P. and Jewell, N. P.}, year= {1994}, title= {Variances for maximum penalized likelihood estimates obtained via the {EM} algorithm }, journal=jrssb, volume={56}, pages= {345--352} } @article{Simon11, title= {Regularization Paths for {C}ox’s Proportional Hazards Model via Coordinate Descent}, author= {Noah Simon and Jerome Friedman and Trevor Hastie Rob Tibshirani}, journal={J Statistical Software}, volume=39, pages={1--123} } @article{Smith96, author= {Smith, P.J. and Hietjan, D.F.}, year= {1996}, title= {Testing and adjusting for overdispersion in generalized linear models}, journal=jrssc, volume={?} } @article{Solomon84, author= {Solomon, P. J. } , title= {Effect of misspecification of regression models in the analysis of survival data}, year= {1984}, journal=biok, volume={71}, pages={291--298} } @article{Solomon86, author= {Solomon, P. J. } , title= {Amendments and corrections}, year= {1986}, journal=biok, volume={73}, pages={245--245} } @book{Spector94, author={Spector, P.}, title= {An Introduction to {S} and {S-Plus}}, publisher={Wadsworth}, address={Pacific Grove, CA}, year= {1994} } @article{Stablein81, author= {Stablein, D. M. and Carter, Jr., W. H. and Novak, J. W.}, title= {Analysis of survival data with nonproportional hazard functions}, year= {1981}, journal={Controlled Clinical Trials}, volume={2}, pages={149--159} } @inproceedings{Stone85, author= {Stone, C. J. and Koo, C. Y.} , title= { Additive splines in statistics}, year= {1985}, booktitle={Computational Statistics Section}, organization={American Statistical Association}, address={Alexandria, Virginia}, pages={646--651} } @article{Stone86, author= {Stone, C. J.} , title= {Comment to paper by {H}astie and {T}ibshirani}, year= {1986}, journal=statsci, volume={1}, pages={312--314} } @article{Storer85, author= {Storer, B. E. and Crowley, J.}, year= {1985}, title ={A diagnostic for {C}ox regression and general conditional likelihoods}, journal =jasa, volume={80}, pages= {139-147} } @article{Struthers86, author= {Struthers, C. A. and Kalbfleisch, J. D. } , title= {Misspecified proportional hazard models}, year= {1986}, journal=biok, volume={73}, pages={363--369} } @article{Therneau90, author= {Therneau, T. M. and Grambsch, P. M. and Fleming, T. R.}, year= {1990}, title= {Martingale based residuals for survival models}, journal={Biometrika}, volume={77}, pages= {147--160} } @article{Therneau97, author= {Therneau, T. M. and Hamilton, S. A.}, year= {1997}, title= {{rhDNase} as an example of recurrent event analysis}, journal=statmed, volume={16}, pages= {2029--2047} } @article{Therneau99, author= {Therneau, T. M. and Li, H.}, year= {1999}, title= {Computing the {C}ox model for case-cohort designs}, journal=lifetime, volume={5}, pages= {99--112} } @book{Therneau00, author={Therneau, T. M. and Grambsch, P. M.}, title= {Modeling Survival Data: Extending the {C}ox Model}, publisher={Springer-Verlag}, address={New York}, year= {2000} } @article{Therneau03, author= {Therneau, T. M. and Grambsch, P. M. and Pankratz, V. S.}, year= {2003}, title= {Penalized survival models and frailty}, journal={J Computational Graphical Statistics}, volume={12}, pages= {156--175} } @article{Thomsen91, author= {Thomsen, B. L. and Keiding, N. and Altman, D. G.}, year= {1991}, title= {A note on the calculation of expected survival, illustrated by the survival of liver transplant patients}, journal=statmed, volume={10}, pages= {733--738} } @article{Thomsen92, author= {Thomsen, B. L. and Keiding, N. and Altman, D. G.}, year= {1992}, title= {Reply to a letter to the editor}, journal=statmed, volume={11}, pages= {1528--1530} } @article{Uitti93, author= {Uitti, R.J. and Ahlskog, J.E. and Maraganore, D.M. and Muenter, M.D. and Atkinson, E.J. and Cha, R.H. and O'Brien, P.C.}, year= {1993}, title= {Levodopa therapy and survival in idiopathic Parkinson's disease: Olmsted County Project}, journal ={Neurology}, volume={43}, pages= {1918--1926} } @book{Venables97, author= {Venables, W. N. and Ripley, B. D.}, year= {1997}, title= {Modern Applied Statistics with {S-PLUS}, second edition}, publisher= {Springer-Verlag}, address ={New York} } @article{Verhuel93, author= {Verheul, H. A. and Dekker, E. and Bossuyt, P. and Moulijn, A. C. and Dunning, A. J.}, year= {1993}, title= {Background mortality in clinical survival studies}, journal ={Lancet}, volume={341}, pages= {872--875} } @article{Wahba83, author= {Wahba, G.}, year= {1983}, title= {Bayesian ''confidence intervals'' for the cross-validated smoothing spline}, journal =JRSSB, volume={45}, pages= {133--150} } @article{Wei89, author= {Wei, L. J. and Lin, D. Y. and Weissfeld, L.}, year= {1989}, title= {Regression analysis of multivariate incomplete failure time data by modeling marginal distributions}, journal=jasa, volume={84}, pages= {1065--1073} } @article{Wei93, author= {Lin, D. Y. and Wei, L. J. and Ying, Z.}, year= {1993}, title= {Checking the {C}ox model with cumulative sums of martingale-based residuals}, journal=biok, volume={80}, pages= {557--572} } @article{White80, author= {White, H.}, year= {1980}, title ={A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity}, journal= {Econometrica}, volume={48}, pages= {817--838} } @article{White82, author= {White, H.}, year= {1982}, title= {Maximum likelihood estimation of misspecified models}, journal= {Econometrica}, volume={50}, pages= {1--26} } @article{Whitehead80, author= {Whitehead, J.}, year= {1980}, title= {Fitting {C}ox's regression model to survival data using {GLIM}}, journal= applstat, volume={29}, pages= {268--275} } @unpublished{Winemiller98, author= {Winemiller, M.H. and Stolp-Smith, K.A, and Silverstein, M.D. and Therneau, T.M.}, year= {1998}, title= {Sequential pneumatic compression or heparin is effective in preventing venous thromboembolism in spinal cord injury patients}, note={Submitted} } @article{Winkler88, author= {Winkler, H. Z. and Rainwater, L. M. and Myers, R. P. and Farrow, G. M. and Therneau, T. M. and Zincke, H. and Lieber, M. M.}, year= {1988}, title= {Stage {D}1 Prostatic Adenocarcinoma: {S}ignificance of nuclear {DNA} ploidy patterns studied by flow cytometry}, journal= {Mayo Clinic Proceedings}, volume={63}, pages= {103--112} } @article{Yau97, author= {Yau, K. K .W. and McGilchrist, C. A.}, year= {1997}, title= {Use of generalised linear mixed models for the analysis of clustered survival data}, journal= {Biometrical Journal}, volume={39}, pages= {3--11} } @article{Zhen94, author= {Zhen, B. and Murphy, J.R.}, year= {1994}, title= {Sample size determination for an exponential survival model with an unrestricted covariate}, journal=statmed, volume={13}, pages= {391--397} } @article{Aitkin80, author= {Aitkin,M. and Clayton, D.} , title= {The fitting of exponential, Weibull and extreme value distributions to complex censored survival data using GLIM}, year= {1980}, journal=applstat, volume={29}, pages={156--163} } @article{Bernstein78, author= {Berstein,D. and Lagakos, S. W.} , title= {Sample size and power determination for stratified clinical trials}, year= {1978}, journal=jscs, volume={8}, pages={65--73} } @article{Bie87, author= {Bie, O. and Borgan, O. and Liestoel, K.}, year = {1987}, title= {Confidence intervals and confidence bands for the cumulative hazard rate function and their small sample properties}, journal= scand, volume={14}, pages={221--233} } @book{Billingsley68, author={Billingsley, P.}, title= {Convergence of Probability Measures}, publisher={Wiley}, address={New York}, year= {1968} } @article{Block85, author= {Block, H. W. and Borges, W. S. and Savits, T. H.}, year= {1985}, title= {Age-dependent minimal repair}, journal=jap, volume={22}, pages={370--385} } @article{Breslow72, author= {Breslow, N. E.}, year= {1972}, title= {Discussion of {P}rofessor {C}ox's paper}, journal=jrssb, volume={34}, pages={216--217} } @book{Breslow80, author={Breslow, N. E. and Day, N. E.}, title= {The Analysis of Case-Control Studies}, volume={1}, series = {Statistical Methods in Cancer Research}, publisher={IARC}, address={Lyon}, year= {1980} } @book{glim2, author= {McCullagh, P. and Nelder, J.A.}, year= {1989}, title= {Generalized Linear Models, 2nd ed.}, publisher= {Chapman and Hall} } @incollection{Hettmansperger98, author= {Hettmansperger, T.}, year= {1998}, title= {Median}, editor={Armitage, P. and Colton, T.}, booktitle={Encyclopedia of Biostatics}, volume={4}, publisher= {Wiley}, address={New York}, pages={2525--2526} } @book{Huber81, author={Huber, P. J.}, title= {Robust Statistics}, publisher={Wiley}, address={New York}, year= {1981} } @article{Johansen83, author= {Johansen, S.}, year= {1983}, title= {An extension of {C}ox's regression model}, journal={Int. Stat. Review}, volume={51}, pages={165--174} } @book{Little87, author={Little, R.J.A. and Rubin, D.B.}, title= {Statistical Analysis with Missing Data}, publisher={John Wiley \& Sons}, address={New York}, year= {1987} } @incollection{Little98, author= {Little, R. J.}, year= {1998}, title= {Missing Data}, editor={Armitage, P. and Colton, T.}, booktitle={Encyclopedia of Biostatics}, volume={4}, publisher= {Wiley}, pages={2622--2635} } @article{Nelson69, author= {Nelson, W.}, year= {1969}, title= {Hazard plotting for incomplete failure data}, journal= {J. Quality Technology}, volume={1}, pages= {27--52} } @article{Peto72, author= {Peto, R.}, year= {1972}, title= {Discussion of {P}rofessor {C}ox's paper}, journal=jrssb, volume={34}, pages={205--207} } @article{Prentice78, author= {Prentice, R. L. and Gloeckler, L. A. } , title= {Regression analysis of grouped survival data with application to breast cancer data}, year= {1978}, journal=biom, volume={34}, pages={57--67} } @article{Self88, author= {Self, S. G. and Prentice, R. L.}, year= {1988}, title= {Asymptotic distribution theory and efficiency results for case-cohort studies}, journal=annals, volume={16}, pages= {64--81} } @techreport{Therneau94, author= {Therneau, T. M. and Sicks, J. and Bergstralh, E. and Offord, J.}, year = {1994}, title = {Expected survival based on hazard rates}, number = {52}, institution= {Department of Health Sciences Research, Mayo Clinic} } @article{Turnbull76, author= {Turnbull, B.W.}, year= {1976}, title= {The empirical distribution function with arbitrarily grouped, censored and truncated data}, journal={jrssb}, volume={38}, pages= {290--295} } @book{Breiman84, author= {Breiman, L. and Friedman, J. H. and Olshen, R. A. and Stone, C. J.}, title= {Classification and Regression Trees}, year = 1984, publisher={Wadsworth}, address={Belmont, CA} } @article{Breslow84, author= {Breslow, N. E. and L. Edler and J. Berger}, year= {1984}, title= {A two-sample censored-data rank test for acceleration}, journal=biom, volume={40}, pages= {1049--1062} } @book{Broca66, author= {Broca, P. P.}, title= {Traites de Tumerus, volumes 1 and 2}, year = 1866, publisher={Asselin}, address={Paris} } @article{Breslow74, author= {Breslow, N. E.}, year= {1974}, title= {Covariance analysis of censored survival data}, journal=biom, volume={30}, pages= {89--99} } @article{Bryson81, author= {Bryson, M. C. and Johnson, M. E}, year= {1981}, title= {The incidence of monotone likelihood in the {C}ox model}, journal={Technometrics}, volume={23}, pages= {381--383} } @article{Chang82, author= {Chang, I. M. and Gelman, R. and Pagano, M.}, year= {1982}, title= {Corrected group prognostic curves and summary statistics}, journal={J. Chronic Diseases}, volume={35}, pages= {669--674} } @inproceedings{Clarkson89, author= {Clarkson, D. B.}, year= {1989}, title= {Computing extended maximum likelihood estimates in monotone likelihood {C}ox proportional-hazards models}, booktitle={Computer Science and Statistics: Proceedings of the 21st Symposium on the Interface}, publisher={American Statistical Association}, address={Alexandria, Virginia}, pages={464--469} } @article{DeLong94, author= {D. M. DeLong and G. H. Guirguis and Y. C. So}, year= {1994}, title= {Efficient computation of subset selection probabilities with application to {C}ox regression}, journal=biok, volume={81}, pages= {607--611} } @book{Delwiche98, author={Delwiche, L. D. and S. J. Slaughter}, title= {The Little {SAS} Book}, publisher={SAS Institute}, address={Cary, NC}, year= {1998} } @article{Ducrocq96, author={V. Ducrocq and G. Casella}, title= {A {B}ayesian analysis of mixed survival models}, year= {1996}, journal={Genet. Sel. Evol.}, volume={28}, pages= {505--529} } @article{Guo92, author= {G. Guo and G. Rodr\'{\i}guez}, year= {1992}, title= {Estimating a multivariate proportional hazards model for clustered data using the {EM} algorithm, with an application to child survival in {G}uatemala}, journal=jasa, volume={87}, pages= {969--976} } @article{Henderson99, author= {R. Henderson and P. Oman}, year= {1999}, title= {Effect of frailty on marginal regression estimates in survival analysis}, journal=jrssb, volume={61}, pages= {367--379} } @article{Hougaard86, author= {Hougaard, P.}, year= {1986}, title= {Survival models for heterogeneous populations derived from stable distributions}, journal=biok, volume={73}, pages= {387--396} } @article{Huster89, author= {W. J. Huster and R. Brookmeyer and S. G. Self}, year= {1989}, title= {Modelling paired survival data with covariates}, journal=biom, volume={45}, pages= {145--156} } @techreport{Jaeckel72, author= {Jaeckel, L.}, year= {1972}, title= {The infinitesimal jackknife}, institution={Bell Laboratories}, type={Memorandum}, number={MM 72-1215-11} } @article{Kavanagh94, author= {Kavanagh, B. F. and Wallrichs, S. and Dewitz, M. and Berry, D. and Currier, B. and Ilstrup, D. and Coventry, M. B.}, year= {1994}, title ={Charnley low-friction arthroplasty of the hip. {T}wenty-year results with cement}, journal={J. Arthroplasty}, volume={9}, pages= {229--234} } @article{Klein92, author= {Klein, J. P.}, year= {1992}, title ={Semiparametric estimation of random effects using the {C}ox model based on the {EM} algorithm}, journal=biom, volume={48}, pages= {795--806} } @article{Kyle93, author= {R. A. Kyle}, year= {1993}, title= {``{B}enign'' monoclonal gammopathy --- after 20 to 35 years of follow-up}, journal={Mayo Clinic Proceedings}, volume={68}, pages= {26--36} } @article{Kyle97, author= {R. A. Kyle}, year= {1997}, title= {Moncolonal gammopathy of undetermined significance and solitary plasmacytoma. {I}mplications for progression to overt multiple myeloma}, journal={Hematology/Oncology Clinics N. Amer.}, volume={11}, pages= {71--87} } @article{Leblanc92, author = {LeBlanc, M. and Crowley, J.}, title = {Relative risk trees for censored survival data}, journal = {Biometrics}, year = {1992}, volume={48}, pages = {411-425} } @article{Lee83, author= {K. L. Lee and Harrell, Jr., F. E. and H. D. Tolley and R. A. Rosati}, year= {1983}, title= {A comparison of test statistics for assessing the effects of concomitant variables in survival analysis}, journal=biom, volume={39}, pages= {341--350} } @article{Loprinzi94, author= {Loprinzi, C. L. and Laurie, J. A. and Wieand, H. S. and Krook, J. E. and Novotny, P. J. and Kugler, J. W. and Bartel, J. and Law, M. and Bateman, M. and Klatt, N. E. and Dose, A. M. and Etzell, P. S. and Nelimark, R. A. and Mailliard, J. A. and Moertel, C. G.}, year= {1994}, title= {Prospective evaluation of prognostic variables from patient-completed questionnaires}, journal={J. Clinical Oncol.}, volume={12}, pages= {601--607} } @article{Mahe99, author= {C{\'{e}}dric Mah{\'{e}} and Sylvie Chevret}, year= {1999}, title= {Estimating regression parameters and degree of dependence for multivariate failure time data}, journal=biom, volume={55}, pages= {1078--1084} } @article{Makuch82, author= {Makuch, R. W.}, year= {1982}, title= {Adjusted survival curve estimation using covariates}, journal={J. Chronic Disease}, volume={35}, pages= {437--443} } @article{Markus89, author= {Markus, B. H. and Dickson, E. R. and Grambsch, P. M. and Fleming, T. R. and Mazzaferro, V. and Klintmalm, G. B .G. and Wiesner, R. H. and VanThiel, D. H. and Starzl, T. E.}, year= {1989}, title= {Efficiency of liver transplantation in patients with primary biliary cirrhosis}, journal=NEJM, volume={320}, pages= {1709--1713} } @article{Miller83, author= {Miller, Jr., R.G.}, year= {1983}, title= {What price {Kaplan--Meier}?}, journal=biom, volume={39}, pages= {1077--1081} } @article{Murphy81, author= {Murphy, V. K. and Haywood, L. J.}, year= {1981}, title= {Survival analysis by sex, age group and hemotype in sickle cell disease}, journal={J. Chronic Diseases}, volume={34}, pages= {313--319} } @techreport{Pugh92, author= {M. Pugh and J. Robbins and S. Lipsitz and D. Harrington}, year= {1992}, title= {Inference in the {C}ox proportional hazards model with missing covariates}, institution={Department of Biostatistics, Harvard School of Public Health}, address={Boston}, number={758Z} } @article{Ripatti00, author= {Ripatti, S. and Palmgren, J.}, year= {2000}, title= {Estimation of multivariate frailty models using penalized partial likelihood}, journal=biom, volume={56}, pages={1016--1022} } @book{Searle71, author = {Searle, S.R.}, year= {1971}, title = {Linear Models}, publisher={Wiley}, address={New York}} @booklet{Seer81, key = {Surveillance}, title= {Surveillance, Epidemiology, and End Results: Incidence and Mortality Data, 1973--77}, year = {1981}, howpublished= {National Cancer Institute Monograph 57, U.S. Department of Health and Human Services, Public Health Service}, address= {National Cancer Institute, Bethesda, MD}, note = {NIH Publication No. 81-2330}, } @article{Sellers95, author= {T. A Sellers and V. E. Anderson and J. D. Potter and S. A. Bartow and P. L. Chen and L. Everson and R. A. King and C. C. Kuni and L. H. Kushi and P. G. McGovern and S. S. Rich and J. F. Whitbeck and G. L. Wiesner}, year= {1995}, title= {Epidemiologic and genetic follow-up study of 544 Minnesota breast cancer families: {D}esign and methods}, journal={Genetic Epidemiology}, volume={12}, pages= {417--429} } @article{Silverstein99, author= {M. D. Silverstein and Loftus, Jr., E. V. and W. J. Sandborn and W. J. Tremaine and B. G. Feagan and P. J. Nietert and W. S. Harmsen and A. R. Zinsmeister}, year= {1999}, title= {Clinical course and costs of care for {C}rohn's disease: {M}arkov model analysis of a population-based cohort}, journal={Gastroenterology}, volume={117}, pages= {49--57} } @article{Tsiatis81, author= {A. A. Tsiatis}, year= {1981}, title= {A large sample study of {C}ox's regression model}, journal= annals, volume={9}, pages= {93--108} } @article{Verweij94, author= {P. J .M. Verweij and Van Houwlingen, H. C.}, year= {1994}, title= {Penalized likelihood in {C}ox regression}, journal= statmed, volume={13}, pages= {2427--2436} } @ARTICLE{Volinsky98bayesianinformation, author = {Chris Volinsky and Adrian E. Raftery}, title = {Bayesian Information Criterion for Censored Survival Models}, journal = {Biometrics}, year = {1998}, volume = {56}, pages = {256--262} } @article{Wei90, author= {L. J. Wei and Z. Ying and D. Y. Lin}, year= {1990}, title= {Linear regression analysis of censored survival data based on rank tests}, journal= biok, volume={77}, pages= {845--851} } @article{Yau97, author= {Yau, K. K .W. and McGilchrist, C. A.}, year= {1997}, title= {Use of generalised linear mixed models for the analysis of clustered survival data}, journal= {Biometrical Journal}, volume={39}, pages= {3--11} } @article{Yau98, author= {Yau, K. K .W. and McGilchrist, C. A.}, year= {1998}, title= {{ML} and {REML} estimation in survival analysis with time dependent correlated frailty}, journal= statmed, volume={17}, pages= {1201--1213} } @article{Zahl96, author= {Zahl, Per-Henrik}, title= {A linear non-parametric model for the excess intensity}, year= {1996}, journal=scand, volume={23}, pages={353--364} } @booklet{smoke90, key={Department of Health}, title={The Health Benefits of Smoking Cessation}, year={1990}, howpublished ={Department of Health and Human Services. Public Health Service, Centers for Disease Control, Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health}, note={DHHS Publication No (CDC)90-8416} } @booklet{lifeus40, title={United States Life Tables and Actuarial Tables, 1939--41}, author={Thomas N. E. Greville}, howpublished={Federal Security Agency, United States Public Health Service, National Office of Vital Statistics}, note={U.S. Government Printing Office, 1947} } @booklet{lifeus50, key={United States Lifetables 1950}, title={United States Life Tables for 1949--51}, howpublished={U.S. Department of Health, Education and Welfare, Public Health Service, National Office of Vital Statistics} } @booklet{lifeus60, key={United States Lifetables 1960}, title ={United States Lifetables 1959--61}, year = {1964}, howpublished= {Public Health Service Publication No. 1252}, note={Volume 1, Number 1} } @booklet{lifeus60b, key={Vital Statistics}, title ={Vital Statistics of the United States, 1960}, year = {1963}, howpublished= {U.S. Department of Health, Education, and Welfare, Public Health Service, National Center for Vital Statistics}, note={Volume 2A, Tabl3 3B} } @booklet{lifeus70, key={United States Lifetables 1970}, title ={U.S. Decennial Lifetables 1969--71}, year = {1975}, howpublished= {DHEW Publication No. HRA 75-115}, note={Volume 1, Number 1} } @booklet{lifeus80, key={United States Lifetables 1990}, title ={U.S. Decennial Lifetables 1979--81}, year = {1985}, howpublished= {DHEW Publication No. PHS 85-1150-1}, note={Volume 1, Number 1} } @booklet{lifest60, key = {United States Lifetables 1959--61}, title = {Life tables for the geographic divisions of the {U}nited {S}tates: 1959--61}, note = {Vol. 1, number 3}, howpublished={National Center for Health Statistics, Public Health Service, Washington, U.S. Government Printing Office}, month=May, year={1965} } @booklet{lifemn60, title ={Minnesota State Lifetables 1959--61}, year = {1965}, howpublished= {Public Health Service Publication No. 1252}, note={Volume 2, Number 24} } @booklet{lifemn70, title ={U.S. Decennial Lifetables 1969--71}, year = {1975}, howpublished= {DHEW Publication No. HRA 75-1151}, note={Volume 2, Number 24} } @booklet{lifefl70, title ={U.S. Decennial Lifetables 1969--71}, year = {1975}, howpublished= {DHEW Publication No. HRA 75-1151}, note={Volume 2, Number 10} } @booklet{lifeaz70, title ={U.S. Decennial Lifetables 1969--71}, year = {1975}, howpublished= {DHEW Publication No. HRA 75-1151}, note={Volume 2, Number 3} } @booklet{lifemn80, title ={U.S. Decennial Lifetables 1979--81}, year = {1985}, howpublished= {DHEW Publication No. PHS 86-1151-2451}, note={Volume 2, Number 24} } @book{Lifetable, author={National {C}enter for {H}ealth {S}tatistics}, year={1965}, title={Life tables for the geographic divisions of the {U}nited {S}tates: 1959-1961}, volume={1}, number={3}, publisher={US Government Printing Office, Washington} } survival/noweb/residuals.survreg.Rnw0000644000176200001440000003601613624031200017413 0ustar liggesusers\section{Accelerated Failure Time models} The [[surveg]] function fits parametric failure time models. This includes accerated failure time models, the Weibull, log-normal, and log-logistic models. It also fits as well as censored linear regression; with left censoring this is referred to in economics \emph{Tobit} regression. \subsection{Residuals} The residuals for a [[survreg]] model are one of several types \begin{description} \item[response] residual [[y]] value on the scale of the original data \item[deviance] an approximate deviance residual. A very bad idea statistically, retained for the sake of backwards compatability. \item[dfbeta] a matrix with one row per observation and one column per parameter showing the approximate influence of each observation on the final parameter value \item[dfbetas] the dfbeta residuals scaled by the standard error of each coefficient \item[working] residuals on the scale of the linear predictor \item[ldcase] likelihood displacement wrt case weights \item[ldresp] likelihood displacement wrt response changes \item[ldshape] likelihood displacement wrt changes in shape \item[matrix] matrix of derivatives of the log-likelihood wrt paramters \end{description} The other parameters are \begin{description} \item[rsigma] whether the scale parameters should be included in the result for dfbeta results. I can think of no reason why one would not want them --- unless of course the scale was fixed by the user, in which case there is no parameter. \item[collapse] optional vector of subject identifiers. This is for the case where a subject has multiple observations in a data set, and one wants to have residuals per subject rather than residuals per observation. \item[weighted] whether the residuals should be multiplied by the case weights. The sum of weighted residuals will be zero. \end{description} The routine starts with standard stuff, checking arguments for validity and etc. The two cases of response or working residuals require a lot less computation. and are the most common calls, so they are taken care of first. <>= # # Residuals for survreg objects residuals.survreg <- function(object, type=c('response', 'deviance', 'dfbeta', 'dfbetas', 'working', 'ldcase', 'ldresp', 'ldshape', 'matrix'), rsigma =TRUE, collapse=FALSE, weighted=FALSE, ...) { type <-match.arg(type) n <- length(object$linear.predictors) Terms <- object$terms if(!inherits(Terms, "terms")) stop("invalid terms component of object") # If the variance wasn't estimated then it has no error if (nrow(object$var) == length(object$coefficients)) rsigma <- FALSE # If there was a cluster directive in the model statment then remove # it. It does not correspond to a coefficient, and would just confuse # things later in the code. cluster <- untangle.specials(Terms,"cluster")$terms if (length(cluster) >0 ) Terms <- Terms[-cluster] strata <- attr(Terms, 'specials')$strata intercept <- attr(Terms, "intercept") response <- attr(Terms, "response") weights <- object$weights if (is.null(weights)) weighted <- FALSE <> <> <> <> } @ First retrieve the distribution, which is used multiple times. The common case is a character string pointing to some element of [[survreg.distributions]], but the other is a user supplied list of the form contained there. Some distributions are defined as the transform of another in which case we need to set [[itrans]] and [[dtrans]] and follow the link, otherwise the transformation and its inverse are the identity. <>= if (is.character(object$dist)) dd <- survreg.distributions[[object$dist]] else dd <- object$dist ytype <- attr(y, "type") if (is.null(dd$itrans)) { itrans <- dtrans <-function(x)x # reprise the work done in survreg to create a transformed y if (ytype=='left') y[,2] <- 2- y[,2] else if (type=='interval' && all(y[,3]<3)) y <- y[,c(1,3)] } else { itrans <- dd$itrans dtrans <- dd$dtrans # reprise the work done in survreg to create a transformed y tranfun <- dd$trans exactsurv <- y[,ncol(y)] ==1 if (any(exactsurv)) logcorrect <-sum(log(dd$dtrans(y[exactsurv,1]))) if (ytype=='interval') { if (any(y[,3]==3)) y <- cbind(tranfun(y[,1:2]), y[,3]) else y <- cbind(tranfun(y[,1]), y[,3]) } else if (ytype=='left') y <- cbind(tranfun(y[,1]), 2-y[,2]) else y <- cbind(tranfun(y[,1]), y[,2]) } if (!is.null(dd$dist)) dd <- survreg.distributions[[dd$dist]] deviance <- dd$deviance dens <- dd$density @ The next task is to decide what data we need. The response is always needed, but is normally saved as a part of the model. If it is a transformed distribution such as the Weibull (a transform of the extreme value) the saved object [[y]] is the transformed data, so we need to replicate that part of the survreg() code. (Why did I even allow for y=F in survreg? Because I was mimicing the lm function --- oh the long, long consequences of a design decision.) The covariate matrix [[x]] will be needed for all but response, deviance, and working residuals. If the model included a strata() term then there will be multiple scales, and the strata variable needs to be recovered. The variable [[sigma]] is set to a scalar if there are no strata, but otherwise to a vector with [[n]] elements containing the appropriate scale for each subject. The leverage type residuals all need the second derivative matrix. If there was a [[cluster]] statement in the model this will be found in [[naive.var]], otherwise in the [[var]] component. <>= if (is.null(object$naive.var)) vv <- object$var else vv <- object$naive.var need.x <- is.na(match(type, c('response', 'deviance', 'working'))) if (is.null(object$y) || !is.null(strata) || (need.x & is.null(object[['x']]))) mf <- stats::model.frame(object) if (is.null(object$y)) y <- model.response(mf) else y <- object$y if (!is.null(strata)) { temp <- untangle.specials(Terms, 'strata', 1) Terms2 <- Terms[-temp$terms] if (length(temp$vars)==1) strata.keep <- mf[[temp$vars]] else strata.keep <- strata(mf[,temp$vars], shortlabel=TRUE) strata <- as.numeric(strata.keep) nstrata <- max(strata) sigma <- object$scale[strata] } else { Terms2 <- Terms nstrata <- 1 sigma <- object$scale } if (need.x) { x <- object[['x']] #don't grab xlevels component if (is.null(x)) x <- model.matrix(Terms2, mf, contrasts.arg=object$contrasts) } @ The most common residual is type response, which requires almost no more work, for the others we need to create the matrix of derivatives before proceeding. We use the [[center]] component from the deviance function for the distribution, which returns the data point [[y]] itself for an exact, left, or right censored observation, and an appropriate midpoint for interval censored ones. <>= if (type=='response') { yhat0 <- deviance(y, sigma, object$parms) rr <- itrans(yhat0$center) - itrans(object$linear.predictor) } else { <> <> } @ The matrix of derviatives is used in all of the other cases. The starting point is the [[density]] function of the distribtion which return a matrix with columns of $F(x)$, $1-F(x)$, $f(x)$, $f'(x)/f(x)$ and $f''(x)/f(x)$. %' The matrix type residual contains columns for each of $$ L_i \quad \frac{\partial L_i}{\partial \eta_i} \quad \frac{\partial^2 L_i}{\partial \eta_i^2} \quad \frac{\partial L_i}{\partial \log(\sigma)} \quad \frac{\partial L_i}{\partial \log(\sigma)^2} \quad \frac{\partial^2 L_i}{\partial \eta \partial\log(\sigma)} $$ where $L_i$ is the contribution to the log-likelihood from each individual. Note that if there are multiple scales, i.e. a strata() term in the model, then terms 3--6 are the derivatives for that subject with respect to their \emph{particular} scale factor; derivatives with respect to all the other scales are zero for that subject. The log-likelihood can be written as \begin{align*} L &= \sum_{exact}\left[ \log(f(z_i)) -\log(\sigma_i) \right] + \sum_{censored} \log \left( \int_{z_i^l}^{z_i^u} f(u)du \right) \\ &\equiv \sum_{exact}\left[g_1(z_i) -\log(\sigma_i) \right] + \sum_{censored} \log(g_2(z_i^l, z_i^u)) \\ z_i &= (y_i - \eta_i)/ \sigma_i \end{align*} For the interval censored observations we have a $z$ defined at both the lower and upper endpoints. The linear predictor is $\eta = X\beta$. The derivatives are shown below. Note that $f(-\infty) = f(\infty) = F(-\infty)=0$, $F(\infty)=1$, $z^u = \infty$ for a right censored observation and $z^l = -\infty$ for a left censored one. \begin{align*} \frac{\partial g_1}{\partial \eta} &= - \frac{1}{\sigma} \left[\frac{f'(z)}{f(z)} \right] \\ %' \frac{\partial g_2}{\partial \eta} &= - \frac{1}{\sigma} \left[ \frac{f(z^u) - f(z^l)}{F(z^u) - F(z^l)} \right] \\ \frac{\partial^2 g_1}{\partial \eta^2} &= \frac{1}{\sigma^2} \left[ \frac{f''(z)}{f(z)} \right] - (\partial g_1 / \partial \eta)^2 \\ \frac{\partial^2 g_2}{\partial \eta^2} &= \frac{1}{\sigma^2} \left[ \frac{f'(z^u) - f'(z^l)}{F(z^u) - F(z^l)} \right] - (\partial g_2 / \partial \eta)^2 \\ \frac{\partial g_1}{\partial \log\sigma} && - \left[ \frac{zf'(z)}{f(z)} \right] \\ \frac{\partial g_2}{\partial \log\sigma} &= - \left[ \frac{z^uf(z^u) - z^lf(z^l)}{F(z^u) - F(z^l)} \right] \\ \frac{\partial^2 g_1}{\partial (\log\sigma)^2} &=& \left[ \frac{z^2 f''(z) + zf'(z)}{f(z)} \right] - (\partial g_1 / \partial \log\sigma)^2 \\ \frac{\partial^2 g_2}{\partial (\log\sigma)^2} &= \left[ \frac{(z^u)^2 f'(z^u) - (z^l)^2f'(z_l) } {F(z^u) - F(z^l)} \right] - \partial g_1 /\partial \log\sigma(1+\partial g_1 / \partial \log\sigma) \\ \frac{\partial^2 g_1}{\partial \eta \partial \log\sigma} &= \frac{zf''(z)}{\sigma f(z)} -\partial g_1/\partial \eta (1 + \partial g_1/\partial \log\sigma) \\ \frac{\partial^2 g_2}{\partial \eta \partial \log\sigma} &= \frac{z^uf'(z^u) - z^lf'(z^l)}{\sigma [F(z^u) - F(z^l)]} -\partial g_2/\partial \eta (1 + \partial g_2/\partial \log\sigma) \\ \end{align*} In the code [[z]] is the relevant point for exact, left, or right censored data, and [[z2]] the upper endpoint for an interval censored one. The variable [[tdenom]] contains the denominator for each subject (which is the same for all derivatives for that subject). For an interval censored observation we try to avoid numeric cancellation by using the appropriate tail of the distribution. For instance with $(z^l, z^u) = (12,15)$ the value of $F(x)$ will be very near 1 and it is better to subtract two upper tail values $(1-F)$ than two lower tail ones $F$. <>= status <- y[,ncol(y)] eta <- object$linear.predictors z <- (y[,1] - eta)/sigma dmat <- dens(z, object$parms) dtemp<- dmat[,3] * dmat[,4] #f' if (any(status==3)) { z2 <- (y[,2] - eta)/sigma dmat2 <- dens(z2, object$parms) } else { dmat2 <- dmat #dummy values z2 <- 0 } tdenom <- ((status==0) * dmat[,2]) + #right censored ((status==1) * 1 ) + #exact ((status==2) * dmat[,1]) + #left ((status==3) * ifelse(z>0, dmat[,2]-dmat2[,2], dmat2[,1] - dmat[,1])) #interval g <- log(ifelse(status==1, dmat[,3]/sigma, tdenom)) #loglik tdenom <- 1/tdenom dg <- -(tdenom/sigma) *(((status==0) * (0-dmat[,3])) + #dg/ eta ((status==1) * dmat[,4]) + ((status==2) * dmat[,3]) + ((status==3) * (dmat2[,3]- dmat[,3]))) ddg <- (tdenom/sigma^2) *(((status==0) * (0- dtemp)) + #ddg/eta^2 ((status==1) * dmat[,5]) + ((status==2) * dtemp) + ((status==3) * (dmat2[,3]*dmat2[,4] - dtemp))) ds <- ifelse(status<3, dg * sigma * z, tdenom*(z2*dmat2[,3] - z*dmat[,3])) dds <- ifelse(status<3, ddg* (sigma*z)^2, tdenom*(z2*z2*dmat2[,3]*dmat2[,4] - z * z*dmat[,3] * dmat[,4])) dsg <- ifelse(status<3, ddg* sigma*z, tdenom *(z2*dmat2[,3]*dmat2[,4] - z*dtemp)) deriv <- cbind(g, dg, ddg=ddg- dg^2, ds = ifelse(status==1, ds-1, ds), dds=dds - ds*(1+ds), dsg=dsg - dg*(1+ds)) @ Now, we can calcultate the actual residuals case by case. For the dfbetas there will be one column per coefficient, so if there are strata column 4 of the deriv matrix needs to be \emph{un}collapsed into a matrix with nstrata columns. The same manipulation is needed for the ld residuals. <>= if (type=='deviance') { yhat0 <- deviance(y, sigma, object$parms) rr <- (-1)*deriv[,2]/deriv[,3] #working residuals rr <- sign(rr)* sqrt(2*(yhat0$loglik - deriv[,1])) } else if (type=='working') rr <- (-1)*deriv[,2]/deriv[,3] else if (type=='dfbeta' || type== 'dfbetas' || type=='ldcase') { score <- deriv[,2] * x # score residuals if (rsigma) { if (nstrata > 1) { d4 <- matrix(0., nrow=n, ncol=nstrata) d4[cbind(1:n, strata)] <- deriv[,4] score <- cbind(score, d4) } else score <- cbind(score, deriv[,4]) } rr <- score %*% vv # cause column names to be retained # old: if (type=='dfbetas') rr[] <- rr %*% diag(1/sqrt(diag(vv))) if (type=='dfbetas') rr <- rr * rep(1/sqrt(diag(vv)), each=nrow(rr)) if (type=='ldcase') rr<- rowSums(rr*score) } else if (type=='ldresp') { rscore <- deriv[,3] * (x * sigma) if (rsigma) { if (nstrata >1) { d6 <- matrix(0., nrow=n, ncol=nstrata) d6[cbind(1:n, strata)] <- deriv[,6]*sigma rscore <- cbind(rscore, d6) } else rscore <- cbind(rscore, deriv[,6] * sigma) } temp <- rscore %*% vv rr <- rowSums(rscore * temp) } else if (type=='ldshape') { sscore <- deriv[,6] *x if (rsigma) { if (nstrata >1) { d5 <- matrix(0., nrow=n, ncol=nstrata) d5[cbind(1:n, strata)] <- deriv[,5] sscore <- cbind(sscore, d5) } else sscore <- cbind(sscore, deriv[,5]) } temp <- sscore %*% vv rr <- rowSums(sscore * temp) } else { #type = matrix rr <- deriv } @ Finally the two optional steps of adding case weights and collapsing over subject id. <>= #case weights if (weighted) rr <- rr * weights #Expand out the missing values in the result if (!is.null(object$na.action)) { rr <- naresid(object$na.action, rr) if (is.matrix(rr)) n <- nrow(rr) else n <- length(rr) } # Collapse if desired if (!missing(collapse)) { if (length(collapse) !=n) stop("Wrong length for 'collapse'") rr <- drop(rowsum(rr, collapse)) } rr @ survival/noweb/yates.Rnw0000644000176200001440000003206314033733462015065 0ustar liggesusers\section{Linear models and contrasts} The primary contrast function is \code{yates}. This function does both simple and population contrasts; the name is a nod to the ``Yates weighted means'' method, the first population contrast that I know of. A second reason for the name is that the word ``contrast'' is already overused in the S/R lexicon. Both \code{yates} and \code{cmatrix} can be used with any model that returns the necessary portions, e.g., lm, coxph, or glm. They were written because I became embroiled in the ``type III'' controversy, and made it a goal to figure out what exactly it is that SAS does. If I had known that that quest would take multiple years would perhaps have never started. Population contrasts can result in some head scratching. It is easy to create the predicted value for any hypothethical subject from a model. A population prediction holds some data values constant and lets the others range over a population, giving a mean predicted value or population average. Population predictions for two treatments are the familiar g-estimates of causal models. We can take sums or differences of these predictions as well, e.g. to ask if they are significantly different. What can't be done is to work backwards from one of these contrasts to the populations, at least for continuous variables. If someone asks for an x contrast of 15-5 is this a sum of two population estimates at 15 and -5, or a difference? It's always hard to guess the mind of a user. Therefore what is needed is a fitted model, the term (covariate) of interest, levels of that covariate, a desired comparison, and a population. First is cmatrix routine. This is called by users to create a contrast matrix for a model, users can also construct their own contrast matrices. The result has two parts: the definition of a set of predicted values and a set of contrasts between those values. The routine requires a fit and a formula. The formula is simply a way to get a set of variable names: all those variables are the fixed ones in the population contrast, and all others form the ``population''. The result will be a matrix or list that has a label attribute containing the name of the term; this is used in printouts in the obvious way. Suppose that our model was \code{coxph(Surv(time, status) ~ age*sex + ph.ecog)}. Someone might want the population matrix for age, sex, ph.ecog, or age+ sex. For the last it doesn't matter if they say age+sex, age*sex, or age:sex. <>= cmatrix <- function(fit, term, test =c("global", "trend", "pairwise", "mean"), levels, assign) { # Make sure that "fit" is present and isn't missing any parts. if (missing(fit)) stop("a fit argument is required") Terms <- try(terms(fit), silent=TRUE) if (inherits(Terms, "try-error")) stop("the fit does not have a terms structure") else Terms <- delete.response(Terms) # y is not needed Tatt <- attributes(Terms) # a flaw in delete.response: it doesn't subset dataClasses Tatt$dataClasses <- Tatt$dataClasses[row.names(Tatt$factors)] test <- match.arg(test) if (missing(term)) stop("a term argument is required") if (is.character(term)) term <- formula(paste("~", term)) else if (is.numeric(term)) { if (all(term == floor(term) & term >0 & term < length(Tatt$term.labels))) term <- formula(paste("~", paste(Tatt$term.labels[term], collapse='+'))) else stop("a numeric term must be an integer between 1 and max terms in the fit") } else if (!inherits(term, "formula")) stop("the term must be a formula or integer") fterm <- delete.response(terms(term)) fatt <- attributes(fterm) user.name <- fatt$term.labels # what the user called it termname <- all.vars(fatt$variables) indx <- match(termname, all.vars(Tatt$variables)) if (any(is.na(indx))) stop("variable ", termname[is.na(indx)], " not found in the formula") # What kind of term is being tested? It can be categorical, continuous, # an interaction of only categorical terms, interaction of only continuous # terms, or a mixed interaction. # Key is a trick to get "zed" from ns(zed, df= dfvar) key <- sapply(Tatt$variables[-1], function(x) all.vars(x)[1]) parts <- names(Tatt$dataClasses)[match(termname, key)] types <- Tatt$dataClasses[parts] iscat <- as.integer(types=="factor" | types=="character") if (length(iscat)==1) termtype <- iscat else termtype <- 2 + any(iscat) + all(iscat) # Were levels specified? If so we either simply accept them (continuous), # or double check them (categorical) if (missing(levels)) { temp <- fit$xlevels[match(parts, names(fit$xlevels), nomatch=0)] if (length(temp) < length(parts)) stop("continuous variables require the levels argument") levels <- do.call(expand.grid, c(temp, stringsAsFactors=FALSE)) } else { #user supplied if (is.list(levels)) { if (is.null(names(levels))) { if (length(termname)==1) names(levels)== termname else stop("levels list requires named elements") } } if (is.data.frame(levels) || is.list(levels)) { index1 <- match(termname, names(levels), nomatch=0) # Grab the cols from levels that are needed (we allow it to have # extra, unused columns) levels <- as.list(levels[index1]) # now, levels = the set of ones that the user supplied (which might # be none, if names were wrong) if (length(levels) < length(termname)) { # add on the ones we don't have, using fit$xlevels as defaults temp <- fit$xlevels[parts[index1==0]] if (length(temp) > 0) { names(temp) <- termname[index1 ==0] levels <- c(levels, temp) } } index2 <- match(termname, names(levels), nomatch=0) if (any(index2==0)) stop("levels information not found for: ", termname[index2==0]) levels <- expand.grid(levels[index2], stringsAsFactors=FALSE) if (any(duplicated(levels))) stop("levels data frame has duplicates") } else if (is.matrix(levels)) { if (ncol(levels) != length(parts)) stop("levels matrix has the wrong number of columns") if (!is.null(dimnames(levels)[[2]])) { index <- match(termname, dimnames(levels)[[2]], nomatch=0) if (index==0) stop("matrix column names do no match the variable list") else levels <- levels[,index, drop=FALSE] } else if (ncol(levels) > 1) stop("multicolumn levels matrix requires column names") if (any(duplicated(levels))) stop("levels matrix has duplicated rows") levels <- data.frame(levels, stringsAsFactors=FALSE) names(levels) <- termname } else if (length(parts) > 1) stop("levels should be a data frame or matrix") else { levels <- data.frame(x=unique(levels), stringsAsFactors=FALSE) names(levels) <- termname } } # check that any categorical levels are legal for (i in which(iscat==1)) { xlev <- fit$xlevels[[parts[i]]] if (is.null(xlev)) stop("xlevels attribute not found for", termname[i]) temp <- match(levels[[i]], xlev) if (any(is.na(temp))) stop("invalid level for term", termname[i]) } rval <- list(levels=levels, termname=termname) # Now add the contrast matrix between the levels, if needed if (test=="global") { <> } else if (test=="pairwise") { <> } else if (test=="mean") { <> } else { <> } # the user can say "age" when the model has "ns(age)", but we need # the more formal label going forward rval <- list(levels=levels, termname=parts, cmat=cmat, iscat=iscat) class(rval) <- "cmatrix" rval } @ The default contrast matrix is a simple test of equality if there is only one term. If the term is the interaction of multiple categorical variables then we do an anova type decomposition. In other cases we currently fail. <>= if (TRUE) { #if (length(parts) ==1) { cmat <- diag(nrow(levels)) cmat[, nrow(cmat)] <- -1 # all equal to the last cmat <- cmat[-nrow(cmat),, drop=FALSE] } else if (termtype== 4) { # anova type stop("not yet done 1") } else stop("not yet done 2") @ The \code{pairwise} option creates a set of contrast matrices for all pairs of a factor. <>= nlev <- nrow(levels) # this is the number of groups being compared if (nlev < 2) stop("pairwise tests need at least 2 groups") npair <- nlev*(nlev-1)/2 if (npair==1) cmat <- matrix(c(1, -1), nrow=1) else { cmat <- vector("list", npair) k <- 1 cname <- rep("", npair) for (i in 1:(nlev-1)) { temp <- double(nlev) temp[i] <- 1 for (j in (i+1):nlev) { temp[j] <- -1 cmat[[k]] <- matrix(temp, nrow=1) temp[j] <- 0 cname[k] <- paste(i, "vs", j) k <- k+1 } } names(cmat) <- cname } @ The mean option compares each to the overall mean. <>= ntest <- nrow(levels) cmat <- vector("list", ntest) for (k in 1:ntest) { temp <- rep(-1/ntest, ntest) temp[k] <- (ntest-1)/ntest cmat[[k]] <- matrix(temp, nrow=1) } names(cmat) <- paste(1:ntest, "vs mean") @ The \code{linear} option is of interest for terms that have more than one column; the two most common cases are a factor variable or a spline. It forms a pair of tests, one for the linear and one for the nonlinear part. For non-linear functions such as splines we need some notion of the range of the data, since we want to be linear over the entire range. <>= cmat <- vector("list", 2) cmat[[1]] <- matrix(1:ntest, 1, ntest) cmat[[2]] <- diag(ntest) attr(cmat, "nested") <- TRUE if (is.null(levels[[1]])) { # a continuous variable, and the user didn't give levels for the test # look up the call and use the knots tcall <- Tatt$predvars[[indx + 1]] # skip the 'call' if (tcall[[1]] == as.name("pspline")) { bb <- tcall[["Boundary.knots"]] levels[[1]] <- seq(bb[1], bb[2], length=ntest) } else if (tcall[[1]] %in% c("ns", "bs")) { bb <- c(tcall[["Boundary.knots"]], tcall[["knots"]]) levels[[1]] <- sort(bb) } else stop("don't know how to do a linear contrast for this term") } @ Here are some helper routines. Formulas are from chapter 5 of Searle. The sums of squares only makes sense within a linear model. <>= gsolve <- function(mat, y, eps=sqrt(.Machine$double.eps)) { # solve using a generalized inverse # this is very similar to the ginv function of MASS temp <- svd(mat, nv=0) dpos <- (temp$d > max(temp$d[1]*eps, 0)) dd <- ifelse(dpos, 1/temp$d, 0) # all the parentheses save a tiny bit of time if y is a vector if (all(dpos)) x <- drop(temp$u %*% (dd*(t(temp$u) %*% y))) else if (!any(dpos)) x <- drop(temp$y %*% (0*y)) # extremely rare else x <-drop(temp$u[,dpos] %*%(dd[dpos] * (t(temp$u[,dpos, drop=FALSE]) %*% y))) attr(x, "df") <- sum(dpos) x } qform <- function(var, beta) { # quadratic form b' (V-inverse) b temp <- gsolve(var, beta) list(test= sum(beta * temp), df=attr(temp, "df")) } @ The next functions do the work. Some bookkeeping is needed for a missing value in beta: we leave that coefficient out of the linear predictor. If there are missing coefs then the variance matrix will not have those columns in any case. The nafun function asks if a linear combination is NA. It treats 0*NA as 0. <>= estfun <- function(cmat, beta, varmat) { nabeta <- is.na(beta) if (any(nabeta)) { k <- which(!nabeta) #columns to keep estimate <- drop(cmat[,k] %*% beta[k]) # vector of predictions evar <- cmat[,k] %*% varmat %*% t(cmat[,k, drop=FALSE]) list(estimate = estimate, var=evar) } else { list(estimate = drop(cmat %*% beta), var = cmat %*% varmat %*% t(cmat)) } } testfun <- function(cmat, beta, varmat, sigma2) { nabeta <- is.na(beta) if (any(nabeta)) { k <- which(!nabeta) #columns to keep estimate <- drop(cmat[,k] %*% beta[k]) # vector of predictions temp <- qform(cmat[,k] %*% varmat %*% t(cmat[,k,drop=FALSE]), estimate) rval <- c(chisq=temp$test, df=temp$df) } else { estimate <- drop(cmat %*% beta) temp <- qform(cmat %*% varmat %*% t(cmat), estimate) rval <- c(chisq=temp$test, df=temp$df) } if (!is.null(sigma2)) rval <- c(rval, ss= unname(rval[1]) * sigma2) rval } nafun <- function(cmat, est) { used <- apply(cmat, 2, function(x) any(x != 0)) any(used & is.na(est)) } @ survival/noweb/coxph.Rnw0000644000176200001440000013703514110720327015056 0ustar liggesusers\section{Cox Models} \subsection{Coxph} The [[coxph]] routine is the underlying basis for all the models. The source was converted to noweb when adding time-transform terms. The call starts out with the basic building of a model frame and proceeds from there. The aeqSurv function is used to adjucate near ties in the time variable, numerical precision issues that occur when users base caculations on days/365.25 instead of days. A cluster term in the model is an exception. The variable mentioned is never part of the formal model, and so it is not kept as part of the saved terms structure. The analysis for multi-state data is a bit more complex. \begin{itemize} \item If the formula statement is a list, we preprocess this to find out any potential extra variables, and create a new global formula which will be used to create the data frame. \item In the above case missing value processing needs to be deferred, since some covariates may apply only to select transitions. \item After the data frame is constructed, the transitions matrix can be used to check that all the state names actually exist, construct the cmap matrix, and do missing value removal. \end{itemize} <>= #tt <- function(x) x coxph <- function(formula, data, weights, subset, na.action, init, control, ties= c("efron", "breslow", "exact"), singular.ok =TRUE, robust, model=FALSE, x=FALSE, y=TRUE, tt, method=ties, id, cluster, istate, statedata, nocenter=c(-1, 0, 1), ...) { ties <- match.arg(ties) Call <- match.call() ## We want to pass any ... args to coxph.control, but not pass things ## like "dats=mydata" where someone just made a typo. The use of ... ## is simply to allow things like "eps=1e6" with easier typing extraArgs <- list(...) if (length(extraArgs)) { controlargs <- names(formals(coxph.control)) #legal arg names indx <- pmatch(names(extraArgs), controlargs, nomatch=0L) if (any(indx==0L)) stop(gettextf("Argument %s not matched", names(extraArgs)[indx==0L]), domain = NA) } if (missing(control)) control <- coxph.control(...) # Move any cluster() term out of the formula, and make it an argument # instead. This makes everything easier. But, I can only do that with # a local copy, doing otherwise messes up future use of update() on # the model object for a user stuck in "+ cluster()" mode. if (missing(formula)) stop("a formula argument is required") ss <- "cluster" if (is.list(formula)) Terms <- if (missing(data)) terms(formula[[1]], specials=ss) else terms(formula[[1]], specials=ss, data=data) else Terms <- if (missing(data)) terms(formula, specials=ss) else terms(formula, specials=ss, data=data) tcl <- attr(Terms, 'specials')$cluster if (length(tcl) > 1) stop("a formula cannot have multiple cluster terms") if (length(tcl) > 0) { # there is one factors <- attr(Terms, 'factors') if (any(factors[tcl,] >1)) stop("cluster() cannot be in an interaction") if (attr(Terms, "response") ==0) stop("formula must have a Surv response") if (is.null(Call$cluster)) Call$cluster <- attr(Terms, "variables")[[1+tcl]][[2]] else warning("cluster appears both in a formula and as an argument, formula term ignored") # [.terms is broken at least through R 4.1; use our # local drop.special() function instead. Terms <- drop.special(Terms, tcl) formula <- Call$formula <- formula(Terms) } # create a call to model.frame() that contains the formula (required) # and any other of the relevant optional arguments # but don't evaluate it just yet indx <- match(c("formula", "data", "weights", "subset", "na.action", "cluster", "id", "istate"), names(Call), nomatch=0) if (indx[1] ==0) stop("A formula argument is required") tform <- Call[c(1,indx)] # only keep the arguments we wanted tform[[1L]] <- quote(stats::model.frame) # change the function called # if the formula is a list, do the first level of processing on it. if (is.list(formula)) { <> } else { multiform <- FALSE # formula is not a list of expressions covlist <- NULL dformula <- formula } # add specials to the formula special <- c("strata", "tt", "frailty", "ridge", "pspline") tform$formula <- if(missing(data)) terms(formula, special) else terms(formula, special, data=data) # Make "tt" visible for coxph formulas, without making it visible elsewhere if (!is.null(attr(tform$formula, "specials")$tt)) { coxenv <- new.env(parent= environment(formula)) assign("tt", function(x) x, envir=coxenv) environment(tform$formula) <- coxenv } # okay, now evaluate the formula mf <- eval(tform, parent.frame()) Terms <- terms(mf) # Grab the response variable, and deal with Surv2 objects n <- nrow(mf) Y <- model.response(mf) isSurv2 <- inherits(Y, "Surv2") if (isSurv2) { # this is Surv2 style data # if there were any obs removed due to missing, remake the model frame if (length(attr(mf, "na.action"))) { tform$na.action <- na.pass mf <- eval.parent(tform) } if (!is.null(attr(Terms, "specials")$cluster)) stop("cluster() cannot appear in the model statement") new <- surv2data(mf) mf <- new$mf istate <- new$istate id <- new$id Y <- new$y n <- nrow(mf) } else { if (!is.Surv(Y)) stop("Response must be a survival object") id <- model.extract(mf, "id") istate <- model.extract(mf, "istate") } if (n==0) stop("No (non-missing) observations") type <- attr(Y, "type") multi <- FALSE if (type=="mright" || type == "mcounting") multi <- TRUE else if (type!='right' && type!='counting') stop(paste("Cox model doesn't support \"", type, "\" survival data", sep='')) data.n <- nrow(Y) #remember this before any time transforms if (!multi && multiform) stop("formula is a list but the response is not multi-state") if (multi && length(attr(Terms, "specials")$frailty) >0) stop("multi-state models do not currently support frailty terms") if (multi && length(attr(Terms, "specials")$pspline) >0) stop("multi-state models do not currently support pspline terms") if (multi && length(attr(Terms, "specials")$ridge) >0) stop("multi-state models do not currently support ridge penalties") if (control$timefix) Y <- aeqSurv(Y) <> # The time transform will expand the data frame mf. To do this # it needs Y and the strata. Everything else (cluster, offset, weights) # should be extracted after the transform # strats <- attr(Terms, "specials")$strata hasinteractions <- FALSE dropterms <- NULL if (length(strats)) { stemp <- untangle.specials(Terms, 'strata', 1) if (length(stemp$vars)==1) strata.keep <- mf[[stemp$vars]] else strata.keep <- strata(mf[,stemp$vars], shortlabel=TRUE) istrat <- as.integer(strata.keep) for (i in stemp$vars) { #multiple strata terms are allowed # The factors attr has one row for each variable in the frame, one # col for each term in the model. Pick rows for each strata # var, and find if it participates in any interactions. if (any(attr(Terms, 'order')[attr(Terms, "factors")[i,] >0] >1)) hasinteractions <- TRUE } if (!hasinteractions) dropterms <- stemp$terms } else istrat <- NULL if (hasinteractions && multi) stop("multi-state coxph does not support strata*covariate interactions") timetrans <- attr(Terms, "specials")$tt if (missing(tt)) tt <- NULL if (length(timetrans)) { if (multi || isSurv2) stop("the tt() transform is not implemented for multi-state or Surv2 models") <> } xlevels <- .getXlevels(Terms, mf) # grab the cluster, if present. Using cluster() in a formula is no # longer encouraged cluster <- model.extract(mf, "cluster") weights <- model.weights(mf) # The user can call with cluster, id, robust, or any combination # Default for robust: if cluster or any id with > 1 event or # any weights that are not 0 or 1, then TRUE # If only id, treat it as the cluster too has.cluster <- !(missing(cluster) || length(cluster)==0) has.id <- !(missing(id) || length(id)==0) has.rwt<- (!is.null(weights) && any(weights != floor(weights))) #has.rwt<- FALSE # we are rethinking this has.robust <- (!missing(robust) && !is.null(robust)) # arg present if (has.id) id <- as.factor(id) if (missing(robust) || is.null(robust)) { if (has.cluster || has.rwt || (has.id && (multi || anyDuplicated(id[Y[,ncol(Y)]==1])))) robust <- TRUE else robust <- FALSE } if (!is.logical(robust)) stop("robust must be TRUE/FALSE") if (has.cluster) { if (!robust) { warning("cluster specified with robust=FALSE, cluster ignored") ncluster <- 0 clname <- NULL } else { if (is.factor(cluster)) { clname <- levels(cluster) cluster <- as.integer(cluster) } else { clname <- sort(unique(cluster)) cluster <- match(cluster, clname) } ncluster <- length(clname) } } else { if (robust && has.id) { # treat the id as both identifier and clustering clname <- levels(id) cluster <- as.integer(id) ncluster <- length(clname) } else { ncluster <- 0 # has neither } } # if the user said "robust", (time1,time2) data, and no cluster or # id, complain about it if (robust && is.null(cluster)) { if (ncol(Y) ==2 || !has.robust) cluster <- seq.int(1, nrow(mf)) else stop("one of cluster or id is needed") } contrast.arg <- NULL #due to shared code with model.matrix.coxph attr(Terms, "intercept") <- 1 # always have a baseline hazard if (multi) { <> } <> <> if (multi) { <> } # infinite covariates are not screened out by the na.omit routines # But this needs to be done after the multi-X part if (!all(is.finite(X))) stop("data contains an infinite predictor") # init is checked after the final X matrix has been made if (missing(init)) init <- NULL else { if (length(init) != ncol(X)) stop("wrong length for init argument") temp <- X %*% init - sum(colMeans(X) * init) + offset # it's okay to have a few underflows, but if all of them are too # small we get all zeros if (any(exp(temp) > .Machine$double.xmax) || all(exp(temp)==0)) stop("initial values lead to overflow or underflow of the exp function") } <> <> <> } @ Multi-state models have a multi-state response, optionally they have a formula that is a list. If the formula is a list then the first element is the default formula with a survival response and covariates on the right. Further elements are of the form from/to ~ covariates / options and specify other covariates for all from:to transitions. Steps in processing such a formula are \begin{enumerate} \item Gather all the variables that appear on a right-hand side, and create a master formula y ~ all of them. This is used to create the model.frame. We also need to defer missing value processing, since some covariates might appear for only some transitions. \item Get the data. The response, id, and statedata variables can now be checked for consistency with the formulas. \item After X has been formed, expand it. \end{enumerate} Here is code for the first step. <>= multiform <- TRUE dformula <- formula[[1]] # the default formula for transitions if (missing(statedata)) covlist <- parsecovar1(formula[-1]) else { if (!inherits(statedata, "data.frame")) stop("statedata must be a data frame") if (is.null(statedata$state)) stop("statedata data frame must contain a 'state' variable") covlist <- parsecovar1(formula[-1], names(statedata)) } # create the master formula, used for model.frame # the term.labels + reformulate + environment trio is used in [.terms; # if it's good enough for base R it's good enough for me tlab <- unlist(lapply(covlist$rhs, function(x) attr(terms.formula(x$formula), "term.labels"))) tlab <- c(attr(terms.formula(dformula), "term.labels"), tlab) newform <- reformulate(tlab, dformula[[2]]) environment(newform) <- environment(dformula) formula <- newform tform$na.action <- na.pass # defer any missing value work to later @ <>= # check for consistency of the states, and create a transition # matrix if (length(id)==0) stop("an id statement is required for multi-state models") mcheck <- survcheck2(Y, id, istate) # error messages here if (mcheck$flag["overlap"] > 0) stop("data set has overlapping intervals for one or more subjects") transitions <- mcheck$transitions istate <- mcheck$istate states <- mcheck$states # build tmap, which has one row per term, one column per transition if (missing(statedata)) covlist2 <- parsecovar2(covlist, NULL, dformula= dformula, Terms, transitions, states) else covlist2 <- parsecovar2(covlist, statedata, dformula= dformula, Terms, transitions, states) tmap <- covlist2$tmap if (!is.null(covlist)) { <> } @ For multi-state models we can't tell what observations should be removed until any extra formulas have been processed. There may be rows that are missing \emph{some} of the covariates but are okay for \emph{some} transitions. Others could be useless. Those rows can be removed from the model frame before creating the X matrix. Also identify partially used rows, ones where the necessary covariates are present for some of the possible transitions but not all. Those obs are dealt with later by the stacker function. <>= # first vector will be true if there is at least 1 transition for which all # covariates are present, second if there is at least 1 for which some are not good.tran <- bad.tran <- rep(FALSE, nrow(Y)) # We don't need to check interaction terms termname <- rownames(attr(Terms, 'factors')) trow <- (!is.na(match(rownames(tmap), termname))) # create a missing indicator for each term termiss <- matrix(0L, nrow(mf), ncol(mf)) for (i in 1:ncol(mf)) { xx <- is.na(mf[[i]]) if (is.matrix(xx)) termiss[,i] <- apply(xx, 1, any) else termiss[,i] <- xx } for (i in levels(istate)) { rindex <- which(istate ==i) j <- which(covlist2$mapid[,1] == match(i, states)) #possible transitions for (jcol in j) { k <- which(trow & tmap[,jcol] > 0) # the terms involved in that bad.tran[rindex] <- (bad.tran[rindex] | apply(termiss[rindex, k, drop=FALSE], 1, any)) good.tran[rindex] <- (good.tran[rindex] | apply(!termiss[rindex, k, drop=FALSE], 1, all)) } } n.partially.used <- sum(good.tran & bad.tran & !is.na(Y)) omit <- (!good.tran & bad.tran) | is.na(Y) if (all(omit)) stop("all observations deleted due to missing values") temp <- setNames(seq(omit)[omit], attr(mf, "row.names")[omit]) attr(temp, "class") <- "omit" mf <- mf[!omit,, drop=FALSE] attr(mf, "na.action") <- temp Y <- Y[!omit] id <- id[!omit] if (length(istate)) istate <- istate[!omit] # istate can be NULL @ For a multi-state model, create the expanded X matrix. Sometimes it is much expanded. The first step is to create the cmap matrix from tmap by expanding terms; factors turn into multiple columns for instance. If tmap has rows (terms) for strata, then we have to deal with the complication that a strata might be applied to some transitions and not to others. <>= if (length(strats) >0) { stratum_map <- tmap[c(1L, strats),] # strats includes Y, + tmap has an extra row stratum_map[-1,] <- ifelse(stratum_map[-1,] >0, 1L, 0L) if (nrow(stratum_map) > 2) { temp <- stratum_map[-1,] if (!all(apply(temp, 2, function(x) all(x==0) || all(x==1)))) { # the hard case: some transitions use one strata variable, some # transitions use another. We need to keep them separate strata.keep <- mf[,strats] # this will be a data frame istrat <- sapply(strata.keep, as.numeric) } } } else stratum_map <- tmap[1,,drop=FALSE] @ Also create the initial values vector. The stacker function will create a separate block of observations for every unique value in \code{stratum\_map}. Now say that two transitions A:B and A:C share the same baseline hazard. Then either a B or a C outcome will be an ``event'' in that stratum; they would only be distinguished by perhaps having different covariates. The first thing we do with the result is to rebuild the transitions matrix: the working version was created before removing missings and can seriously overstate the number of transitions available. Then set up the data. <>= cmap <- parsecovar3(tmap, colnames(X), attr(X, "assign"), covlist2$phbaseline) xstack <- stacker(cmap, stratum_map, as.integer(istate), X, Y, strata=istrat, states=states) rkeep <- unique(xstack$rindex) transitions <- survcheck2(Y[rkeep,], id[rkeep], istate[rkeep])$transitions X <- xstack$X Y <- xstack$Y istrat <- xstack$strata if (length(offset)) offset <- offset[xstack$rindex] if (length(weights)) weights <- weights[xstack$rindex] if (length(cluster)) cluster <- cluster[xstack$rindex] @ The next step for multi X is to remake the assign attribute. It is a list with one element per term, and needs to be expanded in the same way as \code{tmap}, which has one row per term (+ an intercept row). For \code{predict, type='terms'} to work, no label can be repeated in the final assign object. If a variable `fred' were common across all the states we would want to use that as the label, but if it appears twice, as separate terms for two different transitions, then we label it as fred\_x:y where x:y is the transition. <>= t2 <- tmap[-c(1, strats),,drop=FALSE] # remove the intercept row and strata rows r2 <- row(t2)[!duplicated(as.vector(t2)) & t2 !=0] c2 <- col(t2)[!duplicated(as.vector(t2)) & t2 !=0] a2 <- lapply(seq(along.with=r2), function(i) {cmap[assign[[r2[i]]], c2[i]]}) # which elements are unique? tab <- table(r2) count <- tab[r2] names(a2) <- ifelse(count==1, row.names(t2)[r2], paste(row.names(t2)[r2], colnames(cmap)[c2], sep="_")) assign <- a2 @ An increasingly common error is for users to put the time variable on both sides of the formula, in the mistaken idea that this will deal with a failure of proportional hazards. Add a test for such models, but don't bail out. There will be cases where someone has the the stop variable in an expression on the right hand side, to create current age say. The \code{variables} attribute of the Terms object is the expression form of a list that contains the response variable followed by the predictors. Subscripting this, element 1 is the call to ``list'' itself so we always retain it. My \code{terms.inner} function works only with formula objects. <>= if (length(attr(Terms, 'variables')) > 2) { # a ~1 formula has length 2 ytemp <- terms.inner(formula[1:2]) suppressWarnings(z <- as.numeric(ytemp)) # are any of the elements numeric? ytemp <- ytemp[is.na(z)] # toss numerics, e.g. Surv(t, 1-s) xtemp <- terms.inner(formula[-2]) if (any(!is.na(match(xtemp, ytemp)))) warning("a variable appears on both the left and right sides of the formula") } @ At this point we deal with any time transforms. The model frame is expanded to a ``fake'' data set that has a separate stratum for each unique event-time/strata combination, and any tt() terms in the formula are processed. The first step is to create the index vector [[tindex]] and new strata [[.strata.]]. This last is included in a model.frame call (for others to use), internally the code simply replaces the \code{istrat} variable. A (modestly) fast C-routine first counts up and indexes the observations. We start out with error checks; since the computation can be slow we want to complain early. <>= timetrans <- untangle.specials(Terms, 'tt') ntrans <- length(timetrans$terms) if (is.null(tt)) { tt <- function(x, time, riskset, weights){ #default to O'Brien's logit rank obrien <- function(x) { r <- rank(x) (r-.5)/(.5+length(r)-r) } unlist(tapply(x, riskset, obrien)) } } if (is.function(tt)) tt <- list(tt) #single function becomes a list if (is.list(tt)) { if (any(!sapply(tt, is.function))) stop("The tt argument must contain function or list of functions") if (length(tt) != ntrans) { if (length(tt) ==1) { temp <- vector("list", ntrans) for (i in 1:ntrans) temp[[i]] <- tt[[1]] tt <- temp } else stop("Wrong length for tt argument") } } else stop("The tt argument must contain a function or list of functions") if (ncol(Y)==2) { if (length(strats)==0) { sorted <- order(-Y[,1], Y[,2]) newstrat <- rep.int(0L, nrow(Y)) newstrat[1] <- 1L } else { sorted <- order(istrat, -Y[,1], Y[,2]) #newstrat marks the first obs of each strata newstrat <- as.integer(c(1, 1*(diff(istrat[sorted])!=0))) } if (storage.mode(Y) != "double") storage.mode(Y) <- "double" counts <- .Call(Ccoxcount1, Y[sorted,], as.integer(newstrat)) tindex <- sorted[counts$index] } else { if (length(strats)==0) { sort.end <- order(-Y[,2], Y[,3]) sort.start<- order(-Y[,1]) newstrat <- c(1L, rep(0, nrow(Y) -1)) } else { sort.end <- order(istrat, -Y[,2], Y[,3]) sort.start<- order(istrat, -Y[,1]) newstrat <- c(1L, as.integer(diff(istrat[sort.end])!=0)) } if (storage.mode(Y) != "double") storage.mode(Y) <- "double" counts <- .Call(Ccoxcount2, Y, as.integer(sort.start -1L), as.integer(sort.end -1L), as.integer(newstrat)) tindex <- counts$index } @ The C routine has returned a list with 4 elements \begin{description} \item[nrisk] a vector containing the number at risk at each event time \item[time] the vector of event times \item[status] a vector of status values \item[index] a vector containing the set of subjects at risk for event time 1, followed by those at risk at event time 2, those at risk at event time 3, etc. \end{description} The new data frame is then a simple creation. The subtle part below is a desire to retain transformation information so that a downstream call to \code{termplot} will work. The tt function supplied by the user often finishes with a call to \code{pspline} or \code{ns}. If the returned value of the \code{tt} call has a class for which a \code{makepredictcall} method exists then we need to do 2 things: \begin{enumerate} \item Construct a fake call, e.g., ``pspline(age)'', then feed it and the result of tt as arguments to \code{makepredictcall} \item Replace that componenent in the predvars attribute of the terms. \end{enumerate} The \code{timetrans\$terms} value is a count of the right hand side of the formula. Some objects in the terms structure are unevaluated calls that include y, this adds 2 to the count (the call to ``list'' and the response). <>= Y <- Surv(rep(counts$time, counts$nrisk), counts$status) type <- 'right' # new Y is right censored, even if the old was (start, stop] mf <- mf[tindex,] istrat <- rep(1:length(counts$nrisk), counts$nrisk) weights <- model.weights(mf) if (!is.null(weights) && any(!is.finite(weights))) stop("weights must be finite") tcall <- attr(Terms, 'variables')[timetrans$terms+2] pvars <- attr(Terms, 'predvars') pmethod <- sub("makepredictcall.", "", as.vector(methods("makepredictcall"))) for (i in 1:ntrans) { newtt <- (tt[[i]])(mf[[timetrans$var[i]]], Y[,1], istrat, weights) mf[[timetrans$var[i]]] <- newtt nclass <- class(newtt) if (any(nclass %in% pmethod)) { # It has a makepredictcall method dummy <- as.call(list(as.name(class(newtt)[1]), tcall[[i]][[2]])) ptemp <- makepredictcall(newtt, dummy) pvars[[timetrans$terms[i]+2]] <- ptemp } } attr(Terms, "predvars") <- pvars @ This is the C code for time-transformation. For the first case it expects y to contain time and status sorted from longest time to shortest, and strata=1 for the first observation of each strata. <>= #include "survS.h" /* ** Count up risk sets and identify who is in each */ SEXP coxcount1(SEXP y2, SEXP strat2) { int ntime, nrow; int i, j, n; int stratastart=0; /* start row for this strata */ int nrisk=0; /* number at risk (=0 to stop -Wall complaint)*/ double *time, *status; int *strata; double dtime; SEXP rlist, rlistnames, rtime, rn, rindex, rstatus; int *rrindex, *rrstatus; n = nrows(y2); time = REAL(y2); status = time +n; strata = INTEGER(strat2); /* ** First pass: count the total number of death times (risk sets) ** and the total number of rows in the new data set. */ ntime=0; nrow=0; for (i=0; i> /* ** Pass 2, fill them in */ ntime=0; for (i=0; i> } @ The start-stop case is a bit more work. The set of subjects still at risk is an arbitrary set so we have to keep an index vector [[atrisk]]. At each new death time we write out the set of those at risk, with the deaths last. I toyed with the idea of a binary tree then realized it was not useful: at each death we need to list out all the subjects at risk into the index vector which is an $O(n)$ process, tree or not. <>= #include "survS.h" /* count up risk sets and identify who is in each, (start,stop] version */ SEXP coxcount2(SEXP y2, SEXP isort1, SEXP isort2, SEXP strat2) { int ntime, nrow; int i, j, istart, n; int nrisk=0, *atrisk; double *time1, *time2, *status; int *strata; double dtime; int iptr, jptr; SEXP rlist, rlistnames, rtime, rn, rindex, rstatus; int *rrindex, *rrstatus; int *sort1, *sort2; n = nrows(y2); time1 = REAL(y2); time2 = time1+n; status = time2 +n; strata = INTEGER(strat2); sort1 = INTEGER(isort1); sort2 = INTEGER(isort2); /* ** First pass: count the total number of death times (risk sets) ** and the total number of rows in the new data set */ ntime=0; nrow=0; istart =0; /* walks along the sort1 vector (start times) */ for (i=0; i= dtime; istart++) nrisk--; for(j= i+1; j> atrisk = (int *)R_alloc(n, sizeof(int)); /* marks who is at risk */ /* ** Pass 2, fill them in */ ntime=0; nrisk=0; j=0; /* pointer to time1 */; istart=0; for (i=0; i=dtime; istart++) { atrisk[sort1[istart]]=0; nrisk--; } for (j=1; j> } @ <>= /* ** Allocate memory */ PROTECT(rtime = allocVector(REALSXP, ntime)); PROTECT(rn = allocVector(INTSXP, ntime)); PROTECT(rindex=allocVector(INTSXP, nrow)); PROTECT(rstatus=allocVector(INTSXP,nrow)); rrindex = INTEGER(rindex); rrstatus= INTEGER(rstatus); @ <>= /* return the list */ PROTECT(rlist = allocVector(VECSXP, 4)); SET_VECTOR_ELT(rlist, 0, rn); SET_VECTOR_ELT(rlist, 1, rtime); SET_VECTOR_ELT(rlist, 2, rindex); SET_VECTOR_ELT(rlist, 3, rstatus); PROTECT(rlistnames = allocVector(STRSXP, 4)); SET_STRING_ELT(rlistnames, 0, mkChar("nrisk")); SET_STRING_ELT(rlistnames, 1, mkChar("time")); SET_STRING_ELT(rlistnames, 2, mkChar("index")); SET_STRING_ELT(rlistnames, 3, mkChar("status")); setAttrib(rlist, R_NamesSymbol, rlistnames); unprotect(6); return(rlist); @ We now return to the original thread of the program, though perhaps with new data, and build the $X$ matrix. Creation of the $X$ matrix for a Cox model requires just a bit of trickery. The baseline hazard for a Cox model plays the role of an intercept, but does not appear in the $X$ matrix. However, to create the columns of $X$ for factor variables correctly, we need to call the model.matrix routine in such a way that it \emph{thinks} there is an intercept, and so we set the intercept attribute to 1 in the terms object before calling model.matrix, ignoring any -1 term the user may have added. One simple way to handle all this is to call model.matrix on the original formula and then remove the terms we don't need. However, \begin{enumerate} \item The cluster() term, if any, could lead to thousands of extraneous ``intercept'' columns which are never needed. \item Likewise, nested case-control models can have thousands of strata, again leading many intercepts we never need. They never have strata by covariate interactions, however. \item If there are strata by covariate interactions in the model, the dummy intercepts-per-strata columns are necessary information for the model.matrix routine to correctly compute other columns of $X$. \end{enumerate} On later reflection \code{cluster} should never have been in the model statement in the first place, something that became painfully apparent with addition of multi-state models. In the future we will discourage it. For reason 2 above the usual plan is to also remove strata terms from the ``Terms'' object \emph{before} calling model.matrix, unless there are strata by covariate interactions in which case we remove them after. If anything is pre-dropped, for documentation purposes we want the returned assign attribute to match the Terms structure that we will hand back. (Do we ever use it?) In particular, the numbers therein correspond to the column names in \code{attr(Terms, 'factors')} The requires a shift. The cluster and strata terms are seen as main effects, so appear early in that list. We have found a case where terms get relabeled: <>= t1 <- terms( ~(x1 + x2):x3 + strata(x4)) t2 <- terms( ~(x1 + x2):x3) t3 <- t1[-1] colnames(attr(t1, "factors")) colnames(attr(t2, "factors")) colnames(attr(t3, "factors")) @ In t1 the strata term appears first, as it is the only thing that looks like a main effect, and the column labels are strata(x4), x1:x3, x2:x3. In t3 the column labels are x1:x3 and x3:x2 --- note left-right swap of the second. This means that using match() on the labels is not a reliable approach. We instead assume that nothing is reordered and do a shift. <>= if (length(dropterms)) { Terms2 <- Terms[-dropterms] X <- model.matrix(Terms2, mf, constrasts.arg=contrast.arg) # we want to number the terms wrt the original model matrix temp <- attr(X, "assign") shift <- sort(dropterms) for (i in seq(along.with=shift)) temp <- temp + 1*(shift[i] <= temp) attr(X, "assign") <- temp } else X <- model.matrix(Terms, mf, contrasts.arg=contrast.arg) # drop the intercept after the fact, and also drop strata if necessary Xatt <- attributes(X) if (hasinteractions) adrop <- c(0, untangle.specials(Terms, "strata")$terms) else adrop <- 0 xdrop <- Xatt$assign %in% adrop #columns to drop (always the intercept) X <- X[, !xdrop, drop=FALSE] attr(X, "assign") <- Xatt$assign[!xdrop] attr(X, "contrasts") <- Xatt$contrasts @ Finish the setup. If someone includes an init statement or offset, make sure that it does not lead to instant code failure due to overflow/underflow. <>= offset <- model.offset(mf) if (is.null(offset) | all(offset==0)) offset <- rep(0., nrow(mf)) else if (any(!is.finite(offset) | !is.finite(exp(offset)))) stop("offsets must lead to a finite risk score") weights <- model.weights(mf) if (!is.null(weights) && any(!is.finite(weights))) stop("weights must be finite") assign <- attrassign(X, Terms) contr.save <- attr(X, "contrasts") <> @ Check for a rare edge case: a data set with no events. In this case the return structure is simple. The coefficients will all be NA, since they can't be estimated. The variance matrix is all zeros, in line with the usual rule to zero out any row and col corresponding to an NA coef. The loglik is the sum of zero terms, which we set to zero like the usual R result for sum(numeric(0)). An overall idea is to return something that won't blow up later code. <>= if (sum(Y[, ncol(Y)]) == 0) { # No events in the data! ncoef <- ncol(X) ctemp <- rep(NA, ncoef) names(ctemp) <- colnames(X) concordance= c(concordant=0, discordant=0, tied.x=0, tied.y=0, tied.xy=0, concordance=NA, std=NA, timefix=FALSE) rval <- list(coefficients= ctemp, var = matrix(0.0, ncoef, ncoef), loglik=c(0,0), score =0, iter =0, linear.predictors = offset, residuals = rep(0.0, data.n), means = colMeans(X), method=method, n = data.n, nevent=0, terms=Terms, assign=assign, concordance=concordance, wald.test=0.0, y = Y, call=Call) class(rval) <- "coxph" return(rval) } @ Check for penalized terms in the model, and set up infrastructure for the fitting routines to deal with them. <>= pterms <- sapply(mf, inherits, 'coxph.penalty') if (any(pterms)) { pattr <- lapply(mf[pterms], attributes) pname <- names(pterms)[pterms] # # Check the order of any penalty terms ord <- attr(Terms, "order")[match(pname, attr(Terms, 'term.labels'))] if (any(ord>1)) stop ('Penalty terms cannot be in an interaction') pcols <- assign[match(pname, names(assign))] fit <- coxpenal.fit(X, Y, istrat, offset, init=init, control, weights=weights, method=method, row.names(mf), pcols, pattr, assign, nocenter= nocenter) } @ <>= else { rname <- row.names(mf) if (multi) rname <- rname[xstack$rindex] if( method=="breslow" || method =="efron") { if (grepl('right', type)) fit <- coxph.fit(X, Y, istrat, offset, init, control, weights=weights, method=method, rname, nocenter=nocenter) else fit <- agreg.fit(X, Y, istrat, offset, init, control, weights=weights, method=method, rname, nocenter=nocenter) } else if (method=='exact') { if (type== "right") fit <- coxexact.fit(X, Y, istrat, offset, init, control, weights=weights, method=method, rname, nocenter=nocenter) else fit <- agexact.fit(X, Y, istrat, offset, init, control, weights=weights, method=method, rname, nocenter=nocenter) } else stop(paste ("Unknown method", method)) } @ <>= if (is.character(fit)) { fit <- list(fail=fit) class(fit) <- 'coxph' } else { if (!is.null(fit$coefficients) && any(is.na(fit$coefficients))) { vars <- (1:length(fit$coefficients))[is.na(fit$coefficients)] msg <-paste("X matrix deemed to be singular; variable", paste(vars, collapse=" ")) if (!singular.ok) stop(msg) # else warning(msg) # stop being chatty } fit$n <- data.n fit$nevent <- sum(Y[,ncol(Y)]) fit$terms <- Terms fit$assign <- assign class(fit) <- fit$class fit$class <- NULL # don't compute a robust variance if there are no coefficients if (robust && !is.null(fit$coefficients) && !all(is.na(fit$coefficients))) { fit$naive.var <- fit$var # a little sneaky here: by calling resid before adding the # na.action method, I avoid having missings re-inserted # I also make sure that it doesn't have to reconstruct X and Y fit2 <- c(fit, list(x=X, y=Y, weights=weights)) if (length(istrat)) fit2$strata <- istrat if (length(cluster)) { temp <- residuals.coxph(fit2, type='dfbeta', collapse=cluster, weighted=TRUE) # get score for null model if (is.null(init)) fit2$linear.predictors <- 0*fit$linear.predictors else fit2$linear.predictors <- c(X %*% init) temp0 <- residuals.coxph(fit2, type='score', collapse=cluster, weighted=TRUE) } else { temp <- residuals.coxph(fit2, type='dfbeta', weighted=TRUE) fit2$linear.predictors <- 0*fit$linear.predictors temp0 <- residuals.coxph(fit2, type='score', weighted=TRUE) } fit$var <- t(temp) %*% temp u <- apply(as.matrix(temp0), 2, sum) fit$rscore <- coxph.wtest(t(temp0)%*%temp0, u, control$toler.chol)$test } #Wald test if (length(fit$coefficients) && is.null(fit$wald.test)) { #not for intercept only models, or if test is already done nabeta <- !is.na(fit$coefficients) # The init vector might be longer than the betas, for a sparse term if (is.null(init)) temp <- fit$coefficients[nabeta] else temp <- (fit$coefficients - init[1:length(fit$coefficients)])[nabeta] fit$wald.test <- coxph.wtest(fit$var[nabeta,nabeta], temp, control$toler.chol)$test } # Concordance. Done here so that we can use cluster if it is present # The returned value is a subset of the full result, partly because it # is all we need, but more for backward compatability with survConcordance.fit if (length(cluster)) temp <- concordancefit(Y, fit$linear.predictors, istrat, weights, cluster=cluster, reverse=TRUE, timefix= FALSE) else temp <- concordancefit(Y, fit$linear.predictors, istrat, weights, reverse=TRUE, timefix= FALSE) if (is.matrix(temp$count)) fit$concordance <- c(colSums(temp$count), concordance=temp$concordance, std=sqrt(temp$var)) else fit$concordance <- c(temp$count, concordance=temp$concordance, std=sqrt(temp$var)) na.action <- attr(mf, "na.action") if (length(na.action)) fit$na.action <- na.action if (model) { if (length(timetrans)) { stop("'model=TRUE' not supported for models with tt terms") } fit$model <- mf } if (x) { fit$x <- X if (length(timetrans)) fit$strata <- istrat else if (length(strats)) fit$strata <- strata.keep } if (y) fit$y <- Y fit$timefix <- control$timefix # remember this option } @ If any of the weights were not 1, save the results. Add names to the means component, which are occassionally useful to survfit.coxph. Other objects below are used when we need to recreate a model frame. <>= if (!is.null(weights) && any(weights!=1)) fit$weights <- weights if (multi) { fit$transitions <- transitions fit$states <- states fit$cmap <- cmap fit$stratum_map <- stratum_map # why not 'stratamap'? Confusion with fit$strata fit$resid <- rowsum(fit$resid, xstack$rindex) # add a suffix to each coefficent name. Those that map to multiple transitions # get the first transition they map to single <- apply(cmap, 1, function(x) all(x %in% c(0, max(x)))) #only 1 coef cindx <- col(cmap)[match(1:length(fit$coefficients), cmap)] rindx <- row(cmap)[match(1:length(fit$coefficients), cmap)] suffix <- ifelse(single[rindx], "", paste0("_", colnames(cmap)[cindx])) names(fit$coefficients) <- paste0(names(fit$coefficients), suffix) if (x) fit$strata <- istrat # save the expanded strata class(fit) <- c("coxphms", class(fit)) } names(fit$means) <- names(fit$coefficients) fit$formula <- formula(Terms) if (length(xlevels) >0) fit$xlevels <- xlevels fit$contrasts <- contr.save if (any(offset !=0)) fit$offset <- offset fit$call <- Call fit @ The model.matrix and model.frame routines are called after a Cox model to reconstruct those portions. Much of their code is shared with the coxph routine. <>= # In internal use "data" will often be an already derived model frame. # We detect this via it having a terms attribute. model.matrix.coxph <- function(object, data=NULL, contrast.arg=object$contrasts, ...) { # # If the object has an "x" component, return it, unless a new # data set is given if (is.null(data) && !is.null(object[['x']])) return(object[['x']]) #don't match "xlevels" Terms <- delete.response(object$terms) if (is.null(data)) mf <- stats::model.frame(object) else { if (is.null(attr(data, "terms"))) mf <- stats::model.frame(Terms, data, xlev=object$xlevels) else mf <- data #assume "data" is already a model frame } cluster <- attr(Terms, "specials")$cluster if (length(cluster)) { temp <- untangle.specials(Terms, "cluster") dropterms <- temp$terms } else dropterms <- NULL strats <- attr(Terms, "specials")$strata hasinteractions <- FALSE if (length(strats)) { stemp <- untangle.specials(Terms, 'strata', 1) if (length(stemp$vars)==1) strata.keep <- mf[[stemp$vars]] else strata.keep <- strata(mf[,stemp$vars], shortlabel=TRUE) istrat <- as.integer(strata.keep) for (i in stemp$vars) { #multiple strata terms are allowed # The factors attr has one row for each variable in the frame, one # col for each term in the model. Pick rows for each strata # var, and find if it participates in any interactions. if (any(attr(Terms, 'order')[attr(Terms, "factors")[i,] >0] >1)) hasinteractions <- TRUE } if (!hasinteractions) dropterms <- c(dropterms, stemp$terms) } else istrat <- NULL <> X } @ In parallel is the model.frame routine, which reconstructs the model frame. This routine currently doesn't do all that we want. To wit, the following code fails: \begin{verbatim} > tfun <- function(formula, ndata) { fit <- coxph(formula, data=ndata) model.frame(fit) } > tfun(Surv(time, status) ~ age, lung) Error: ndata not found \end{verbatim} The genesis of this problem is hard to unearth, but has to do with non standard evaluation rules used by model.frame.default. In essence it pays attention to the environment of the formula, but the enclos argument of eval appears to be ignored. I've not yet found a solution. <>= model.frame.coxph <- function(formula, ...) { dots <- list(...) nargs <- dots[match(c("data", "na.action", "subset", "weights", "id", "cluster", "istate"), names(dots), 0)] # If nothing has changed and the coxph object had a model component, # simply return it. if (length(nargs) ==0 && !is.null(formula$model)) return(formula$model) else { # Rebuild the original call to model.frame Terms <- terms(formula) fcall <- formula$call indx <- match(c("formula", "data", "weights", "subset", "na.action", "cluster", "id", "istate"), names(fcall), nomatch=0) if (indx[1] ==0) stop("The coxph call is missing a formula!") temp <- fcall[c(1,indx)] # only keep the arguments we wanted temp[[1]] <- quote(stats::model.frame) # change the function called temp$xlev <- formula$xlevels # this will turn strings to factors temp$formula <- Terms #keep the predvars attribute # Now, any arguments that were on this call overtake the ones that # were in the original call. if (length(nargs) >0) temp[names(nargs)] <- nargs # Make "tt" visible for coxph formulas, if (!is.null(attr(temp$formula, "specials")$tt)) { coxenv <- new.env(parent= environment(temp$formula)) assign("tt", function(x) x, envir=coxenv) environment(temp$formula) <- coxenv } # The documentation for model.frame implies that the environment arg # to eval will be ignored, but if we omit it there is a problem. if (is.null(environment(formula$terms))) mf <- eval(temp, parent.frame()) else mf <- eval(temp, environment(formula$terms), parent.frame()) if (!is.null(attr(formula$terms, "dataClasses"))) .checkMFClasses(attr(formula$terms, "dataClasses"), mf) if (is.null(attr(Terms, "specials")$tt)) return(mf) else { # Do time transform tt <- eval(formula$call$tt) Y <- aeqSurv(model.response(mf)) strats <- attr(Terms, "specials")$strata if (length(strats)) { stemp <- untangle.specials(Terms, 'strata', 1) if (length(stemp$vars)==1) strata.keep <- mf[[stemp$vars]] else strata.keep <- strata(mf[,stemp$vars], shortlabel=TRUE) istrat <- as.numeric(strata.keep) } <> mf[[".strata."]] <- istrat return(mf) } } } @ survival/noweb/casecohort.Rnw0000644000176200001440000004313213537676563016111 0ustar liggesusers\section{Case-cohort models} Prentice proposed a case-cohort design for large survey studies such as the Women's Health Study, where the population size makes it infeasable to collect data on all of the cases. In this case we might collect data on a random subcohort $SC$ of the full cohort of subjects $C$ along with information on all subjects $E$ who experience an event. Let $n$ be the size of the full cohort, $m$ the size of the subcohort $SC$ and $d$ be the number of events. Let $x$ be the covariate vector. The kernel of the Cox model's score equation has a term for each event $i$ of $x_i(t_i) - \bar{x}(t_i)$ where $\bar{x}$ is the mean of all subjects at risk at that time. In a case-cohort data set the naive estimate of $\bar{x}$ based on the available data will not be correct since the data set is enriched for deaths. The methods below all use a Cox model as the base, but ``fix up'' the result to correct for this bias. <>= cch <- function(formula, data, weights, subset, na.action, subcoh, id, stratum, cohort.size, method=c("Prentice", "SelfPrentice", "LinYing","I.Borgan","II.Borgan"), robust=FALSE){ Call <- match.call() method <- match.arg(method) <> <> <> <> } @ The call processing is a little unusual due to backwards compatability with an older version of the program which used separate model statements for each of the [[subcoh]], [[id]], and [[stratum]] arguments. This was a bad design because it does not properly handle missing values. The obvious way to check this is [[is.formula(subcoh)]] but that would start by retrieving the subcoh argument, which will fail if the variable is part of the data frame. Instead we have to parse the call itself and look for a tilde. If there is one, then we add this to the current formula minus the tilde, otherwise add the argument as is. <>= addterm <- function(oldform, new) { j <- length(oldform) oldform[[j]] <- call("+", oldform[[j]], new[[2]]) } newterm <- list() if (missing(subcoh)) stop ("the subcoh argument is required") else { if (is.call(Call$subcoh) && Call$subcoh[[1]] == as.name("~")) newterm$subcoh <- Call$subcoh[[2]] else newterm$subcoh <- Call$subcoh } if (!missing(id)){ if (is.call(Call$id) && Call$id[[1]] == as.name("~")) newterm$id <- Call$id[[2]] else newterm$id <- Call$id } if (!missing(stratum)) { if (is.call(Call$stratum) && call$stratum[[i]] == as.name("~")) newterm$stratum <- Call$stratum[[2]] else newterm$stratum <- Call$stratum } newform <- formula for (i in newterm) newform <- addterm(newform, newterm) # Formulas can also have cluster and strata terms in them # The next few lines are standard and almost identical to coxph indx <- match(c("formula", "data", "weights", "subset", "na.action"), names(Call), nomatch=0) if (indx[1] ==0) stop("A formula argument is required") temp <- Call[c(1,indx)] # only keep the arguments we wanted temp[[1]] <- quote(stats::model.frame) # change the function called special <- c("strata", "cluster") temp$formula <- if(missing(data)) terms(newform, special) else terms(newform, special, data=data) mf <- eval(temp, parent.frame()) Terms <- terms(mf) @ Now do a sanity check: squawk if they had both an id argument and a cluster term, or both a stratum argument and a strata term. <>= extras <- seq(length=length(newterm), to=length(mf)) names(extras) <- names(newterm) tdrop <- extras if (!is.null(attr(Terms, "specials")$id)) { if (!is.null(newterm$id)) stop("cannot have both an id argument and a cluster term in the formula") temp <- untangle.specials(Terms, "cluster") id <- m[[temp$vars]] tdrop <- c(tdrop, temp$terms) #to be dropped later } else id <- mf[[extras["id"]]] #will usually be NULL if (!is.null(attr(Terms, "specials")$strata)) { if (!is.null(newterms$stratum)) stop("cannot have both a stratum argument and a strata term in the formula") temp <- untangle.specials(Terms, "strata") if (length(temp$vars)==1) strata <- m[[stemp$vars]] else strata <- strata(m[, temp$vars], shortlabel=TRUE) tdrop <- c(tdrop, temp$terms) } else strata <- mf[[extras["stratum"]]] subcoh <- mf[[extras["subcoh"]]] Terms <- Terms[-tdrop] @ Now do a few checks on the retrieved variables <>= n <- nrow(mf) #number of observations if (is.logical(subcoh)) subcoh <- as.numeric(subcoh) if (!all(subcoh %in% 0:1)) stop("subcoh values must be 0/1 or FALSE/TRUE") if (n > sum(cohort.size)) stop("number of records is greater than the cohort size") if (!is.null(strata)) { if (method == "Prentice") method <- "I.Borgan" if (method == "Self-Prentice") method <- "II.Borgan" if (!(method %in% c("I.Borgan", "II.Borgan"))) stop("invalid method for stratified data") strata <- as.factor(strata) if (length(cohort.size)!=length(levels(strata))) stop("cohort.size and stratum do not match") subcohort.sizes <- table(strata) } else { if (length(cohort.size)!=1) stop("cohort size must be a scalar for unstratified analysis") subcohort.sizes <- n } if (robust && method != "LinYing") warning ("robust option ignored for this method") if (!is.null(id) && any(duplicated(id))) stop ("multiple records per id not allowed") @ Now create the data set that will be used for the calls to the underlying coxph code. It will be a (start, stop] data set, even if the input data is not. <>= Y <- model.extract(m, "response") if(!inherits(Y, "Surv")) stop("Response must be a survival object") ytype <- attr(Y, "type") if (!ytype %in% c("right", "counting")) stop("Cox model doesn't support \"", type, "\" survival data") if (ytype == "right") cdata <- data.frame(tstart= rep(0., n), tstop = Y[,1], cens = Y[,2], X = model.matrix(Terms, mf)[, -1, drop=FALSE) else cdata <- data.frame(tstart= Y[,1], tstop = Y[,2], cens = Y[,3], X = model.matrix(Terms, mf[, -1, drop=FALSE))) if (any (!subcoh & cens==0)) stop ("every observation should either be in the subcohort or be an event") @ Y[,3]) if (any(!subcoh & !cens)) stop(sum(!subcoh & !cens),"censored observations not in subcohort") cc<-cens+1-subcoh texit<-switch(itype+1, stop(), Y[,1], Y[,2]) tenter<-switch(itype+1, stop(), rep(0,length(texit)), Y[,1]) X <- model.matrix(Terms, m) X <- X[,2:ncol(X)] fitter <- get(method) if (stratified) out<-fitter(tenter=tenter, texit=texit, cc=cc, id=id, X=X, stratum=as.numeric(stratum), stratum.sizes=cohort.size) else out<-fitter(tenter=tenter, texit=texit, cc=cc, id=id, X=X, ntot=nn, robust=robust) out$method <- method names(out$coefficients) <- dimnames(X)[[2]] if(!is.null(out$var)) dimnames(out$var) <- list(dimnames(X)[[2]], dimnames(X)[[2]]) if(!is.null(out$naive.var)) dimnames(out$naive.var) <- list(dimnames(X)[[2]], dimnames(X)[[2]]) out$call <- call out$cohort.size <- cohort.size out$stratified<-stratified if (stratified){ out$stratum<-stratum out$subcohort.size <-subcohort.sizes } else { out$subcohort.size <- tt[2] } class(out) <- "cch" out } ### Subprograms Prentice <- function(tenter, texit, cc, id, X, ntot,robust){ eps <- 0.00000001 cens <- as.numeric(cc>0) # Censorship indicators subcoh <- as.numeric(cc<2) # Subcohort indicators ## Calculate Prentice estimate ent2 <- tenter ent2[cc==2] <- texit[cc==2]-eps fit1 <- coxph(Surv(ent2,texit,cens)~X,eps=eps,x=TRUE) ## Calculate Prentice estimate and variance nd <- sum(cens) # Number of failures nc <- sum(subcoh) # Number in subcohort ncd <- sum(cc==1) #Number of failures in subcohort X <- as.matrix(X) aent <- c(tenter[cc>0],tenter[cc<2]) aexit <- c(texit[cc>0],texit[cc<2]) aX <- rbind(as.matrix(X[cc>0,]),as.matrix(X[cc<2,])) aid <- c(id[cc>0],id[cc<2]) dum <- rep(-100,nd) dum <- c(dum,rep(0,nc)) gp <- rep(1,nd) gp <- c(gp,rep(0,nc)) fit <- coxph(Surv(aent,aexit,gp)~aX+offset(dum)+cluster(aid),eps=eps,x=TRUE, iter.max=35,init=fit1$coefficients) db <- resid(fit,type="dfbeta") db <- as.matrix(db) db <- db[gp==0,] fit$phase2var<-(1-(nc/ntot))*t(db)%*%(db) fit$naive.var <- fit$naive.var+fit$phase2var fit$var<-fit$naive.var fit$coefficients <- fit$coef <- fit1$coefficients fit } SelfPrentice <- function(tenter, texit, cc, id, X, ntot,robust){ eps <- 0.00000001 cens <- as.numeric(cc>0) # Censorship indicators subcoh <- as.numeric(cc<2) # Subcohort indicators ## Calculate Self-Prentice estimate and variance nd <- sum(cens) # Number of failures nc <- sum(subcoh) # Number in subcohort ncd <- sum(cc==1) #Number of failures in subcohort X <- as.matrix(X) aent <- c(tenter[cc>0],tenter[cc<2]) aexit <- c(texit[cc>0],texit[cc<2]) aX <- rbind(as.matrix(X[cc>0,]),as.matrix(X[cc<2,])) aid <- c(id[cc>0],id[cc<2]) dum <- rep(-100,nd) dum <- c(dum,rep(0,nc)) gp <- rep(1,nd) gp <- c(gp,rep(0,nc)) fit <- coxph(Surv(aent,aexit,gp)~aX+offset(dum)+cluster(aid),eps=eps,x=TRUE) db <- resid(fit,type="dfbeta") db <- as.matrix(db) db <- db[gp==0,,drop=FALSE] fit$phase2var<-(1-(nc/ntot))*t(db)%*%(db) fit$naive.var <- fit$naive.var+fit$phase2var fit$var<-fit$naive.var fit } LinYing <- function(tenter, texit, cc, id, X, ntot,robust){ eps <- 0.000000001 cens <- as.numeric(cc>0) # Censorship indicators subcoh <- as.numeric(cc<2) # Subcohort indicators nd <- sum(cens) # Number of failures nc <- sum(subcoh) # Number in subcohort ncd <- sum(cc==1) #Number of failures in subcohort ## Calculate Lin-Ying estimate and variance offs <- rep((ntot-nd)/(nc-ncd),length(texit)) offs[cc>0] <- 1 loffs <- log(offs) fit <- coxph(Surv(tenter, texit, cens)~X+offset(loffs)+cluster(id), eps=eps,x=TRUE) db <- resid(fit,type="dfbeta") db <- as.matrix(db) db0 <- db[cens==0,,drop=FALSE] dbm <- apply(db0,2,mean) db0 <- sweep(db0,2,dbm) fit$phase2var<-(1-(nc-ncd)/(ntot-nd))*crossprod(db0) fit$naive.var <- fit$naive.var+fit$phase2var if (robust) fit$var<- crossprod(db,db/offs)+fit$phase2var else fit$var<-fit$naive.var fit } I.Borgan <- function(tenter, texit, cc, id, X, stratum, stratum.sizes){ eps <- 0.00000001 nobs <- length(texit) idx <- 1:length(nobs) jj <- max(stratum) nn <- stratum.sizes ## Cohort stratum sizes n <- table(stratum) ## Sample stratum sizes d <- table(stratum[cc>0]) ## Failures in each stratum tt <- table(cc,stratum) cens <- as.numeric(cc>0) ## Failure indicators subcoh <- as.numeric(cc<2) ## Subcohort indicators nd <- sum(cens) ## Number of failures nc <- sum(subcoh) ## Number in subcohort ncd <- sum(as.numeric(cc==1)) #Number of failures in subcohort m0 <- tt[1,] ## Subcohort stratum sizes (noncases only) if (ncd>0) m <- m0+tt[2,] else m <- m0 #Subcohort stratum sizes X <- as.matrix(X) kk <- ncol(X) ## Number of variables wt <- as.vector(nn/m) ## Weights for Estimator I stratum <- c(stratum[cc>0],stratum[cc<2]) w <- wt[stratum] ent <- c(tenter[cc > 0], tenter[cc < 2]) exit <- c(texit[cc > 0], texit[cc < 2]) X <- rbind(as.matrix(X[cc > 0, ]), as.matrix(X[cc < 2, ])) id <- c(id[cc > 0], id[cc < 2]) dum <- rep(-100, nd) dum <- c(dum, rep(0, nc)) gp <- rep(1, nd) gp <- c(gp, rep(0, nc)) w[gp==1] <- 1 fit <- coxph(Surv(ent,exit,gp)~X+offset(dum)+cluster(id), weights=w, eps=eps,x=T, iter.max=25) score <- resid(fit, type = "score", weighted=F) sc <- resid(fit, type="score", collapse=id, weighted=T) score <- as.matrix(score) score <- score[gp == 0,,drop=F] st <- stratum[gp==0] sto <- st %o% rep(1,kk) Index <- col(score) tscore <- tapply(score,list(sto,Index),mean) pscore <- tapply(score,list(sto,Index)) score <- score-tscore[pscore] delta <- matrix(0,kk,kk) opt <- NULL for (j in 1:jj) { temp <- t(score[st==j,])%*%score[st==j,]/(m[j]-1) delta <- matrix(delta+(wt[j]-1)*nn[j]*temp,kk,kk) if(is.null(opt)) opt <- nn[j]*sqrt(diag(fit$naive.var %*% temp %*% fit$naive.var)) else opt <- rbind(opt,nn[j]*sqrt(diag(fit$naive.var %*% temp %*% fit$naive.var))) } z <- apply(opt,2,sum) fit$opt <- sweep(opt,2,z,FUN="/") fit$phase2var<-fit$naive.var%*%delta%*%fit$naive.var fit$naive.var <- fit$naive.var+fit$phase2var fit$var<-fit$naive.var fit$delta <- delta fit$sc <- sc fit } II.Borgan <- function(tenter, texit, cc, id, X, stratum, stratum.sizes){ eps <- 0.00000001 jj <- max(stratum) nn <- stratum.sizes ## Cohort stratum sizes n <- table(stratum) ## Sample stratum sizes d <- table(stratum[cc>0]) ## Failures in each stratum tt <- table(cc,stratum) cens <- as.numeric(cc>0) ## Failure indicators subcoh <- as.numeric(cc<2) ## Subcohort indicators nd <- sum(cens) ## Number of failures nc <- sum(subcoh) ## Number in subcohort ncd <- sum(as.numeric(cc==1)) #Number of failures in subcohort m0 <- tt[1,] ## Subcohort stratum sizes (controls only) if (ncd>0) m <- m0+tt[2,] else m <- m0 #Subcohort stratum sizes X <- as.matrix(X) kk <- ncol(X) ## Number of variables nn0 <- nn-as.vector(d) #Noncases in cohort wt <- as.vector(nn0/m0) w <- wt[stratum] w[cens==1] <- 1 fit <- coxph(Surv(tenter,texit,cens)~X+cluster(id), weights=w,eps=eps,x=T, iter.max=25) ## Borgan Estimate II score <- resid(fit, type = "score", weighted=F) sc <- resid(fit,type="score", collapse=id, weighted=T) score <- as.matrix(score) score <- score[cens == 0,,drop=F] ## Scores for controls st <- stratum[cens==0] ## Stratum indicators for controls sto <- st %o% rep(1,kk) Index <- col(score) tscore <- tapply(score,list(sto,Index),mean) ## Within stratum control score means pscore <- tapply(score,list(sto,Index)) score <- score-tscore[pscore] ## Subtract off within stratum score means delta <- matrix(0,kk,kk) opt <- NULL for (j in 1:jj) { temp <- t(score[st==j,])%*%score[st==j,]/(m0[j]-1) ## Borgan equation (19) delta <- delta+(wt[j]-1)*nn0[j]*temp ## Borgan equation (17) if(is.null(opt)) opt <- nn0[j]*sqrt(diag(fit$naive.var %*% temp %*% fit$naive.var)) else opt <- rbind(opt,nn0[j]*sqrt(diag(fit$naive.var %*% temp %*% fit$naive.var))) } z <- apply(opt,2,sum) fit$opt <- sweep(opt,2,z,FUN="/") fit$phase2var<-fit$naive.var %*% delta %*% fit$naive.var fit$naive.var <- fit$naive.var+fit$phase2var fit$var<-fit$naive.var fit$delta <- delta fit$sc <- sc fit } ## Methods vcov.cch<-function(object,...) object$var "print.cch"<- function(x,...) { ## produces summary from an x of the class "cch" call<-x$call coef <- coef(x) method <- x$method se <- sqrt(diag(vcov(x))) Z<- abs(coef/se) p<- pnorm(Z) cohort.size<-x$cohort.size subcohort.size<-x$subcohort.size coefficients <- matrix(0, nrow = length(coef), ncol = 4) dimnames(coefficients) <- list(names(coef), c("Value", "SE", "Z", "p")) coefficients[, 1] <- coef coefficients[, 2] <- se coefficients[, 3] <- Z coefficients[, 4] <- 2*(1-p) if (x$stratified){ cat("Exposure-stratified case-cohort analysis,", x$method, "method.\n") m<-rbind(subcohort=x$subcohort.size, cohort=x$cohort.size) prmatrix(m,quote=FALSE) } else{ cat("Case-cohort analysis,") cat("x$method,", x$method,"\n with subcohort of", x$subcohort.size,"from cohort of", x$cohort.size,"\n\n") } cat("Call: "); print(x$call) cat("\nCoefficients:\n") print(coefficients) invisible(x) } "summary.cch"<-function(object,...) { ## produces summary from an object of the class "cch" call<-object$call coef <- coef(object) method <- object$method se <- sqrt(diag(vcov(object))) Z<- abs(coef/se) p<- pnorm(Z) cohort.size<-object$cohort.size subcohort.size<-object$subcohort.size coefficients <- matrix(0, nrow = length(coef), ncol = 4) dimnames(coefficients) <- list(names(coef), c("Value", "SE", "Z", "p")) coefficients[, 1] <- coef coefficients[, 2] <- se coefficients[, 3] <- Z coefficients[, 4] <- 2*(1-p) structure(list(call=call, method=method, cohort.size=cohort.size, subcohort.size=subcohort.size, coefficients = coefficients, stratified=object$stratified), class = "summary.cch") } print.summary.cch <- function(x,digits=3,...){ if (x$stratified){ cat("Exposure-stratified case-cohort analysis,", x$method, "method.\n") m<-rbind(subcohort=x$subcohort.size, cohort=x$cohort.size) prmatrix(m,quote=FALSE) } else{ cat("Case-cohort analysis,") cat("x$method,", x$method,"\n with subcohort of", x$subcohort.size,"from cohort of", x$cohort.size,"\n\n") } cat("Call: "); print(x$call) cat("\nCoefficients:\n") output<-cbind(Coef=x$coefficients[,1],HR=exp(x$coefficients[,1]), "(95%"=exp(x$coefficients[,1]-1.96*x$coefficients[,2]), "CI)"=exp(x$coefficients[,1]+1.96*x$coefficients[,2]), "p"=x$coefficients[,4] ) print(round(output,3)) invisible(x) } survival/noweb/parse.Rnw0000644000176200001440000005000414027425473015050 0ustar liggesusers\subsection{Parsing the covariates list} For a multi-state Cox model we allow a list of formulas to take the place of the \code{formula} argument. The first element of the list is the default formula, later elements are of the form \code{transitions ~ formula/options}, where the left hand side denotes one or more transitions, and the right hand side is used to augment the basic formula wrt those transitions. Step 1 is to break the formula into parts. There will be a list of left sides, a list of right sides, and a list of options. From this we can create a single ``pseudo formula'' that is used to drive the model.frame process, which ensures that all of the variables we need will be found in the model frame. Further processing has to wait until after the model frame has been constructed, i.e., if a left side referred to state ``deathh'' that might be a real state or a typing mistake, we can't know until the data is in hand. Should we walk the parse tree of the formula, or convert it to character and use string manipulations? The latter looks promising until you see a fragment like this: \code{entry:death ~ age/sex + ns(weight/height, df=4) / common} Walking the parse tree is a bit more subtle, but we then can take advantage of all the knowledge built into the R parser. A formula is a 3 element list of ``~'', leftside, rightside, or 2 elements if it has only a right hand side. Legal ones for coxph have both left and right. <>= parsecovar1 <- function(flist, statedata) { if (any(sapply(flist, function(x) !inherits(x, "formula")))) stop("an element of the formula list is not a formula") if (any(sapply(flist, length) != 3)) stop("all formulas must have a left and right side") # split the formulas into a right hand and left hand side lhs <- lapply(flist, function(x) x[-3]) # keep the ~ rhs <- lapply(flist, function(x) x[[3]]) # don't keep the ~ rhs <- parse_rightside(rhs) <> list(rhs = rhs, lhs= lterm) } @ \begin{figure} \includegraphics{figures/fig1.pdf} \caption{The parse tree for the formula \code{1:3 +2:3 ~ strata(sex)/(age + trt) + ns(weight/ht, df=4) / common + shared}} \label{figparse} \end{figure} Figure \ref{figparse} shows the parse tree for a complex formula. The following function splits the formula at the rightmost slash, ignoring the inside of any function or parenthesised phrase. Recursive functions like this are almost impossible to read, but luckily it is short. The formula recurrs on the left and right side of +*: and \%in\%, and on binary - (but not on unary -). <>= rightslash <- function(x) { if (class(x) != 'call') return(x) else { if (x[[1]] == as.name('/')) return(list(x[[2]], x[[3]])) else if (x[[1]]==as.name('+') || (x[[1]]==as.name('-') && length(x)==3)|| x[[1]]==as.name('*') || x[[1]]==as.name(':') || x[[1]]==as.name('%in%')) { temp <- rightslash(x[[3]]) if (is.list(temp)) { x[[3]] <- temp[[1]] return(list(x, temp[[2]])) } else { temp <- rightslash(x[[2]]) if (is.list(temp)) { x[[2]] <- temp[[2]] return(list(temp[[1]], x)) } else return(x) } } else return(x) } } @ There are 4 possble options of common, shared, and init. The first 2 appear just as words, the last should have a set of values attached which become the \code{ival} vector. There will, of course, one day be a user with a variable named \code{common} who wants a nested term \code{x/common}. Since we don't look inside parenthesis they will be able to use \code{1:3 ~ (x/common)}. <>= parse_rightside <- function(rhs) { parts <- lapply(rhs, rightslash) new <- lapply(parts, function(opt) { tform <- ~ x # a skeleton, "x" will be replaced if (!is.list(opt)) { # no options for this line tform[[2]] <- opt list(formula = tform, ival = NULL, common = FALSE, shared = FALSE) } else{ # treat the option list as though it were a formula temp <- ~ x temp[[2]] <- opt[[2]] optterms <- terms(temp) ff <- rownames(attr(optterms, "factors")) index <- match(ff, c("common", "shared", "init")) if (any(is.na(index))) stop("option not recognized in a covariates formula: ", paste(ff[is.na(index)], collapse=", ")) common <- any(index==1) shared <- any(index==2) if (any(index==3)) { optatt <- attributes(optterms) j <- optatt$variables[1 + which(index==3)] j[[1]] <- as.name("list") ival <- unlist(eval(j, parent.frame())) } else ival <- NULL tform[[2]] <- opt[[1]] list(formula= tform, ival= ival, common= common, shared=shared) } }) new } @ The left hand side of each formula specifies the set of transitions to which the covariates apply, and is more complex. Say instance that we had 7 states and the following statedata data set. \begin{center} \begin{tabular}{cccc} state & A& N& death \\ \hline A-N- & 0& 0 & 0\\ A+N- & 1& 0 & 0\\ A-N1 & 0& 1 & 0\\ A+N1 & 1& 1 & 0\\ A-N2 & 0& 2 & 0\\ A+N2 & 1& 2 & 0\\ Death& NA & NA& 1 \end{tabular} \end{center} Here are some valid transitions \begin{enumerate} \item 0:state('A+N+'), any transition to the A+N+ state \item state('A-N-'):death(0), a transition from A-N-, but not to death \item A(0):A(1), any of the 4 changes that start with A=0 and end with A=1 \item N(0):N(1,2) + N(1):N(2), an upward change of N \item 'A-N-':c('A-N+','A+N-'); if there is no variable then the overall state is assumed \item 1:3 + 2:3; we can refer to states by number, and we can have multiples \end{enumerate} <>= # deal with the left hand side of the formula # the next routine cuts at '+' signs pcut <- function(form) { if (length(form)==3) { if (form[[1]] == '+') c(pcut(form[[2]]), pcut(form[[3]])) else if (form[[1]] == '~') pcut(form[[2]]) else list(form) } else list(form) } lcut <- lapply(lhs, function(x) pcut(x[[2]])) @ We now have one list per formula, each list is either a single term or a list of terms (case 4 above). To make evaluation easier, create functions that append their name to a list of values. I have not yet found a way to do this without eval(parse()), which always seems clumsy. A use for the labels without an argument will arise later, hence the double environments. Repeating the list above, this is what we want to end with \begin{itemize} \item a list with one element per formula in the covariates list \item each element is a list, with one element per term: multiple a:b terms are allowed separated by + signs \item each of these level 3 elements is a list with two elements ``left'' and ``right'', for the two sides of the : operator \item left and right will be one of 3 forms: a simple vector, a one element list containing the stateid, or a two element list containing the stateid and the values. Any word that doesn't match one of the column names of statedata ends up as a vector. \end{itemize} <>= env1 <- new.env(parent= parent.frame(2)) env2 <- new.env(parent= env1) if (missing(statedata)) { assign("state", function(...) list(stateid= "state", values=c(...)), env1) assign("state", list(stateid="state")) } else { for (i in statedata) { assign(i, eval(list(stateid=i)), env2) tfun <- eval(parse(text=paste0("function(...) list(stateid='" , i, "', values=c(...))"))) assign(i, tfun, env1) } } lterm <- lapply(lcut, function(x) { lapply(x, function(z) { if (length(z)==1) { temp <- eval(z, envir= env2) if (is.list(temp) && names(temp)[[1]] =="stateid") temp else temp } else if (length(z) ==3 && z[[1]]==':') list(left=eval(z[[2]], envir=env2), right=eval(z[[3]], envir=env2)) else stop("invalid term: ", deparse(z)) }) }) @ The second call, which builds tmap, the terms map. Arguments are the results from the first pass, the statedata data frame, the default formula, the terms structure from the full formula, and the transitions count. One nuisance is that the terms function sometimes inverts things. For example in the formula \code{terms(~ x1 + x1:iage + x2 + x2:iage)} the label for the second of these becomes \code{iage:x2}. I'm guessing it is because the variable first appear in the order x1, iage, x2 and labels make use of that order. But when we look at the formula fragment \code{~ x2 + x2:iage} the terms will be in the other order. A way out of this is to use the simple \code{termmatch} function below, which keys off of the factors attribute instead of the names. <>= termmatch <- function(f1, f2) { # look for f1 in f2, each the factors attribute of a terms object if (length(f1)==0) return(NULL) # a formula with only ~1 irow <- match(rownames(f1), rownames(f2)) if (any(is.na(irow))) stop ("termmatch failure 1") hashfun <- function(j) sum(ifelse(j==0, 0, 2^(seq(along.with=j)))) hash1 <- apply(f1, 2, hashfun) hash2 <- apply(f2[irow,,drop=FALSE], 2, hashfun) index <- match(hash1, hash2) if (any(is.na(index))) stop("termmatch failure 2") index } parsecovar2 <- function(covar1, statedata, dformula, Terms, transitions,states) { if (is.null(statedata)) statedata <- data.frame(state = states, stringsAsFactors=FALSE) else { if (is.null(statedata$state)) stop("the statedata data set must contain a variable 'state'") indx1 <- match(states, statedata$state, nomatch=0) if (any(indx1==0)) stop("statedata does not contain all the possible states: ", states[indx1==0]) statedata <- statedata[indx1,] # put it in order } # Statedata might have rows for states that are not in the data set, # for instance if the coxph call had used a subset argument. Any of # those were eliminated above. # Likewise, the formula list might have rules for transitions that are # not present. Don't worry about it at this stage. allterm <- attr(Terms, 'factors') nterm <- ncol(allterm) # create a map for every transition, even ones that are not used. # at the end we will thin it out # It has an extra first row for intercept (baseline) # Fill it in with the default formula nstate <- length(states) tmap <- array(0, dim=c(nterm+1, nstate, nstate)) dmap <- array(seq_len(length(tmap)), dim=c(nterm+1, nstate, nstate)) #unique values dterm <- termmatch(attr(terms(dformula), "factors"), allterm) dterm <- c(1L, 1L+ dterm) # add intercept tmap[dterm,,] <- dmap[dterm,,] inits <- NULL if (!is.null(covar1)) { <> } <> } @ Now go through the formulas one by one. The left hand side tells us which state:state transitions to fill in, the right hand side tells the variables. The code block below goes through lhs element(s) for a single formula. That element is itself a list which has an entry for each term, and that entry can have left and right portions. <>= state1 <- state2 <- NULL for (x in lhs) { # x is one term if (!is.list(x) || is.null(x$left)) stop("term found without a ':' ", x) # left of the colon if (!is.list(x$left) && length(x$left) ==1 && x$left==0) temp1 <- 1:nrow(statedata) else if (is.numeric(x$left)) { temp1 <- as.integer(x$left) if (any(temp1 != x$left)) stop("non-integer state number") if (any(temp1 <1 | temp1> nstate)) stop("numeric state is out of range") } else if (is.list(x$left) && names(x$left)[1] == "stateid"){ if (is.null(x$left$value)) stop("state variable with no list of values: ",x$left$stateid) else { if (any(k= is.na(match(x$left$stateid, names(statedata))))) stop(x$left$stateid[k], ": state variable not found") zz <- statedata[[x$left$stateid]] if (any(k= is.na(match(x$left$value, zz)))) stop(x$left$value[k], ": state value not found") temp1 <- which(zz %in% x$left$value) } } else { k <- match(x$left, statedata$state) if (any(is.na(k))) stop(x$left[is.na(k)], ": state not found") temp1 <- which(statedata$state %in% x$left) } # right of colon if (!is.list(x$right) && length(x$right) ==1 && x$right ==0) temp2 <- 1:nrow(statedata) else if (is.numeric(x$right)) { temp2 <- as.integer(x$right) if (any(temp2 != x$right)) stop("non-integer state number") if (any(temp2 <1 | temp2> nstate)) stop("numeric state is out of range") } else if (is.list(x$right) && names(x$right)[1] == "stateid") { if (is.null(x$right$value)) stop("state variable with no list of values: ",x$right$stateid) else { if (any(k= is.na(match(x$right$stateid, names(statedata))))) stop(x$right$stateid[k], ": state variable not found") zz <- statedata[[x$right$stateid]] if (any(k= is.na(match(x$right$value, zz)))) stop(x$right$value[k], ": state value not found") temp2 <- which(zz %in% x$right$value) } } else { k <- match(x$right, statedata$state) if (any(is.na(k))) stop(x$right[k], ": state not found") temp2 <- which(statedata$state %in% x$right) } state1 <- c(state1, rep(temp1, length(temp2))) state2 <- c(state2, rep(temp2, each=length(temp1))) } @ At the end it has created to vectors state1 and state2 listing all the pairs of states that are indicated. The init clause (initial values) are gathered but not checked: we don't yet know how many columns a term will expand into. tmap is a 3 way array: term, state1, state2 containing coefficient numbers and zeros. <>= for (i in 1:length(covar1$rhs)) { rhs <- covar1$rhs[[i]] lhs <- covar1$lhs[[i]] # one rhs and one lhs per formula <> npair <- length(state1) # number of state:state pairs for this line # update tmap for this set of transitions # first, what variables are mentioned, and check for errors rterm <- terms(rhs$formula) rindex <- 1L + termmatch(attr(rterm, "factors"), allterm) # the update.formula function is good at identifying changes # formulas that start with "- x" have to be pasted on carefully temp <- substring(deparse(rhs$formula, width.cutoff=500), 2) if (substring(temp, 1,1) == '-') dummy <- formula(paste("~ .", temp)) else dummy <- formula(paste("~. +", temp)) rindex1 <- termmatch(attr(terms(dformula), "factors"), allterm) rindex2 <- termmatch(attr(terms(update(dformula, dummy)), "factors"), allterm) dropped <- 1L + rindex1[is.na(match(rindex1, rindex2))] # remember the intercept if (length(dropped) >0) { for (k in 1:npair) tmap[dropped, state1[k], state2[k]] <- 0 } # grab initial values if (length(rhs$ival)) inits <- c(inits, list(term=rindex, state1=state1, state2= state2, init= rhs$ival)) # adding -1 to the front is a trick, to check if there is a "+1" term dummy <- ~ -1 + x dummy[[2]][[3]] <- rhs$formula if (attr(terms(dummy), "intercept") ==1) rindex <- c(1L, rindex) # an update of "- sex" won't generate anything to add # dmap is simply an indexed set of unique values to pull from, so that # no number is used twice if (length(rindex) > 0) { # rindex = things to add if (rhs$common) { j <- dmap[rindex, state1[1], state2[1]] for(k in 1:npair) tmap[rindex, state1[k], state2[k]] <- j } else { for (k in 1:npair) tmap[rindex, state1[k], state2[k]] <- dmap[rindex, state1[k], state2[k]] } } # Deal with the shared argument, using - for a separate coef if (rhs$shared && npair>1) { j <- dmap[1, state1[1], state2[1]] for (k in 2:npair) tmap[1, state1[k], state2[k]] <- -j } } @ Fold the 3-dimensional tmap into a matrix with terms as rows and one column for each transition that actually occured. <>= i <- match("(censored)", colnames(transitions), nomatch=0) if (i==0) t2 <- transitions else t2 <- transitions[,-i, drop=FALSE] # transitions to 'censor' don't count indx1 <- match(rownames(t2), states) indx2 <- match(colnames(t2), states) tmap2 <- matrix(0L, nrow= 1+nterm, ncol= sum(t2>0)) trow <- row(t2)[t2>0] tcol <- col(t2)[t2>0] for (i in 1:nrow(tmap2)) { for (j in 1:ncol(tmap2)) tmap2[i,j] <- tmap[i, indx1[trow[j]], indx2[tcol[j]]] } # Remember which hazards had ph # tmap2[1,] is the 'intercept' row # If the hazard for colum 6 is proportional to the hazard for column 2, # the tmap2[1,2] = tmap[1,6], and phbaseline[6] =2 temp <- tmap2[1,] tmap2[1,] <- match(abs(tmap2[1,]), unique(abs(temp))) phbaseline <- ifelse(temp<0, tmap2[1,], 0) if (nrow(tmap2) > 1) tmap2[-1,] <- match(tmap2[-1,], unique(c(0L, tmap2[-1,]))) -1L dimnames(tmap2) <- list(c("(Baseline)", colnames(allterm)), paste(indx1[trow], indx2[tcol], sep=':')) # mapid gives the from,to for each realized state list(tmap = tmap2, inits=inits, mapid= cbind(from=indx1[trow], to=indx2[tcol]), phbaseline = phbaseline) @ Last is a helper routine that converts tmap, which has one row per term, into cmap, which has one row per coefficient. Both have one column per transition. It uses the assign attribute of the X matrix along with the column names. Consider the model \code{~ x1 + strata(x2) + factor(x3)} where x3 has 4 levels. The Xassign vector will be 1, 3, 3, 3, since it refers to terms and there are 3 columns of X for term number 3. If there were an intercept the first column of X would be a 1 and Xassign would be 0, 1, 3, 3, 3. Let's say that there were 3 transitions and tmap looks like this: \begin{tabular}{rccc} & 1:2 & 1:3 & 2:3 \\ (Baseline) & 1 & 2 & 3 \\ x1 & 1 & 4 & 4 \\ strata(x2) & 2 & 5 & 6 \\ factor(x3) & 3 & 3 & 7 \end{tabular} The cmap matrix will ignore rows 1 and 3 since they do not correspond to coefficients in the model. <>= parsecovar3 <- function(tmap, Xcol, Xassign, phbaseline=NULL) { # sometime X will have an intercept, sometimes not; cmap never does hasintercept <- (Xassign[1] ==0) ptemp <- phbaseline[phbaseline >0] nph.coef <- length(ptemp) nph.row <- length(unique(ptemp)) cmap <- matrix(0L, length(Xcol) + nph.row - hasintercept, ncol(tmap)) uterm <- unique(Xassign[Xassign != 0]) # terms that will have coefficients xcount <- table(factor(Xassign, levels=1:max(Xassign))) mult <- 1+ max(xcount) # temporary scaling ii <- 0 for (i in uterm) { k <- seq_len(xcount[i]) for (j in 1:ncol(tmap)) cmap[ii+k, j] <- if(tmap[i+1,j]==0) 0 else tmap[i+1,j]*mult +k ii <- ii + max(k) } if (nph.row > 0) { i <- length(Xcol)- hasintercept # non-ph rows in cmap j <- cbind(i+ match(ptemp, unique(ptemp)), which(phbaseline>0)) cmap[j] <- max(cmap) + seq(along.with =ptemp) newname <- paste0("ph(",colnames(tmap)[unique(ptemp)], ")") } else newname <- NULL # renumber coefs as 1, 2, 3, ... cmap[,] <- match(cmap, sort(unique(c(0L, cmap)))) -1L colnames(cmap) <- colnames(tmap) if (hasintercept) rownames(cmap) <- c(Xcol[-1], newname) else rownames(cmap) <- c(Xcol, newname) cmap } @ survival/noweb/predict.coxph.Rnw0000644000176200001440000005540713761050642016520 0ustar liggesusers\subsection{The predict method} The \code{predict.coxph} function produces various types of predicted values from a Cox model. The arguments are \begin{description} \item [object] The result of a call to \code{coxph}. \item [newdata] Optionally, a new data set for which prediction is desired. If this is absent predictions are for the observations used fit the model. \item[type] The type of prediction \begin{itemize} \item lp = the linear predictor for each observation \item risk = the risk score $exp(lp)$ for each observation \item expected = the expected number of events \item survival = predicted survival = exp(-expected) \item terms = a matrix with one row per subject and one column for each term in the model. \end{itemize} \item[se.fit] Whether or not to return standard errors of the predictions. \item[na.action] What to do with missing values \emph{if} there is new data. \item[terms] The terms that are desired. This option is almost never used, so rarely in fact that it's hard to justify keeping it. \item[collapse] An optional vector of subject identifiers, over which to sum or `collapse' the results \item[reference] the reference context for centering the results \item[\ldots] All predict methods need to have a \ldots argument; we make no use of it however. \end{description} %\subsection{Setup} The first task of the routine is to reconsruct necessary data elements that were not saved as a part of the \code{coxph} fit. We will need the following components: \begin{itemize} \item for type=`expected' residuals we need the orignal survival y. This %'` is saved in coxph objects by default so will only need to be fetched in the highly unusual case that a user specfied \code{y=FALSE} in the orignal call. \item for any call with either newdata, standard errors, or type='terms' the original $X$ matrix, weights, strata, and offset. When checking for the existence of a saved $X$ matrix we can't %' use \code{object\$x} since that will also match the \code{xlevels} component. \item the new data matrix, if any \end{itemize} <>= predict.coxph <- function(object, newdata, type=c("lp", "risk", "expected", "terms", "survival"), se.fit=FALSE, na.action=na.pass, terms=names(object$assign), collapse, reference=c("strata", "sample", "zero"), ...) { <> <> if (type=="expected") { <> } else { <> <> } <> } @ We start of course with basic argument checking. Then retrieve the model parameters: does it have a strata statement, offset, etc. The \code{Terms2} object is a model statement without the strata or cluster terms, appropriate for recreating the matrix of covariates $X$. For type=expected the response variable needs to be kept, if not we remove it as well since the user's newdata might not contain one. %' The type= survival is treated the same as type expected. <>= if (!inherits(object, 'coxph')) stop("Primary argument much be a coxph object") Call <- match.call() type <-match.arg(type) if (type=="survival") { survival <- TRUE type <- "expected" #this is to stop lots of "or" statements } else survival <- FALSE n <- object$n Terms <- object$terms if (!missing(terms)) { if (is.numeric(terms)) { if (any(terms != floor(terms) | terms > length(object$assign) | terms <1)) stop("Invalid terms argument") } else if (any(is.na(match(terms, names(object$assign))))) stop("a name given in the terms argument not found in the model") } # I will never need the cluster argument, if present delete it. # Terms2 are terms I need for the newdata (if present), y is only # needed there if type == 'expected' if (length(attr(Terms, 'specials')$cluster)) { temp <- untangle.specials(Terms, 'cluster', 1) Terms <- object$terms[-temp$terms] } else Terms <- object$terms if (type != 'expected') Terms2 <- delete.response(Terms) else Terms2 <- Terms has.strata <- !is.null(attr(Terms, 'specials')$strata) has.offset <- !is.null(attr(Terms, 'offset')) has.weights <- any(names(object$call) == 'weights') na.action.used <- object$na.action n <- length(object$residuals) if (missing(reference) && type=="terms") reference <- "sample" else reference <- match.arg(reference) @ The next task of the routine is to reconsruct necessary data elements that were not saved as a part of the \code{coxph} fit. We will need the following components: \begin{itemize} \item for type=`expected' residuals we need the orignal survival y. This %'` is saved in coxph objects by default so will only need to be fetched in the highly unusual case that a user specfied \code{y=FALSE} in the orignal call. We also need the strata in this case. Grabbing it is the same amount of work as grabbing X, so gets lumped with that case in the code. \item for any call with either standard errors, reference strata, or type=`terms' the original $X$ matrix, weights, strata, and offset. When checking for the existence of a saved $X$ matrix we can't %' use \code{object\$x} since that will also match the \code{xlevels} component. \item the new data matrix, if present, along with offset and strata. \end{itemize} For the case that none of the above are needed, we can use the \code{linear.predictors} component of the fit. The variable \code{use.x} signals this case, which takes up almost none of the code but is common in usage. The check below that nrow(mf)==n is to avoid data sets that change under our feet. A fit was based on data set ``x'', and when we reconstruct the data frame it is a different size! This means someone changed the data between the model fit and the extraction of residuals. One other non-obvious case is that coxph treats the model \code{age:strata(grp)} as though it were \code{age:strata(grp) + strata(grp)}. The untangle.specials function will return \code{vars= strata(grp), terms=integer(0)}; the first shows a strata to extract and the second that there is nothing to remove from the terms structure. <>= have.mf <- FALSE if (type == "expected") { y <- object[['y']] if (is.null(y)) { # very rare case mf <- stats::model.frame(object) y <- model.extract(mf, 'response') have.mf <- TRUE #for the logic a few lines below, avoid double work } } # This will be needed if there are strata, and is cheap to compute strat.term <- untangle.specials(Terms, "strata") if (se.fit || type=='terms' || (!missing(newdata) && type=="expected") || (has.strata && (reference=="strata") || type=="expected")) { use.x <- TRUE if (is.null(object[['x']]) || has.weights || has.offset || (has.strata && is.null(object$strata))) { # I need the original model frame if (!have.mf) mf <- stats::model.frame(object) if (nrow(mf) != n) stop("Data is not the same size as it was in the original fit") x <- model.matrix(object, data=mf) if (has.strata) { if (!is.null(object$strata)) oldstrat <- object$strata else { if (length(strat.term$vars)==1) oldstrat <- mf[[strat.term$vars]] else oldstrat <- strata(mf[,strat.term$vars], shortlabel=TRUE) } } else oldstrat <- rep(0L, n) weights <- model.weights(mf) if (is.null(weights)) weights <- rep(1.0, n) offset <- model.offset(mf) if (is.null(offset)) offset <- 0 } else { x <- object[['x']] if (has.strata) oldstrat <- object$strata else oldstrat <- rep(0L, n) weights <- rep(1.,n) offset <- 0 } } else { # I won't need strata in this case either if (has.strata) { stemp <- untangle.specials(Terms, 'strata', 1) Terms2 <- Terms2[-stemp$terms] has.strata <- FALSE #remaining routine never needs to look } oldstrat <- rep(0L, n) offset <- 0 use.x <- FALSE } @ Now grab data from the new data set. We want to use model.frame processing, in order to correctly expand factors and such. We don't need weights, however, and don't want to make the user include them in their new dataset. Thus we build the call up the way it is done in coxph itself, but only keeping the newdata argument. Note that terms2 may have fewer variables than the original model: no cluster and if type!= expected no response. If the original model had a strata, but newdata does not, we need to remove the strata from xlev to stop a spurious warning message. <>= if (!missing(newdata)) { use.x <- TRUE #we do use an X matrix later tcall <- Call[c(1, match(c("newdata", "collapse"), names(Call), nomatch=0))] names(tcall)[2] <- 'data' #rename newdata to data tcall$formula <- Terms2 #version with no response tcall$na.action <- na.action #always present, since there is a default tcall[[1L]] <- quote(stats::model.frame) # change the function called if (!is.null(attr(Terms, "specials")$strata) && !has.strata) { temp.lev <- object$xlevels temp.lev[[strat.term$vars]] <- NULL tcall$xlev <- temp.lev } else tcall$xlev <- object$xlevels mf2 <- eval(tcall, parent.frame()) collapse <- model.extract(mf2, "collapse") n2 <- nrow(mf2) if (has.strata) { if (length(strat.term$vars)==1) newstrat <- mf2[[strat.term$vars]] else newstrat <- strata(mf2[,strat.term$vars], shortlabel=TRUE) if (any(is.na(match(newstrat, oldstrat)))) stop("New data has a strata not found in the original model") else newstrat <- factor(newstrat, levels=levels(oldstrat)) #give it all if (length(strat.term$terms)) newx <- model.matrix(Terms2[-strat.term$terms], mf2, contr=object$contrasts)[,-1,drop=FALSE] else newx <- model.matrix(Terms2, mf2, contr=object$contrasts)[,-1,drop=FALSE] } else { newx <- model.matrix(Terms2, mf2, contr=object$contrasts)[,-1,drop=FALSE] newstrat <- rep(0L, nrow(mf2)) } newoffset <- model.offset(mf2) if (is.null(newoffset)) newoffset <- 0 if (type== 'expected') { newy <- model.response(mf2) if (attr(newy, 'type') != attr(y, 'type')) stop("New data has a different survival type than the model") } na.action.used <- attr(mf2, 'na.action') } else n2 <- n @ %\subsection{Expected hazard} When we do not need standard errors the computation of expected hazard is very simple since the martingale residual is defined as status - expected. The 0/1 status is saved as the last column of $y$. <>= if (missing(newdata)) pred <- y[,ncol(y)] - object$residuals if (!missing(newdata) || se.fit) { <> } if (survival) { #it actually was type= survival, do one more step if (se.fit) se <- se * exp(-pred) pred <- exp(-pred) # probablility of being in state 0 } @ The more general case makes use of the [agsurv] routine to calculate a survival curve for each strata. The routine is defined in the section on individual Cox survival curves. The code here closely matches that. The routine only returns values at the death times, so we need approx to get a complete index. One non-obvious, but careful choice is to use the residuals for the predicted value instead of the compuation below, whenever operating on the original data set. This is a consequence of the Efron approx. When someone in a new data set has exactly the same time as one of the death times in the original data set, the code below implicitly makes them the ``last'' death in the set of tied times. The Efron approx puts a tie somewhere in the middle of the pack. This is way too hard to work out in the code below, but thankfully the original Cox model already did it. However, it does mean that a different answer will arise if you set newdata = the original coxph data set. Standard errors have the same issue, but 1. they are hardly used and 2. the original coxph doesn't do that calculation. So we do what's easiest. <>= ustrata <- unique(oldstrat) risk <- exp(object$linear.predictors) x <- x - rep(object$means, each=nrow(x)) #subtract from each column if (missing(newdata)) #se.fit must be true se <- double(n) else { pred <- se <- double(nrow(mf2)) newx <- newx - rep(object$means, each=nrow(newx)) newrisk <- c(exp(newx %*% object$coef) + newoffset) } survtype<- ifelse(object$method=='efron', 3,2) for (i in ustrata) { indx <- which(oldstrat == i) afit <- agsurv(y[indx,,drop=F], x[indx,,drop=F], weights[indx], risk[indx], survtype, survtype) afit.n <- length(afit$time) if (missing(newdata)) { # In this case we need se.fit, nothing else j1 <- approx(afit$time, 1:afit.n, y[indx,1], method='constant', f=0, yleft=0, yright=afit.n)$y chaz <- c(0, afit$cumhaz)[j1 +1] varh <- c(0, cumsum(afit$varhaz))[j1 +1] xbar <- rbind(0, afit$xbar)[j1+1,,drop=F] if (ncol(y)==2) { dt <- (chaz * x[indx,]) - xbar se[indx] <- sqrt(varh + rowSums((dt %*% object$var) *dt)) * risk[indx] } else { j2 <- approx(afit$time, 1:afit.n, y[indx,2], method='constant', f=0, yleft=0, yright=afit.n)$y chaz2 <- c(0, afit$cumhaz)[j2 +1] varh2 <- c(0, cumsum(afit$varhaz))[j2 +1] xbar2 <- rbind(0, afit$xbar)[j2+1,,drop=F] dt <- (chaz * x[indx,]) - xbar v1 <- varh + rowSums((dt %*% object$var) *dt) dt2 <- (chaz2 * x[indx,]) - xbar2 v2 <- varh2 + rowSums((dt2 %*% object$var) *dt2) se[indx] <- sqrt(v2-v1)* risk[indx] } } else { #there is new data use.x <- TRUE indx2 <- which(newstrat == i) j1 <- approx(afit$time, 1:afit.n, newy[indx2,1], method='constant', f=0, yleft=0, yright=afit.n)$y chaz <-c(0, afit$cumhaz)[j1+1] pred[indx2] <- chaz * newrisk[indx2] if (se.fit) { varh <- c(0, cumsum(afit$varhaz))[j1+1] xbar <- rbind(0, afit$xbar)[j1+1,,drop=F] } if (ncol(y)==2) { if (se.fit) { dt <- (chaz * newx[indx2,]) - xbar se[indx2] <- sqrt(varh + rowSums((dt %*% object$var) *dt)) * newrisk[indx2] } } else { j2 <- approx(afit$time, 1:afit.n, newy[indx2,2], method='constant', f=0, yleft=0, yright=afit.n)$y chaz2 <- approx(-afit$time, afit$cumhaz, -newy[indx2,2], method="constant", rule=2, f=0)$y chaz2 <-c(0, afit$cumhaz)[j2+1] pred[indx2] <- (chaz2 - chaz) * newrisk[indx2] if (se.fit) { varh2 <- c(0, cumsum(afit$varhaz))[j1+1] xbar2 <- rbind(0, afit$xbar)[j1+1,,drop=F] dt <- (chaz * newx[indx2,]) - xbar dt2 <- (chaz2 * newx[indx2,]) - xbar2 v2 <- varh2 + rowSums((dt2 %*% object$var) *dt2) v1 <- varh + rowSums((dt %*% object$var) *dt) se[indx2] <- sqrt(v2-v1)* risk[indx2] } } } } @ %\subsection{Linear predictor, risk, and terms} For these three options what is returned is a \emph{relative} prediction which compares each observation to the average for the data set. Partly this is practical. Say for instance that a treatment covariate was coded as 0=control and 1=treatment. If the model were refit using a new coding of 3=control 4=treatment, the results of the Cox model would be exactly the same with respect to coefficients, variance, tests, etc. The raw linear predictor $X\beta$ however would change, increasing by a value of $3\beta$. The relative predictor \begin{equation} \eta_i = X_i\beta - (1/n)\sum_j X_j\beta \label{eq:eta} \end{equation} will stay the same. The second reason for doing this is that the Cox model is a relative risks model rather than an absolute risks model, and thus relative predictions are almost certainly what the user was thinking of. When the fit was for a stratified Cox model more care is needed. For instance assume that we had a fit that was stratified by sex with covaritate $x$, and a second data set were created where for the females $x$ is replaced by $x+3$. The Cox model results will be unchanged for the two models, but the `normalized' linear predictors $(x - \overline x)'\beta$ %` will not be the same. This reflects a more fundamental issue that the for a stratified Cox model relative risks are well defined only \emph{within} a stratum, i.e. for subject pairs that share a common baseline hazard. The example above is artificial, but the problem arises naturally whenever the model includes a strata by covariate interaction. So for a stratified Cox model the predictions should be forced to sum to zero within each stratum, or equivalently be made relative to the weighted mean of the stratum. Unfortunately, this important issue was not realized until late in 2009 when a puzzling query was sent to the author involving the results from such an interaction. Note that this issue did not arise with type='expected', which has a natural scaling. An offset variable, if specified, is treated like any other covariate with respect to centering. The logic for this choice is not as compelling, but it seemed the best that I could do. Note that offsets play no role whatever in predicted terms, only in the lp and risk. Start with the simple ones <>= if (is.null(object$coefficients)) coef<-numeric(0) else { # Replace any NA coefs with 0, to stop NA in the linear predictor coef <- ifelse(is.na(object$coefficients), 0, object$coefficients) } if (missing(newdata)) { offset <- offset - mean(offset) if (has.strata && reference=="strata") { # We can't use as.integer(oldstrat) as an index, if oldstrat is # a factor variable with unrepresented levels as.integer could # give 1,2,5 for instance. xmeans <- rowsum(x*weights, oldstrat)/c(rowsum(weights, oldstrat)) newx <- x - xmeans[match(oldstrat,row.names(xmeans)),] } else if (use.x) { if (reference == "zero") newx <- x else newx <- x - rep(object$means, each=nrow(x)) } } else { offset <- newoffset - mean(offset) if (has.strata && reference=="strata") { xmeans <- rowsum(x*weights, oldstrat)/c(rowsum(weights, oldstrat)) newx <- newx - xmeans[match(newstrat, row.names(xmeans)),] } else if (reference!= "zero") newx <- newx - rep(object$means, each=nrow(newx)) } if (type=='lp' || type=='risk') { if (use.x) pred <- drop(newx %*% coef) + offset else pred <- object$linear.predictors if (se.fit) se <- sqrt(rowSums((newx %*% object$var) *newx)) if (type=='risk') { pred <- exp(pred) if (se.fit) se <- se * sqrt(pred) # standard Taylor series approx } } @ The type=terms residuals are a bit more work. In Splus this code used the Build.terms function, which was essentially the code from predict.lm extracted out as a separate function. As of March 2010 (today) a check of the Splus function and the R code for predict.lm revealed no important differences. A lot of the bookkeeping in both is to work around any possible NA coefficients resulting from a singularity. The basic formula is to \begin{enumerate} \item If the model has an intercept, then sweep the column means out of the X matrix. We've already done this. \item For each term separately, get the list of coefficients that belong to that term; call this list \code{tt}. \item Restrict $X$, $\beta$ and $V$ (the variance matrix) to that subset, then the linear predictor is $X\beta$ with variance matrix $X V X'$. The standard errors are the square root of the diagonal of this latter matrix. This can be computed, as colSums((X %*% V) * X)). \end{enumerate} Note that the \code{assign} component of a coxph object is the same as that found in Splus models (a list), most R models retain a numeric vector which contains the same information but it is not as easily used. The first first part of predict.lm in R rebuilds the list form as its \code{asgn} variable. I can skip this part since it is already done. <>= else if (type=='terms') { asgn <- object$assign nterms<-length(asgn) pred<-matrix(ncol=nterms,nrow=NROW(newx)) dimnames(pred) <- list(rownames(newx), names(asgn)) if (se.fit) se <- pred for (i in 1:nterms) { tt <- asgn[[i]] tt <- tt[!is.na(object$coefficients[tt])] xtt <- newx[,tt, drop=F] pred[,i] <- xtt %*% object$coefficient[tt] if (se.fit) se[,i] <- sqrt(rowSums((xtt %*% object$var[tt,tt]) *xtt)) } pred <- pred[,terms, drop=F] if (se.fit) se <- se[,terms, drop=F] attr(pred, 'constant') <- sum(object$coefficients*object$means, na.rm=T) } @ To finish up we need to first expand out any missings in the result based on the na.action, and optionally collapse the results within a subject. What should we do about the standard errors when collapse is specified? We assume that the individual pieces are independent and thus var(sum) = sum(variances). The statistical justification of this is quite solid for the linear predictor, risk and terms type of prediction due to independent increments in a martingale. For expecteds the individual terms are positively correlated so the se will be too small. One solution would be to refuse to return an se in this case, but the the bias should usually be small, and besides it would be unkind to the user. Prediction of type='terms' is expected to always return a matrix, or the R termplot() function gets unhappy. <>= if (type != 'terms') { pred <- drop(pred) if (se.fit) se <- drop(se) } if (!is.null(na.action.used)) { pred <- napredict(na.action.used, pred) if (is.matrix(pred)) n <- nrow(pred) else n <- length(pred) if(se.fit) se <- napredict(na.action.used, se) } if (!missing(collapse) && !is.null(collapse)) { if (length(collapse) != n2) stop("Collapse vector is the wrong length") pred <- rowsum(pred, collapse) # in R, rowsum is a matrix, always if (se.fit) se <- sqrt(rowsum(se^2, collapse)) if (type != 'terms') { pred <- drop(pred) if (se.fit) se <- drop(se) } } if (se.fit) list(fit=pred, se.fit=se) else pred @ survival/noweb/tmerge.Rnw0000644000176200001440000007405314006146725015230 0ustar liggesusers\section{tmerge} The tmerge function was designed around a set of specific problems. The idea is to build up a time dependent data set one endpoint at at time. The primary arguments are \begin{itemize} \item data1: the base data set that will be added onto \item data2: the source for new information \item id: the subject identifier in the new data \item \ldots: additional arguments that add variables to the data set \item tstart, tstop: used to set the time range for each subject \item options \end{itemize} The created data set has three new variables (at least), which are \code{id}, \code{tstart} and \code{tstop}. The key part of the call are the ``\ldots'' arguments which each can be one of four types: tdc() and cumtdc() add a time dependent variable, event() and cumevent() add a new endpoint. In the survival routines time intervals are open on the left and closed on the right, i.e., (tstart, tstop]. Time dependent covariates apply from the start of an interval and events occur at the end of an interval. If a data set already had intervals of (0,10] and (10, 14] a new time dependent covariate or event at time 8 would lead to three intervals of (0,8], (8,10], and (10,14]; the new time-dependent covariate value would be added to the second interval, a new event would be added to the first one. A typical call would be <>= newdata <- tmerge(newdata, old, id=clinic, diabetes=tdc(diab.time)) @ which would add a new time dependent covariate \code{diabetes} to the data set. <>= tmerge <- function(data1, data2, id, ..., tstart, tstop, options) { Call <- match.call() # The function wants to recognize special keywords in the # arguments, so define a set of functions which will be used to # mark objects new <- new.env(parent=parent.frame()) assign("tdc", function(time, value=NULL, init=NULL) { x <- list(time=time, value=value, default= init); class(x) <- "tdc"; x}, envir=new) assign("cumtdc", function(time, value=NULL, init=NULL) { x <- list(time=time, value=value, default= init); class(x) <-"cumtdc"; x}, envir=new) assign("event", function(time, value=NULL, censor=NULL) { x <- list(time=time, value=value, censor=censor); class(x) <-"event"; x}, envir=new) assign("cumevent", function(time, value=NULL, censor=NULL) { x <- list(time=time, value=value, censor=censor); class(x) <-"cumevent"; x}, envir=new) if (missing(data1) || missing(data2) || missing(id)) stop("the data1, data2, and id arguments are required") if (!inherits(data1, "data.frame")) stop("data1 must be a data frame") <> <> <> } <> @ The program can't use formulas because the \ldots arguments need to be named. This results in a bit of evaluation magic to correctly assess arguments. The routine below could have been set out as a separate top-level routine, the argument is where we want to document it: within the tmerge page or on a separate one. I decided on the former. <>= tmerge.control <- function(idname="id", tstartname="tstart", tstopname="tstop", delay =0, na.rm=TRUE, tdcstart=NA_real_, ...) { extras <- list(...) if (length(extras) > 0) stop("unrecognized option(s):", paste(names(extras), collapse=', ')) if (length(idname) != 1 || make.names(idname) != idname) stop("idname option must be a valid variable name") if (!is.null(tstartname) && (length(tstartname) !=1 || make.names(tstartname) != tstartname)) stop("tstart option must be NULL or a valid variable name") if (length(tstopname) != 1 || make.names(tstopname) != tstopname) stop("tstop option must be a valid variable name") if (length(delay) !=1 || !is.numeric(delay) || delay < 0) stop("delay option must be a number >= 0") if (length(na.rm) !=1 || ! is.logical(na.rm)) stop("na.rm option must be TRUE or FALSE") if (length(tdcstart) !=1) stop("tdcstart must be a single value") list(idname=idname, tstartname=tstartname, tstopname=tstopname, delay=delay, na.rm=na.rm, tdcstart=tdcstart) } if (!inherits(data1, "tmerge") && !is.null(attr(data1, "tname"))) { # old style object that someone saved! tm.retain <- list(tname = attr(data1, "tname"), tevent= list(name=attr(data1, "tevent"), censor= attr(data1, "tcensor")), tdcvar = attr(data1, "tdcvar"), n = nrow(data1)) attr(data1, "tname") <- attr(data1, "tevent") <- NULL attr(data1, "tcensor") <- attr(data1, "tdcvar") <- NULL attr(data1, "tm.retain") <- tm.retain class(data1) <- c("tmerge", class(data1)) } if (inherits(data1, "tmerge")) { tm.retain <- attr(data1, "tm.retain") firstcall <- FALSE # check out whether the object looks legit: # has someone tinkered with it? This won't catch everything tname <- tm.retain$tname tevent <- tm.retain$tevent tdcvar <- tm.retain$tdcvar if (nrow(data1) != tm.retain$n) stop("tmerge object has been modified, size") if (any(is.null(match(unlist(tname), names(data1)))) || any(is.null(match(tm.retain$tcdname, names(data1)))) || any(is.null(match(tevent$name, names(data1))))) stop("tmerge object has been modified, missing variables") for (i in seq(along=tevent$name)) { ename <- tevent$name[i] if (is.numeric(data1[[ename]])) { if (!is.numeric(tevent$censor[[i]])) stop("event variable ", ename, " no longer matches it's original class") } else if (is.character(data1[[ename]])) { if (!is.character(tevent$censor[[i]])) stop("event variable ", ename, " no longer matches it's original class") } else if (is.logical(data1[[ename]])) { if (!is.logical(tevent$censor[[i]])) stop("event variable ", ename, " no longer matches it's original class") } else if (is.factor(data1[[ename]])) { if (levels(data1[[ename]])[1] != tevent$censor[[i]]) stop("event variable ", ename, " has a new first level") } else stop("event variable ", ename, " is of an invalid class") } } else { firstcall <- TRUE tname <- tevent <- tdcvar <- NULL if (is.name(Call[["id"]])) { idx <- as.character(Call[["id"]]) if (missing(options)) options <-list(idname= idx) else if (is.null(options$idname)) options$idname <- idx } } if (!missing(options)) { if (!is.list(options)) stop("options must be a list") if (!is.null(tname)) { # If an option name matches one already in tname, don't confuse # the tmerge.control routine with duplicate arguments temp <- match(names(options), names(tname), nomatch=0) topt <- do.call(tmerge.control, c(options, tname[temp==0])) if (any(temp >0)) { # A variable name is changing midstream, update the # variable names in data1 varname <- tname[c("idname", "tstartname", "tstopname")] temp2 <- match(varname, names(data1)) names(data1)[temp2] <- varname } } else topt <- do.call(tmerge.control, options) } else if (length(tname)) topt <- do.call(tmerge.control, tname) else topt <- tmerge.control() # id, tstart, tstop are found in data2 if (missing(id)) stop("the id argument is required") if (missing(data1) || missing(data2)) stop("two data sets are required") id <- eval(Call[["id"]], data2, enclos=emptyenv()) #don't find it elsewhere if (is.null(id)) stop("id variable not found in data2") if (any(is.na(id))) stop("id variable cannot have missing values") if (firstcall) { if (!missing(tstop)) { tstop <- eval(Call[["tstop"]], data2) if (length(tstop) != length(id)) stop("tstop and id must be the same length") # The neardate routine will check for legal tstop data type } if (!missing(tstart)) { tstart <- eval(Call[["tstart"]], data2) if (length(tstart)==1) tstart <- rep(tstart, length(id)) if (length(tstart) != length(id)) stop("tstart and id must be the same length") if (any(tstart >= tstop)) stop("tstart must be < tstop") } } else { if (!missing(tstart) || !missing(tstop)) stop("tstart and tstop arguments only apply to the first call") } @ Get the \ldots arguments. They are evaluated in a special frame, set up earlier, so that the definitions of the functions tdc, cumtdc, event, and cumevent are local to tmerge. Check that they are all legal: each argument is named, and is one of the four allowed types. <>= # grab the... arguments notdot <- c("data1", "data2", "id", "tstart", "tstop", "options") dotarg <- Call[is.na(match(names(Call), notdot))] dotarg[[1]] <- as.name("list") # The as-yet dotarg arguments if (missing(data2)) args <- eval(dotarg, envir=new) else args <- eval(dotarg, data2, enclos=new) argclass <- sapply(args, function(x) (class(x))[1]) argname <- names(args) if (any(argname== "")) stop("all additional argments must have a name") check <- match(argclass, c("tdc", "cumtdc", "event", "cumevent")) if (any(is.na(check))) stop(paste("argument(s)", argname[is.na(check)], "not a recognized type")) @ The tcount matrix keeps track of what we have done, and is added to the final object at the end. This is useful to the user for debugging what may have gone right or wrong in their usage. <>= # The tcount matrix is useful for debugging tcount <- matrix(0L, length(argname), 9) dimnames(tcount) <- list(argname, c("early","late", "gap", "within", "boundary", "leading", "trailing", "tied", "missid")) tcens <- tevent$censor tevent <- tevent$name if (is.null(tcens)) tcens <- vector('list', 0) @ The very first call to the routine is special, since this is when the range of legal times is set. We also apply an initial sort to the data if necessary so that times are in order. There are 2 cases: \begin{enumerate} \item Adding a time range: tstop comes from data2, optional tstart, and the id can be simply matched, by which we mean no duplicates in data1. \item The more common case: there is no tstop, one observation per subject, and the first optional argument is an event or cumevent. We then use its time as the range. \end{enumerate} One thing we could add, but didn't, was to warn if any of the three new variables will stomp on ones already in data1. Note that in case 2 we cannot wait for the later code to deal with duplicate id/time pairs, since that later code requires a valid starting point. That code will work out which of a duplicate should be retained, however. <>= newdata <- data1 #make a copy if (firstcall) { # We don't look for topt$id. What if the user had id=clinic, but their # starting data set also had a variable named "id". We want clinic for # this first call. idname <- Call[["id"]] if (!is.name(idname)) stop("on the first call 'id' must be a single variable name") # The line below finds tstop and tstart variables in data1 indx <- match(c(topt$idname, topt$tstartname, topt$tstopname), names(data1), nomatch=0) if (any(indx[1:2]>0) && FALSE) { # warning currently turned off. Be chatty? overwrite <- c(topt$tstartname, topt$tstopname)[indx[2:3]] warning("overwriting data1 variables", paste(overwrite, collapse=' ')) } temp <- as.character(idname) if (!is.na(match(temp, names(data1)))) { data1[[topt$idname]] <- data1[[temp]] baseid <- data1[[temp]] } else stop("id variable not found in data1") if (any(duplicated(baseid))) stop("for the first call (that establishes the time range) data1 must have no duplicate identifiers") if (missing(tstop)) { if (length(argclass)==0 || argclass[1] != "event") stop("neither a tstop argument nor an initial event argument was found") # this is case 2 -- the first time value for each obs sets the range last <- !duplicated(id) indx2 <- match(unique(id[last]), baseid) if (any(is.na(indx2))) stop("setting the range, and data2 has id values not in data1") if (any(is.na(match(baseid, id)))) stop("setting the range, and data1 has id values not in data2") newdata <- data1[indx2,] tstop <- (args[[1]]$time)[last] } else { if (length(baseid)== length(id) && all(baseid == id)) newdata <- data1 else { # Note: 'id' is the idlist for data 2 indx2 <- match(id, baseid) if (any(is.na(indx2))) stop("setting the range, and data2 has id values not in data1") if (any(is.na(match(baseid, id)))) stop("setting the range, and data1 has id values not in data2") newdata <- data1[indx2,] } } if (any(is.na(tstop))) stop("missing time value, when that variable defines the span") if (missing(tstart)) { indx <- which(tstop <=0) if (length(indx) >0) stop("found an ending time of ", tstop[indx[1]], ", the default starting time of 0 is invalid") tstart <- rep(0, length(tstop)) } if (any(tstart >= tstop)) stop("tstart must be < tstop") newdata[[topt$tstartname]] <- tstart newdata[[topt$tstopname]] <- tstop n <- nrow(newdata) if (any(duplicated(id))) { # sort by time within id indx1 <- match(id, unique(id)) newdata <- newdata[order(indx1, tstop),] } temp <- newdata[[topt$idname]] if (any(tstart >= tstop)) stop("tstart must be < tstop") if (any(newdata$tstop[-n] > newdata$tstart[-1] & temp[-n] == temp[-1])) stop("first call has created overlapping or duplicated time intervals") idmiss <- 0 # the tcount table should have a zero } else { #not a first call idmatch <- match(id, data1[[topt$idname]], nomatch=0) if (any(idmatch==0)) idmiss <- sum(idmatch==0) else idmiss <- 0 } @ Now for the real work. For each additional argument we first match the id/time pairs of the new data to the current data set, and categorize each into a type. If the time value in data2 is NA, then that addition is skipped. Ditto if the value is NA and options narm=TRUE. This is a convenience for the user, who will often be merging in a variable like ``day of first diabetes diagnosis'' which is missing for those who never had that outcome occur. <>= saveid <- id for (ii in seq(along.with=args)) { argi <- args[[ii]] baseid <- newdata[[topt$idname]] dstart <- newdata[[topt$tstartname]] dstop <- newdata[[topt$tstopname]] argcen <- argi$censor # if an event time is missing then skip that obs. Also toss obs that # whose id does not match anyone in data1 etime <- argi$time if (idmiss ==0) keep <- rep(TRUE, length(etime)) else keep <- (idmatch > 0) if (length(etime) != length(saveid)) stop("argument ", argname[ii], " is not the same length as id") if (!is.null(argi$value)) { if (length(argi$value) != length(saveid)) stop("argument ", argname[ii], " is not the same length as id") if (topt$na.rm) keep <- keep & !(is.na(etime) | is.na(argi$value)) else keep <- keep & !is.na(etime) if (!all(keep)) { etime <- etime[keep] argi$value <- argi$value[keep] } } else { keep <- keep & !is.na(etime) etime <- etime[keep] } id <- saveid[keep] # Later steps become easier if we sort the new data by id and time # The match() is critical when baseid is not in sorted order. The # etime part of the sort will change from one ii value to the next. indx <- order(match(id, baseid), etime) id <- id[indx] etime <- etime[indx] if (!is.null(argi$value)) yinc <- argi$value[indx] else yinc <- NULL # indx1 points to the closest start time in the baseline data (data1) # that is <= etime. indx2 to the closest end time that is >=etime. # If etime falls into a (tstart, tstop) interval, indx1 and indx2 # will match # If the "delay" argument is set and this event is of type tdc, then # move any etime that is after the entry time for a subject. if (topt$delay >0 && argclass[ii] %in% c("tdc", "cumtdc")) { mintime <- tapply(dstart, baseid, min) index <- match(id, names(mintime)) etime <- ifelse(etime <= mintime[index], etime, etime+ topt$delay) } indx1 <- neardate(id, baseid, etime, dstart, best="prior") indx2 <- neardate(id, baseid, etime, dstop, best="after") # The event times fall into one of 5 categories # 1. Before the first interval # 2. After the last interval # 3. Outside any interval but with time span, i.e, it falls into # a gap in follow-up # 4. Strictly inside an interval (does't touch either end) # 5. Inside an interval, but touching. itype <- ifelse(is.na(indx1), 1, ifelse(is.na(indx2), 2, ifelse(indx2 > indx1, 3, ifelse(etime== dstart[indx1] | etime== dstop[indx2], 5, 4)))) # Subdivide the events that touch on a boundary # 1: intervals of (a,b] (b,d], new count at b "tied edge" # 2: intervals of (a,b] (c,d] with c>b, new count at c, "front edge" # 3: intervals of (a,b] (c,d] with c>b, new count at b, "back edge" # subtype <- ifelse(itype!=5, 0, ifelse(indx1 == indx2+1, 1, ifelse(etime==dstart[indx1], 2, 3))) tcount[ii,1:7] <- table(factor(itype+subtype, levels=c(1:4, 6:8))) # count ties. id and etime are not necessarily sorted tcount[ii,8] <- sum(tapply(etime, id, function(x) sum(duplicated(x)))) tcount[ii,9] <- idmiss <> } @ A \code{tdc} or \code{cumtdc} operator defines a new time-dependent variable which applies to all future times. Say that we had the following scenario for one subject \begin{center} \begin{tabular}{rr|rr} \multicolumn{2}{c}{current} & \multicolumn{2}{c}{addition} \\ tstart & tstop & time & x \\ 2 & 5 & 1 & 20.2 \\ 6 & 7 & 7 & 11 \\ 7 & 15 & 8 & 17.3 \\ 15 & 30 \\ \end{tabular} \end{center} The resulting data set will have intervals of (2,5), (6,7), (7,8) and (8,15) with covariate values of 20.2, 20.2, 11, and 17.3. Only a covariate change that occurs within an interval causes a new data row. Covariate changes that happen after the last interval are ignored, i.e. at change at time $\ge 30$ in the above example. If instead this had been events at times 1, 7, and 8, the first event would be ignored since it happens outside of any interval, so would an event at exactly time 2. The event at time 7 would be recorded in the (6,7) interval and the one at time 8 in the (7,8) interval: events happen at the ends of intervals. In both cases new rows are only generated for new time values that fall strictly within one of the old intervals. When a subject has two increments on the same day the later one wins. This is correct behavior for cumtdc, a bit odd for cumevent, and the user's problem for tdc and event. We report back the number of ties so that the user can deal with it. Where are we now with the variables? \begin{center} \begin{tabular}{cccc} itype& class & indx1 & indx2 \\ \hline 1 & before & NA & next interval \\ 2 & after & prior interval & NA \\ 3 & in a gap & prior interval & next interval \\ 4 & within interval & containing interval & containing interval \\ 5-1 & on a join & next interval & prior interval \\ 5-2 & front edge & containing & containing \\ 5-3 & back edge & containing & containing \\ \end{tabular} \end{center} If there are any itype 4, start by expanding the data set to add new cut points, which will turn all the 4's into 5-1 types. When expanding, all the event type variables turn into ``censor'' at the newly added times and other variables stay the same. A subject could have more than one new cutpoint added within an interval so we have to count each. In newdata all the rows for a given subject are contiguous and in time order, though the data set may not be in subject order. <>= indx4 <- which(itype==4) n4 <- length(indx4) if (n4 > 0) { # we need to eliminate duplicate times within the same id, but # do so without changing the class of etime: it might # be a Date, an integer, a double, ... # Using unique on a data.frame does the trick icount <- data.frame(irow= indx1[indx4], etime=etime[indx4]) icount <- unique(icount) # the icount data frame will be sorted by second column within first # so rle is faster than table n.add <- rle(icount$irow)$length # number of rows to add for each id # expand the data irep <- rep.int(1L, nrow(newdata)) erow <- unique(indx1[indx4]) # which rows in newdata to be expanded irep[erow] <- 1+ n.add # number of rows in new data jrep <- rep(1:nrow(newdata), irep) #stutter the duplicated rows newdata <- newdata[jrep,] #expand it out dstart <- dstart[jrep] dstop <- dstop[jrep] #fix up times nfix <- length(erow) temp <- vector("list", nfix) iend <- (cumsum(irep))[irep >1] #end row of each duplication set for (j in 1:nfix) temp[[j]] <- -(seq(n.add[j] -1, 0)) + iend[j] newrows <- unlist(temp) dstart[newrows] <- dstop[newrows-1] <- icount$etime newdata[[topt$tstartname]] <- dstart newdata[[topt$tstopname]] <- dstop for (ename in tevent) newdata[newrows-1, ename] <- tcens[[ename]] # refresh indices baseid <- newdata[[topt$idname]] indx1 <- neardate(id, baseid, etime, dstart, best="prior") indx2 <- neardate(id, baseid, etime, dstop, best="after") subtype[itype==4] <- 1 #all the "insides" are now on a tied edge itype[itype==4] <- 5 } @ Now we can add the new variable. The most common is a tdc, so start with it. The C routine returns a set of indices: 0,1,1,2,3,0,4,... would mean that row 1 of the new data happens before the tdc variable, 2 and 3 take values from the first element of yinc, etc. By returning an index, the yinc variable can be of any data type. Using is.na() on the left side below causes the \emph{right} kind of NA to be inserted (this trick was stolen from the merge routine). If this is a first call, don't allow the new variable to overwrite a variable already existing in the data set, we found it leads to problems. (Usually it is a user mistake.) However, tdc calls themselves can stack. <>= # add a tdc variable newvar <- newdata[[argname[ii]]] # prior value (for sequential tmerge calls) if (argclass[ii] %in% c("tdc", "cumtdc")){ if (argname[[ii]] %in% tevent) stop("attempt to turn event variable", argname[[ii]], "into a tdc") if (!(argname[[ii]] %in% tdcvar)){ tdcvar <- c(tdcvar, argname[[ii]]) if (!is.null(newvar) && argclass[ii] == "tdc") { warning(paste0("replacement of variable '", argname[ii], "'")) newvar <- NULL } } } if (argclass[ii] == "tdc") { default <- argi$default # default value if (is.null(default)) default <- topt$tdcstart else if (length(default) !=1) stop("initial tdc value must be of length 1") # id can be any data type; feed integers to the C routine storage.mode(dstart) <- storage.mode(etime) <- "double" #if time is integer uid <- unique(baseid) index <- .Call(Ctmerge2, match(baseid, uid), dstart, match(id, uid), etime) if (!is.null(yinc)) newvar <- NULL # a tdc can't be updated, other than 0/1 if (is.null(newvar)) { if (is.null(yinc)) newvar <- ifelse(index==0, 0L, 1L) #add a 0/1 variable else { newvar <- yinc[pmax(1L, index)] if (any(index==0)) { if (is.na(default)) is.na(newvar) <- (index==0L) else { if (is.numeric(newvar)) newvar[index==0L] <- as.numeric(default) else { if (is.factor(newvar)) { # special case: if default isn't in the set of levels, # add it to the levels if (is.na(match(default, levels(newvar)))) levels(newvar) <- c(levels(newvar), default) } newvar[index== 0L] <- default } } } } } else { # update a 0/1 variable if (is.integer(newvar) && all(newvar==0L | newvar==1L)) newvar[index!=0L] <- 1L else stop("tdc update does not match prior variable type: ", argname[ii]) } tdcvar <- unique(c(tdcvar, argname[[ii]])) } @ Events and cumevents are easy because each affects only one interval. <>= # add events if (argclass[ii] %in% c("cumtdc", "cumevent")) { if (is.null(yinc)) yinc <- rep(1L, length(id)) else if (is.logical(yinc)) yinc <- as.numeric(yinc) # allow cumulative T/F if (!is.numeric(yinc)) stop("invalid increment for cumtdc or cumevent") } if (argclass[ii] == "cumevent"){ ykeep <- (yinc !=0) # ignore the addition of a censoring event yinc <- unlist(tapply(yinc, match(id, baseid), cumsum)) } if (argclass[ii] %in% c("event", "cumevent")) { if (!is.null(newvar)) { if (!argname[ii] %in% tevent) { #warning(paste0("non-event variable '", argname[ii], "' replaced by an event variable")) newvar <- NULL } else if (!is.null(yinc)) { if (class(newvar) != class(yinc)) stop("attempt to update an event variable with a different type") if (is.factor(newvar) && !all(levels(yinc) %in% levels(newvar))) stop("attemp to update an event variable and levels do not match") } } if (is.null(yinc)) yinc <- rep(1L, length(id)) if (is.null(newvar)) { if (is.numeric(yinc)) newvar <- rep(0L, nrow(newdata)) else if (is.factor(yinc)) newvar <- factor(rep(levels(yinc)[1], nrow(newdata)), levels(yinc)) else if (is.character(yinc)) newvar <- rep('', nrow(newdata)) else if (is.logical(yinc)) newvar <- rep(FALSE, nrow(newdata)) else stop("invalid value for a status variable") } keep <- (subtype==1 | subtype==3) # all other events are thrown away if (argclass[ii] == "cumevent") keep <- (keep & ykeep) newvar[indx2[keep]] <- yinc[keep] # add this into our list of 'this is an event type variable' if (!(argname[ii] %in% tevent)) { tevent <- c(tevent, argname[[ii]]) if (is.factor(yinc)) tcens <- c(tcens, list(levels(yinc)[1])) else if (is.logical(yinc)) tcens <- c(tcens, list(FALSE)) else if (is.character(yinc)) tcens <- c(tcens, list("")) else if (is.integer(yinc)) tcens <- c(tcens, list(0L)) else tcens <- c(tcens, list(0)) names(tcens) <- tevent } } else if (argclass[ii] == "cumtdc") { # process a cumtdc variable # I don't have a good way to catch the reverse of this user error if (argname[[ii]] %in% tevent) stop("attempt to turn event variable", argname[[ii]], "into a cumtdc") keep <- itype != 2 # changes after the last interval are ignored indx <- ifelse(subtype==1, indx1, ifelse(subtype==3, indx2+1L, indx2)) # we want to pass the right kind of NA to the C code default <- argi$default if (is.null(default)) default <- as.numeric(topt$tdcstart) else { if (length(default) != 1) stop("tdc initial value must be of length 1") if (!is.numeric(default)) stop("cumtdc initial value must be numeric") } if (is.null(newvar)) { # not overwriting a prior value if (is.null(argi$value)) newvar <- rep(0.0, nrow(newdata)) else newvar <- rep(default, nrow(newdata)) } # the increment must be numeric if (!is.numeric(newvar)) stop("data and starting value do not agree on data type") # id can be any data type; feed integers to the C routine storage.mode(yinc) <- storage.mode(dstart) <- "double" storage.mode(newvar) <- storage.mode(etime) <- "double" newvar <- .Call(Ctmerge, match(baseid, baseid), dstart, newvar, match(id, baseid)[keep], etime[keep], yinc[keep], indx[keep]) } newdata[[argname[ii]]] <- newvar @ Finish up by adding the attributes and the class <>= tm.retain <- list(tname = topt[c("idname", "tstartname", "tstopname")], n= nrow(newdata)) if (length(tevent)) tm.retain$tevent <- list(name = tevent, censor=tcens) if (length(tdcvar)>0) tm.retain$tdcvar <- tdcvar attr(newdata, "tm.retain") <- tm.retain attr(newdata, "tcount") <- rbind(attr(data1, "tcount"), tcount) attr(newdata, "call") <- Call row.names(newdata) <- NULL #These are a mess; kill them off. # Not that it works: R just assigns new row names. class(newdata) <- c("tmerge", "data.frame") newdata @ The summary routine is for checking: it simply prints out the attributes. <>= summary.tmerge <- function(object, ...) { if (!is.null(cl <- attr(object, "call"))) { cat("Call:\n") dput(cl) cat("\n") } print(attr(object, "tcount")) } # This could be smarter: if you only drop variables that are not known # to tmerge then it would be okay. But I currently like the "touch it # and it dies" philosophy "[.tmerge" <- function(x, ..., drop=TRUE){ class(x) <- "data.frame" attr(x, "tm.retain") <- NULL attr(x, "tcount") <- NULL attr(x, "call") <- NULL NextMethod(x) } @ survival/noweb/pyears.Rnw0000644000176200001440000003521614033733462015246 0ustar liggesusers\section{Person years} The person years routine and the expected survival code are the two parts of the survival package that make use of external rate tables, of which the United States mortality tables \code{survexp.us} and \code{survexp.usr} are examples contained in the package. The arguments for pyears are \begin{description} \item[formula] The model formula. The right hand side consists of grouping variables and is essentially identical to [[survfit]], the result of the model will be a table of results with dimensions determined from the right hand variables. The formula can include an optional [[ratetable]] directive; but this style has been superseded by the [[rmap]] argument. \item [data, weights, subset, na.action] as usual \item[rmap] an optional mapping for rate table variables, see more below. \item[ratetable] the population rate table to use as a reference. This can either be a ratetable object or a previously fitted Cox model \item[scale] Scale the resulting output times, e.g., 365.25 to turn days into years. \item[expect] Should the output table include the expected number of events, or the expected number of person-years of observation? \item[model, x, y] as usual \item[data.frame] if true the result is returned as a data frame, if false as a set of tables. \end{description} <>= pyears <- function(formula, data, weights, subset, na.action, rmap, ratetable, scale=365.25, expect=c('event', 'pyears'), model=FALSE, x=FALSE, y=FALSE, data.frame=FALSE) { <> <> <> } @ Start out with the standard model processing, which involves making a copy of the input call, but keeping only the arguments we want. We then process the special argument [[rmap]]. This is discussed in the section on the [[survexp]] function so we need not repeat the explantation here. <>= expect <- match.arg(expect) Call <- match.call() # create a call to model.frame() that contains the formula (required) # and any other of the relevant optional arguments # then evaluate it in the proper frame indx <- match(c("formula", "data", "weights", "subset", "na.action"), names(Call), nomatch=0) if (indx[1] ==0) stop("A formula argument is required") tform <- Call[c(1,indx)] # only keep the arguments we wanted tform[[1L]] <- quote(stats::model.frame) # change the function called Terms <- if(missing(data)) terms(formula, 'ratetable') else terms(formula, 'ratetable',data=data) if (any(attr(Terms, 'order') >1)) stop("Pyears cannot have interaction terms") rate <- attr(Terms, "specials")$ratetable if (length(rate) >0 || !missing(rmap) || !missing(ratetable)) { has.ratetable <- TRUE if(length(rate) > 1) stop("Can have only 1 ratetable() call in a formula") if (missing(ratetable)) stop("No rate table specified") <> } else has.ratetable <- FALSE mf <- eval(tform, parent.frame()) Y <- model.extract(mf, 'response') if (is.null(Y)) stop ("Follow-up time must appear in the formula") if (!is.Surv(Y)){ if (any(Y <0)) stop ("Negative follow up time") Y <- as.matrix(Y) if (ncol(Y) >2) stop("Y has too many columns") } else { stype <- attr(Y, 'type') if (stype == 'right') { if (any(Y[,1] <0)) stop("Negative survival time") nzero <- sum(Y[,1]==0 & Y[,2] ==1) if (nzero >0) warning(paste(nzero, "observations with an event and 0 follow-up time,", "any rate calculations are statistically questionable")) } else if (stype != 'counting') stop("Only right-censored and counting process survival types are supported") } n <- nrow(Y) if (is.null(n) || n==0) stop("Data set has 0 observations") weights <- model.extract(mf, 'weights') if (is.null(weights)) weights <- rep(1.0, n) @ The next step is to check out the ratetable. For a population rate table a set of consistency checks is done by the [[match.ratetable]] function, giving a set of sanitized indices [[R]]. This function wants characters turned to factors. For a Cox model [[R]] will be a model matix whose covariates are coded in exactly the same way that variables were coded in the original Cox model. We call the model.matrix.coxph function so as not to have to repeat the steps found there (remove cluster statements, etc). <>= # rdata contains the variables matching the ratetable if (has.ratetable) { rdata <- data.frame(eval(rcall, mf), stringsAsFactors=TRUE) if (is.ratetable(ratetable)) { israte <- TRUE rtemp <- match.ratetable(rdata, ratetable) R <- rtemp$R } else if (inherits(ratetable, 'coxph') && !inherits(ratetable, "coxphms")) { israte <- FALSE Terms <- ratetable$terms if (!is.null(attr(Terms, 'offset'))) stop("Cannot deal with models that contain an offset") strats <- attr(Terms, "specials")$strata if (length(strats)) stop("pyears cannot handle stratified Cox models") if (any(names(mf[,rate]) != attr(ratetable$terms, 'term.labels'))) stop("Unable to match new data to old formula") R <- model.matrix.coxph(ratetable, data=rdata) } else stop("Invalid ratetable") } @ Now we process the non-ratetable variables. Those of class [[tcut]] set up time-dependent classes. For these the cutpoints attribute sets the intervals, if there were 4 cutpoints of 1, 5,6, and 10 the 3 intervals will be 1-5, 5-6 and 6-10, and odims will be 3. All other variables are treated as factors. <>= ovars <- attr(Terms, 'term.labels') if (length(ovars)==0) { # no categories! X <- rep(1,n) ofac <- odim <- odims <- ocut <- 1 } else { odim <- length(ovars) ocut <- NULL odims <- ofac <- double(odim) X <- matrix(0, n, odim) outdname <- vector("list", odim) names(outdname) <- attr(Terms, 'term.labels') for (i in 1:odim) { temp <- mf[[ovars[i]]] if (inherits(temp, 'tcut')) { X[,i] <- temp temp2 <- attr(temp, 'cutpoints') odims[i] <- length(temp2) -1 ocut <- c(ocut, temp2) ofac[i] <- 0 outdname[[i]] <- attr(temp, 'labels') } else { temp2 <- as.factor(temp) X[,i] <- temp2 temp3 <- levels(temp2) odims[i] <- length(temp3) ofac[i] <- 1 outdname[[i]] <- temp3 } } } @ Now do the computations. The code above has separated out the variables into 3 groups: \begin{itemize} \item The variables in the rate table. These determine where we \emph{start} in the rate table with respect to retrieving the relevant death rates. For the US table [[survexp.us]] this will be the date of study entry, age (in days) at study entry, and sex of each subject. \item The variables on the right hand side of the model. These are interpreted almost identically to a call to [[table]], with special treatment for those of class \emph{tcut}. \item The response variable, which tells the number of days of follow-up and optionally the status at the end of follow-up. \end{itemize} Start with the rate table variables. There is an oddity about US rate tables: the entry for age (year=1970, age=55) contains the daily rate for anyone who turns 55 in that year, from their birthday forward for 365 days. So if your birthday is on Oct 2, the 1970 table applies from 2Oct 1970 to 1Oct 1971. The underlying C code wants to make the 1970 rate table apply from 1Jan 1970 to 31Dec 1970. The easiest way to finess this is to fudge everyone's enter-the-study date. If you were born in March but entered in April, make it look like you entered in Febuary; that way you get the first 11 months at the entry year's rates, etc. The birth date is entry date - age in days (based on 1/1/1970). The other aspect of the rate tables is that ``older style'' tables, those that have the factor attribute, contained only decennial data which the C code would interpolate on the fly. The value of [[atts$factor]] was 10 indicating that there are 10 years in the interpolation interval. The newer tables do not do this and the C code is passed a 0/1 for continuous (age and year) versus discrete (sex, race). <>= ocut <-c(ocut,0) #just in case it were of length 0 osize <- prod(odims) if (has.ratetable) { #include expected atts <- attributes(ratetable) datecheck <- function(x) inherits(x, c("Date", "POSIXt", "date", "chron")) cuts <- lapply(attr(ratetable, "cutpoints"), function(x) if (!is.null(x) & datecheck(x)) ratetableDate(x) else x) if (is.null(atts$type)) { #old stlye table rfac <- atts$factor us.special <- (rfac >1) } else { rfac <- 1*(atts$type ==1) us.special <- (atts$type==4) } if (any(us.special)) { #special handling for US pop tables if (sum(us.special) > 1) stop("more than one type=4 in a rate table") # Someone born in June of 1945, say, gets the 1945 US rate until their # next birthday. But the underlying logic of the code would change # them to the 1946 rate on 1/1/1946, which is the cutpoint in the # rate table. We fudge by faking their enrollment date back to their # birth date. # # The cutpoint for year has been converted to days since 1/1/1970 by # the ratetableDate function. (Date objects in R didn't exist when # rate tables were conceived.) if (is.null(atts$dimid)) dimid <- names(atts$dimnames) else dimid <- atts$dimid cols <- match(c("age", "year"), dimid) if (any(is.na(cols))) stop("ratetable does not have expected shape") # The format command works for Dates, use it to get an offset bdate <- as.Date("1970-01-01") + (R[,cols[2]] - R[,cols[1]]) byear <- format(bdate, "%Y") offset <- as.numeric(bdate - as.Date(paste0(byear, "-01-01"))) R[,cols[2]] <- R[,cols[2]] - offset # Doctor up "cutpoints" - only needed for (very) old style rate tables # for which the C code does interpolation on the fly if (any(rfac >1)) { temp <- which(us.special) nyear <- length(cuts[[temp]]) nint <- rfac[temp] #intervals to interpolate over cuts[[temp]] <- round(approx(nint*(1:nyear), cuts[[temp]], nint:(nint*nyear))$y - .0001) } } docount <- is.Surv(Y) temp <- .C(Cpyears1, as.integer(n), as.integer(ncol(Y)), as.integer(is.Surv(Y)), as.double(Y), as.double(weights), as.integer(length(atts$dim)), as.integer(rfac), as.integer(atts$dim), as.double(unlist(cuts)), as.double(ratetable), as.double(R), as.integer(odim), as.integer(ofac), as.integer(odims), as.double(ocut), as.integer(expect=='event'), as.double(X), pyears=double(osize), pn =double(osize), pcount=double(if(docount) osize else 1), pexpect=double(osize), offtable=double(1))[18:22] } else { #no expected docount <- as.integer(ncol(Y) >1) temp <- .C(Cpyears2, as.integer(n), as.integer(ncol(Y)), as.integer(docount), as.double(Y), as.double(weights), as.integer(odim), as.integer(ofac), as.integer(odims), as.double(ocut), as.double(X), pyears=double(osize), pn =double(osize), pcount=double(if (docount) osize else 1), offtable=double(1)) [11:14] } @ Create the output object. <>= has.tcut <- any(sapply(mf, function(x) inherits(x, 'tcut'))) if (data.frame) { # Create a data frame as the output, rather than a set of # rate tables if (length(ovars) ==0) { # no variables on the right hand side keep <- TRUE df <- data.frame(pyears= temp$pyears/scale, n = temp$n) } else { keep <- (temp$pyears >0) # what rows to keep in the output # grab prototype rows from the model frame, this preserves class # (unless it is a tcut variable, then we know what to do) tdata <- lapply(1:length(ovars), function(i) { temp <- mf[[ovars[i]]] if (inherits(temp, "tcut")) { #if levels are numeric, return numeric if (is.numeric(outdname[[i]])) outdname[[i]] else factor(outdname[[i]], outdname[[i]]) # else factor } else temp[match(outdname[[i]], temp)] }) tdata$stringsAsFactors <- FALSE # argument for expand.grid df <- do.call("expand.grid", tdata)[keep,,drop=FALSE] names(df) <- ovars df$pyears <- temp$pyears[keep]/scale df$n <- temp$pn[keep] } row.names(df) <- NULL # toss useless 'creation history' if (has.ratetable) df$expected <- temp$pexpect[keep] if (expect=='pyears') df$expected <- df$expected/scale if (docount) df$event <- temp$pcount[keep] # if any of the predictors were factors, make them factors in the output for (i in 1:length(ovars)){ if (is.factor( mf[[ovars[i]]])) df[[ovars[i]]] <- factor(df[[ovars[i]]], levels( mf[[ovars[i]]])) } out <- list(call=Call, data= df, offtable=temp$offtable/scale, tcut=has.tcut) if (has.ratetable && !is.null(rtemp$summ)) out$summary <- rtemp$summ } else if (prod(odims) ==1) { #don't make it an array out <- list(call=Call, pyears=temp$pyears/scale, n=temp$pn, offtable=temp$offtable/scale, tcut = has.tcut) if (has.ratetable) { out$expected <- temp$pexpect if (expect=='pyears') out$expected <- out$expected/scale if (!is.null(rtemp$summ)) out$summary <- rtemp$summ } if (docount) out$event <- temp$pcount } else { out <- list(call = Call, pyears= array(temp$pyears/scale, dim=odims, dimnames=outdname), n = array(temp$pn, dim=odims, dimnames=outdname), offtable = temp$offtable/scale, tcut=has.tcut) if (has.ratetable) { out$expected <- array(temp$pexpect, dim=odims, dimnames=outdname) if (expect=='pyears') out$expected <- out$expected/scale if (!is.null(rtemp$summ)) out$summary <- rtemp$summ } if (docount) out$event <- array(temp$pcount, dim=odims, dimnames=outdname) } out$observations <- nrow(mf) out$terms <- Terms na.action <- attr(mf, "na.action") if (length(na.action)) out$na.action <- na.action if (model) out$model <- mf else { if (x) out$x <- X if (y) out$y <- Y } class(out) <- 'pyears' out @ survival/noweb/survConcordance.Rnw0000644000176200001440000006602513775165444017116 0ustar liggesusers\subsection{Concordance} This is the code for the deprecated function survConcordance. It will be removed in due course. The concordance statistic is gaining popularity as a measure of goodness-of-fit in survival models. Consider all pairs of subjects with $(r_i, r_j)$ as the two risk scores for each pair and $(s_i, s_j)$ the corresponding survival times. The c-statistic is defined by dividing these sets into four groups. \begin{itemize} \item Concordant pairs: for a Cox model this will be pairs where a shorter survival is paired with a larger risk score, e.g. $r_i>r_j$ and $s_i < s_j$ \item Discordant pairs: the lower risk score has a shorter survival \item Tied pairs: there are three common choices \begin{itemize} \item Kendall's tau: any pair where $r_i=r_j$ or $s_i = s_j$ is considered tied. \item AUC: pairs with $r_i=r_j$ are tied; those with $s_i=s_j$ are considered incomparable. This is the definition of the AUC in logisitic regression, and has become the most common choice for Cox models as well. \item Somer's D: All ties are treated as incomparable. \end{itemize} \item Incomparable pairs: For survival this always includes pairs where the survival times cannot be ranked with certainty. For instance $s_i$ is censored at time 10 and $s_j$ is an event (or censor) at time 20. Subject $i$ may or may not survive longer than subject $j$. Note that if $s_i$ is censored at time 10 and $s_j$ is an event at time 10 then $s_i > s_j$. Add onto this those ties that are treated as incomparable.\\ Observations that are in different strata are also incomparable, since the Cox model only compares within strata. \end{itemize} Then the concordance statistic is defined as $(C + T/2)/(C + D + T)$. The denominator is the number of comparable pairs. The program creates 4 variables, which are the number of concordant pairs, discordant, tied on time, and tied on $x$ but not on time. The default concordance is based on the AUC definition, but all 4 values are reported back so that a user can recreate the others if desired. The primary compuational questions is how to do this efficiently, i.e., better that the naive $O(n^2)$ algorithm that loops across all $n(n-1)/2$ possible pairs. There are two key ideas. \begin{enumerate} \item Rearrange the counting so that we do it by death times. For each death we count the number of other subjects in the risk set whose score is higher, lower, or tied and add it into the totals. This also neatly solves the question of time-dependent covariates. \item To count the number higher and lower we need to rank the subjects in the risk set by their scores $r_i$. This can be done in $O(\log n)$ time if the data is kept in a binary tree. \end{enumerate} \begin{figure} \myfig{balance} \caption{A balanced tree of 13 nodes.} \label{treefig} \end{figure} Figure \ref{treefig} shows a balanced binary tree containing 13 risk scores. For each node the left child and all its descendants have a smaller value than the parent, the right child and all its descendents have a larger value. Each node in figure \ref{treefig} is also annotated with the total weight of observations in that node and the weight for all its children (not shown on graph). Assume that the tree shown represents all of the subjects still alive at the time a particular subject ``Smith'' expires, and that Smith has the risk score 2.1. The concordant pairs are all of those with a risk score greater than 2.1, which can be found by traversing the tree from the top down, adding the (parent - child) value each time we branch left (5-3 at the 2.6 node), with a last addition of the right hand child when we find the node with Smith's value (1). %' There are 3 concordant and 12-3=9 discordant pairs. This takes a little less than $\log_2(n)$ steps on average, as compared to an average of $n/2$ for the naive method. The difference can matter when $n$ is large since this traversal must be done for each event. (In the code below we start at Smith's node and walk up.) %' The classic way to store trees is as a linked list. There are several algorithms for adding and subtracting nodes from a tree while maintaining the balance (red-black trees, AA trees, etc) but we take a different approach. Since we need to deal with case weights in the model and we know all the risk score at the outset, the full set of risk scores is organised into a tree at the beginning and node counts are changed to zero as observations are removed. If we index the nodes of the tree as 1 for the top, 2--3 for the next horizontal row, 4--7 for the next, \ldots then the parent-child traversal becomes particularly easy. The parent of node $i$ is $i/2$ (integer arithmetic) and the children of node $i$ are $2i$ and $2i +1$. In C code the indices start at 0 and the children are $2i+1$ and $2i+2$ and the parent is $(i-1)/2$. The following bit of code returns the indices of a sorted list when placed into such a tree. The basic idea is that the rows of the tree start at indices 1, 2, 4, \ldots. For the above tree, the last row will contains the 1st, 3rd, \ldots, 11th smallest ranks. The next row above contains every other value of the ranks \emph{not yet assigned}, and etc to the top of the tree. There is some care to make sure the result is an integer. <>= btree <- function(n) { ranks <- rep(0L, n) #will be overwritten yet.to.do <- 1:n depth <- floor(logb(n,2)) start <- as.integer(2^depth) lastrow.length <- 1+n-start indx <- seq(1L, by=2L, length= lastrow.length) ranks[yet.to.do[indx]] <- start + 0:(length(indx)-1L) yet.to.do <- yet.to.do[-indx] while (start >1) { start <- as.integer(start/2) indx <- seq(1L, by=2L, length=start) ranks[yet.to.do[indx]] <- start + 0:(start-1L) yet.to.do <- yet.to.do[-indx] } ranks } @ Referring again to figure \ref{treefig}, [[btree(13)]] yields the vector [[8 4 9 2 10 5 11 1 12 6 13 3 7]] meaning that the smallest element will be in position 8 of the tree, the next smallest in position 4, etc. Here is a shorter recursive version. It knows the form of trees with 1, 2, or 3 nodes; and builds the others from them. The maximum depth of recursion is $\log_2(n) -1$. It is more clever but a bit slower. (Not that it matters as both take less than 5 seconds for a million elements.) <>= btree <- function(n) { tfun <- function(n, id, power) { if (n==1) id else if (n==2) c(2L *id, id) else if (n==3) c(2L*id, id, 2L*id +1L) else { nleft <- if (n== power*2) power else min(power-1, n-power/2) c(tfun(nleft, 2L *id, power/2), id, tfun(n-(nleft+1), 2L*id +1L, power/2)) } } tfun(n, 1L, 2^(floor(logb(n-1,2)))) } @ A second question is how to compute the variance of the result. The insight used here is to consider a Cox model with time dependent covariates, where the covariate $x$ at each death time has been transformed into ${\rm rank}(x)$. One can show that the Cox score statistic contribution of $r_i - \overline{r}$ at each death time is equal to $(C-D)/2$ where $C$ and $D$ are the number of concordant and discordant pairs comparing that death to all those at risk, and using the Breslow approximation for ties. The contribution to the variance of the score statistic is $V(t) =\sum (r_i - \overline{r})^2 /n$, the $r_i$ being the ranks at that time point and $n$ the number at risk. How can we update this sum using an update formula? First remember the identity \begin{equation*} \sum w_i(x_i - \overline{x})^2 = \sum w_i(x_i-c)^2 - \sum w_i(c - \overline{x})^2 \end{equation*} true for any set of values $x$ and centering constant $c$. For weighted data define the rank of an observation with risk score $r_k$ as \begin{equation*} {\rm rank} = \sum_{r_ik} w_i(r_i - \mu_n)^2 - \sum_{i>k} w_i(r_i - \mu_g)^2 &= (\sum_{i>k} w_i) [(\mu_u -\mu_n)^2 - ((\mu_u-w_k) - \mu_g)^2] \nonumber \\ &= (\sum_{i>k} w_i) (\mu_n + z - 2\mu_u)(\mu_n -z) \label{upper1} \\ &= (\sum_{i>k} w_i) (\mu_n+z - 2\mu_u) (-w_k/2) \label{upper}\\ z&\equiv \mu_g+ w_k \nonumber \end{align} For items of tied rank, their rank increases by the same amount as the overall mean, and so their contribution to the total SS is unchanged. The final part of the update step is to add in the SS contributed by the new observation. An observation is removed from the tree whenver the current time becomes less than the (start, stop] interval of the datum. The ranks for observations of lower risk are unchanged by the removal so equation \eqref{lower1} applies just as before, but with the new mean smaller than the old so the last term in equation \eqref{lower} changes sign. For the observations of higher risk both the mean and the ranks change by $w_k$ and equation \eqref{upper1} holds but with $z=\mu_0- w_k$. We can now define the C-routine that does the bulk of the work. First we give the outline shell of the code and then discuss the parts one by one. This routine is for ordinary survival data, and will be called once per stratum. Input variables are \begin{description} \item[n] the number of observations \item[y] matrix containing the time and status, data is sorted by ascending time, with deaths preceding censorings. \item[indx] the tree node at which this observation's risk score resides %' \item[wt] case weight for the observation \item[sum] scratch space, weights for each node of the tree: 3 values are for the node, all left children, and all right children \item[count] the returned counts of concordant, discordant, tied on x, tied on time, and the variance \end{description} <>= #include "survS.h" SEXP concordance1(SEXP y, SEXP wt2, SEXP indx2, SEXP ntree2) { int i, j, k, index; int child, parent; int n, ntree; double *time, *status; double *twt, *nwt, *count; double vss, myrank, wsum1, wsum2, wsum3; /*sum of wts below, tied, above*/ double lmean, umean, oldmean, newmean; double ndeath; /* weighted number of deaths at this point */ SEXP count2; double *wt; int *indx; n = nrows(y); ntree = asInteger(ntree2); wt = REAL(wt2); indx = INTEGER(indx2); time = REAL(y); status = time + n; PROTECT(count2 = allocVector(REALSXP, 5)); count = REAL(count2); /* count5 contains the information matrix */ twt = (double *) R_alloc(2*ntree, sizeof(double)); nwt = twt + ntree; for (i=0; i< 2*ntree; i++) twt[i] =0.0; for (i=0; i<5; i++) count[i]=0.0; vss=0; <> UNPROTECT(1); return(count2); } @ The key part of our computation is to update the vectors of weights. We don't actually pass the risk score values $r$ into the routine, %' it is enough for each observation to point to the appropriate tree node. The tree contains the for everyone whose survival is larger than the time currently under review, so starts with all weights equal to zero. For any pair of observations $i,j$ we need to add [[wt[i]*wt[j]]] to the appropriate count. Starting at the largest time (which is sorted last), walk through the tree. \begin{itemize} \item If it is a death time, we need to process all the deaths tied at this time. \begin{enumerate} \item Add [[wt[i] * wt[j]]] to the tied-on-time total, for all pairs $i,j$ of tied times. \item The addition to tied-on-r will be the weight of this observation times the sum of weights for all others with the same risk score and a a greater time, i.e., the weight found at [[indx[i]]] in the tree. \item Similarly for those with smaller or larger risk scores. First add in the children of this node. The left child will be smaller risk scores (and longer times) adding to the concordant pairs, the right child discordant. Then walk up the tree to the root. At each step up we add in data for the 'not me' branch. If we were the right branch (even number node) of a parent then when moving up we add in the left branch counts, and vice-versa. \end{enumerate} \item Now add this set of subject weights into the tree. The weight for a node is [[nwt]] and for the node and all its children is [[twt]]. \end{itemize} <>= for (i=n-1; i>=0; ) { ndeath =0; if (status[i]==1) { /* process all tied deaths at this point */ for (j=i; j>=0 && status[j]==1 && time[j]==time[i]; j--) { ndeath += wt[j]; index = indx[j]; for (k=i; k>j; k--) count[3] += wt[j]*wt[k]; /* tied on time */ count[2] += wt[j] * nwt[index]; /* tied on x */ child = (2*index) +1; /* left child */ if (child < ntree) count[0] += wt[j] * twt[child]; /*left children */ child++; if (child < ntree) count[1] += wt[j] * twt[child]; /*right children */ while (index >0) { /* walk up the tree */ parent = (index-1)/2; if (index & 1) /* I am the left child */ count[1] += wt[j] * (twt[parent] - twt[index]); else count[0] += wt[j] * (twt[parent] - twt[index]); index = parent; } } } else j = i-1; /* Add the weights for these obs into the tree and update variance*/ for (; i>j; i--) { wsum1=0; oldmean = twt[0]/2; index = indx[i]; nwt[index] += wt[i]; twt[index] += wt[i]; wsum2 = nwt[index]; child = 2*index +1; /* left child */ if (child < ntree) wsum1 += twt[child]; while (index >0) { parent = (index-1)/2; twt[parent] += wt[i]; if (!(index&1)) /* I am a right child */ wsum1 += (twt[parent] - twt[index]); index=parent; } wsum3 = twt[0] - (wsum1 + wsum2); /* sum of weights above */ lmean = wsum1/2; umean = wsum1 + wsum2 + wsum3/2; /* new upper mean */ newmean = twt[0]/2; myrank = wsum1 + wsum2/2; vss += wsum1*(newmean+ oldmean - 2*lmean) * (newmean - oldmean); vss += wsum3*(newmean+ oldmean+ wt[i]- 2*umean) *(oldmean-newmean); vss += wt[i]* (myrank -newmean)*(myrank -newmean); } count[4] += ndeath * vss/twt[0]; } @ The code for [start, stop) data is quite similar. As in the agreg routines there are two sort indices, the first indexes the data by stop time, longest to earliest, and the second by start time. The [[y]] variable now has three columns. <>= SEXP concordance2(SEXP y, SEXP wt2, SEXP indx2, SEXP ntree2, SEXP sortstop, SEXP sortstart) { int i, j, k, index; int child, parent; int n, ntree; int istart, iptr, jptr; double *time1, *time2, *status, dtime; double *twt, *nwt, *count; int *sort1, *sort2; double vss, myrank; double wsum1, wsum2, wsum3; /*sum of wts below, tied, above*/ double lmean, umean, oldmean, newmean; double ndeath; SEXP count2; double *wt; int *indx; n = nrows(y); ntree = asInteger(ntree2); wt = REAL(wt2); indx = INTEGER(indx2); sort2 = INTEGER(sortstop); sort1 = INTEGER(sortstart); time1 = REAL(y); time2 = time1 + n; status= time2 + n; PROTECT(count2 = allocVector(REALSXP, 5)); count = REAL(count2); twt = (double *) R_alloc(2*ntree, sizeof(double)); nwt = twt + ntree; for (i=0; i< 2*ntree; i++) twt[i] =0.0; for (i=0; i<5; i++) count[i]=0.0; vss =0; <> UNPROTECT(1); return(count2); } @ The processing changes in 2 ways \begin{itemize} \item The loops go from $0$ to $n-1$ instead of $n-1$ to 0. We need to use [[sort1[i]]] instead of [[i]] as the subscript for the time2 and wt vectors. (The sort vectors go backwards in time.) This happens enough that we use a temporary variables [[iptr]] and [[jptr]] to avoid the double subscript. \item As we move from the longest time to the shortest observations are added into the tree of weights whenever we encounter their stop time. This is just as before. Weights now also need to be removed from the tree whenever we encounter an observation's start time. %' It is convenient ``catch up'' on this second task whenever we encounter a death. \end{itemize} <>= istart = 0; /* where we are with start times */ for (i=0; i= dtime; istart++) { wsum1 =0; oldmean = twt[0]/2; jptr = sort1[istart]; index = indx[jptr]; nwt[index] -= wt[jptr]; twt[index] -= wt[jptr]; wsum2 = nwt[index]; child = 2*index +1; /* left child */ if (child < ntree) wsum1 += twt[child]; while (index >0) { parent = (index-1)/2; twt[parent] -= wt[jptr]; if (!(index&1)) /* I am a right child */ wsum1 += (twt[parent] - twt[index]); index=parent; } wsum3 = twt[0] - (wsum1 + wsum2); lmean = wsum1/2; umean = wsum1 + wsum2 + wsum3/2; /* new upper mean */ newmean = twt[0]/2; myrank = wsum1 + wsum2/2; vss += wsum1*(newmean+ oldmean - 2*lmean) * (newmean-oldmean); oldmean -= wt[jptr]; /* the z in equations above */ vss += wsum3*(newmean+ oldmean -2*umean) * (newmean-oldmean); vss -= wt[jptr]* (myrank -newmean)*(myrank -newmean); } /* Process deaths */ for (j=i; j 0) { /* walk up the tree */ parent = (index-1)/2; if (index &1) /* I am the left child */ count[1] += wt[jptr] * (twt[parent] - twt[index]); else count[0] += wt[jptr] * (twt[parent] - twt[index]); index = parent; } } } else j = i+1; /* Add the weights for these obs into the tree and compute variance */ for (; i0) { parent = (index-1)/2; twt[parent] += wt[iptr]; if (!(index&1)) /* I am a right child */ wsum1 += (twt[parent] - twt[index]); index=parent; } wsum3 = twt[0] - (wsum1 + wsum2); lmean = wsum1/2; umean = wsum1 + wsum2 + wsum3/2; /* new upper mean */ newmean = twt[0]/2; myrank = wsum1 + wsum2/2; vss += wsum1*(newmean+ oldmean - 2*lmean) * (newmean-oldmean); vss += wsum3*(newmean+ oldmean +wt[iptr] - 2*umean) * (oldmean-newmean); vss += wt[iptr]* (myrank -newmean)*(myrank -newmean); } count[4] += ndeath * vss/twt[0]; } @ One last wrinkle is tied risk scores: they are all set to point to the same node of the tree. Here is the main routine. <>= survConcordance <- function(formula, data, weights, subset, na.action) { Call <- match.call() # save a copy of of the call, as documentation .Deprecated("concordance") m <- match.call(expand.dots=FALSE) m[[1L]] <- quote(stats::model.frame) m$formula <- if(missing(data)) terms(formula, "strata") else terms(formula, "strata", data=data) m <- eval(m, sys.parent()) Terms <- attr(m, 'terms') Y <- model.extract(m, "response") if (!inherits(Y, "Surv")) { if (is.numeric(Y) && is.vector(Y)) Y <- Surv(Y) else stop("left hand side of the formula must be a numeric vector or a surival") } n <- nrow(Y) wt <- model.extract(m, 'weights') offset<- attr(Terms, "offset") if (length(offset)>0) stop("Offset terms not allowed") stemp <- untangle.specials(Terms, 'strata') if (length(stemp$vars)) { if (length(stemp$vars)==1) strat <- m[[stemp$vars]] else strat <- strata(m[,stemp$vars], shortlabel=TRUE) Terms <- Terms[-stemp$terms] } else strat <- NULL x <- model.matrix(Terms, m)[,-1, drop=FALSE] #remove the intercept if (ncol(x) > 1) stop("Only one predictor variable allowed") count <- survConcordance.fit(Y, x, strat, wt) if (is.null(strat)) { concordance <- (count[1] + count[3]/2)/sum(count[1:3]) std.err <- count[5]/(2* sum(count[1:3])) } else { temp <- colSums(count) concordance <- (temp[1] + temp[3]/2)/ sum(temp[1:3]) std.err <- temp[5]/(2*sum(temp[1:3])) } fit <- list(concordance= concordance, stats=count, n=n, std.err=std.err, call=Call) na.action <- attr(m, "na.action") if (length(na.action)) fit$na.action <- na.action oldClass(fit) <- 'survConcordance' fit } print.survConcordance <- function(x, ...) { if(!is.null(cl <- x$call)) { cat("Call:\n") dput(cl) cat("\n") } omit <- x$na.action if(length(omit)) cat(" n=", x$n, " (", naprint(omit), ")\n", sep = "") else cat(" n=", x$n, "\n") cat("Concordance= ", format(x$concordance), " se= ", format(x$std.err), '\n', sep='') print(x$stats) invisible(x) } @ This part of the compuation is a separate function, since it is also called by the coxph routines. Although we are very careful to create integers and/or doubles for the arguments to .Call I still wrap them in the appropriate as.xxx construction: ``belt and suspenders''. Also, referring to the the mathematics many paragraphs ago, the C routine returns the variance of $(C-D)/2$ and we return the standard deviation of $(C-D)$. If this routine is called with all the x values identical, then $C$ and $D$ will both be zero, but the calculated variance of $C-D$ can be a nonzero tiny number due to round off error. Since this can cause a warning message from the sqrt function we check and correct this. <>= survConcordance.fit <- function(y, x, strata, weight) { .Deprecated("concordancefit") # The coxph program may occassionally fail, and this will kill the C # routine below if (any(is.na(x)) || any(is.na(y))) return(NULL) <> docount <- function(stime, risk, wts) { if (attr(stime, 'type') == 'right') { ord <- order(stime[,1], -stime[,2]) ux <- sort(unique(risk)) n2 <- length(ux) index <- btree(n2)[match(risk[ord], ux)] - 1L .Call(Cconcordance1, stime[ord,], as.double(wts[ord]), as.integer(index), as.integer(length(ux))) } else if (attr(stime, 'type') == "counting") { sort.stop <- order(-stime[,2], stime[,3]) sort.start <- order(-stime[,1]) ux <- sort(unique(risk)) n2 <- length(ux) index <- btree(n2)[match(risk, ux)] - 1L .Call(Cconcordance2, stime, as.double(wts), as.integer(index), as.integer(length(ux)), as.integer(sort.stop-1L), as.integer(sort.start-1L)) } else stop("Invalid survival type for concordance") } if (missing(weight) || length(weight)==0) weight <- rep(1.0, length(x)) storage.mode(y) <- "double" if (missing(strata) || length(strata)==0) { count <- docount(y, x, weight) if (count[1]==0 && count[2]==0) count[5]<-0 else count[5] <- 2*sqrt(count[5]) names(count) <- c("concordant", "discordant", "tied.risk", "tied.time", "std(c-d)") } else { strata <- as.factor(strata) ustrat <- levels(strata)[table(strata) >0] #some strata may have 0 obs count <- matrix(0., nrow=length(ustrat), ncol=5) for (i in 1:length(ustrat)) { keep <- which(strata == ustrat[i]) count[i,] <- docount(y[keep,,drop=F], x[keep], weight[keep]) } count[,5] <- 2*sqrt(ifelse(count[,1]+count[,2]==0, 0, count[,5])) dimnames(count) <- list(ustrat, c("concordant", "discordant", "tied.risk", "tied.time", "std(c-d)")) } count } @ survival/noweb/coxsurv2.Rnw0000644000176200001440000005570313753367716015556 0ustar liggesusers% % Second part of coxsurv.Rnw, broken in two to make it easier for me % to work with emacs. Now, we're ready to do the main compuation. %' The code has gone through multiple iteration as options and complexity increased. Computations are separate for each strata, and each strata will have a different number of time points in the result. Thus we can't preallocate a matrix. Instead we generate an empty list, %' one per strata, and then populate it with the survival curves. At the end we unlist the individual components one by one. This is memory efficient, the number of curves is usually small enough that the "for" loop is no great cost, and it's easier to see what's going on than C code. The computational exception is a model with thousands of strata, e.g., a matched logistic, but in that case survival curves are useless. (That won't stop some users from trying it though.) First, compute the baseline survival curves for each strata. If the strata was a factor produce output curves in that order, otherwise in sorted order. This fitting routine was set out as a separate function for the sake of the rms package. They want to utilize the computation, but have a diffferent process to create the x and y data. <>= coxsurv.fit <- function(ctype, stype, se.fit, varmat, cluster, y, x, wt, risk, position, strata, oldid, y2, x2, risk2, strata2, id2, unlist=TRUE) { if (missing(strata) || length(strata)==0) strata <- rep(0L, nrow(y)) if (is.factor(strata)) ustrata <- levels(strata) else ustrata <- sort(unique(strata)) nstrata <- length(ustrata) survlist <- vector('list', nstrata) names(survlist) <- ustrata survtype <- if (stype==1) 1 else ctype+1 vartype <- survtype if (is.null(wt)) wt <- rep(1.0, nrow(y)) if (is.null(strata)) strata <- rep(1L, nrow(y)) for (i in 1:nstrata) { indx <- which(strata== ustrata[i]) survlist[[i]] <- agsurv(y[indx,,drop=F], x[indx,,drop=F], wt[indx], risk[indx], survtype, vartype) } <> if (unlist) { if (length(result)==1) { # the no strata case if (se.fit) result[[1]][c("n", "time", "n.risk", "n.event", "n.censor", "surv", "cumhaz", "std.err")] else result[[1]][c("n", "time", "n.risk", "n.event", "n.censor", "surv", "cumhaz")] } else { <> } } else { names(result) <- ustrata result } } @ In an ordinary survival curve object with multiple strata, as produced by \code{survfitKM}, the time, survival and etc components are each a single vector that contains the results for strata 1, followed by strata 2, \ldots. The strata compontent is a vector of integers, one per strata, that gives the number of elements belonging to each stratum. The reason is that each strata will have a different number of observations, so that a matrix form was not viable, and the underlying C routines were not capable of handling lists (the code predates the .Call function by a decade). The underlying computation of \code{survfitcoxph.fit} naturally creates the list form, we unlist it to \code{survfit} form as our last action unless the caller requests otherwise. <>= temp <-list(n = unlist(lapply(result, function(x) x$n), use.names=FALSE), time= unlist(lapply(result, function(x) x$time), use.names=FALSE), n.risk= unlist(lapply(result, function(x) x$n.risk), use.names=FALSE), n.event= unlist(lapply(result, function(x) x$n.event), use.names=FALSE), n.censor=unlist(lapply(result, function(x) x$n.censor), use.names=FALSE), strata = sapply(result, function(x) length(x$time))) names(temp$strata) <- names(result) if ((missing(id2) || is.null(id2)) && nrow(x2)>1) { temp$surv <- t(matrix(unlist(lapply(result, function(x) t(x$surv)), use.names=FALSE), nrow= nrow(x2))) dimnames(temp$surv) <- list(NULL, row.names(x2)) temp$cumhaz <- t(matrix(unlist(lapply(result, function(x) t(x$cumhaz)), use.names=FALSE), nrow= nrow(x2))) if (se.fit) temp$std.err <- t(matrix(unlist(lapply(result, function(x) t(x$std.err)), use.names=FALSE), nrow= nrow(x2))) } else { temp$surv <- unlist(lapply(result, function(x) x$surv), use.names=FALSE) temp$cumhaz <- unlist(lapply(result, function(x) x$cumhaz), use.names=FALSE) if (se.fit) temp$std.err <- unlist(lapply(result, function(x) x$std.err), use.names=FALSE) } temp @ For \code{individual=FALSE} we have a second dimension, namely each of the target covariate sets (if there are multiples). Each of these generates a unique set of survival and variance(survival) values, but all of the same size since each uses all the strata. The final output structure in this case has single vectors for the time, number of events, number censored, and number at risk values since they are common to all the curves, and a matrix of survival and variance estimates, one column for each of the distinct target values. If $\Lambda_0$ is the baseline cumulative hazard from the above calculation, then $r_i \Lambda_0$ is the cumulative hazard for the $i$th new risk score $r_i$. The variance has two parts, the first of which is $r_i^2 H_1$ where $H_1$ is returned from the \code{agsurv} routine, and the second is \begin{align*} H_2(t) =& d'(t) V d(t) \\ %' d(t) = \int_0^t [z- \overline x(s)] d\Lambda(s) \end{align*} $V$ is the variance matrix for $\beta$ from the fitted Cox model, and $d(t)$ is the distance between the target covariate $z$ and the mean of the original data, summed up over the interval from 0 to $t$. Essentially the variance in $\hat \beta$ has a larger influence when prediction is far from the mean. The function below takes the basic curve from the list and multiplies it out to matrix form. <>= expand <- function(fit, x2, varmat, se.fit) { if (survtype==1) surv <- cumprod(fit$surv) else surv <- exp(-fit$cumhaz) if (is.matrix(x2) && nrow(x2) >1) { #more than 1 row in newdata fit$surv <- outer(surv, risk2, '^') dimnames(fit$surv) <- list(NULL, row.names(x2)) if (se.fit) { varh <- matrix(0., nrow=length(fit$varhaz), ncol=nrow(x2)) for (i in 1:nrow(x2)) { dt <- outer(fit$cumhaz, x2[i,], '*') - fit$xbar varh[,i] <- (cumsum(fit$varhaz) + rowSums((dt %*% varmat)* dt))* risk2[i]^2 } fit$std.err <- sqrt(varh) } fit$cumhaz <- outer(fit$cumhaz, risk2, '*') } else { fit$surv <- surv^risk2 if (se.fit) { dt <- outer(fit$cumhaz, c(x2)) - fit$xbar varh <- (cumsum(fit$varhaz) + rowSums((dt %*% varmat)* dt)) * risk2^2 fit$std.err <- sqrt(varh) } fit$cumhaz <- fit$cumhaz * risk2 } fit } @ In the lines just above: I have a matrix \code{dt} with one row per death time and one column per variable. For each row $d_i$ separately we want the quadratic form $d_i V d_i'$. The first matrix product can %' be done for all rows at once: found in the inner parenthesis. Ordinary (not matrix) multiplication followed by rowsums does the rest in one fell swoop. Now, if \code{id2} is missing we can simply apply the \code{expand} function to each strata. For the case with \code{id2} not missing, we create a single survival curve for each unique id (subject). A subject will spend blocks of time with different covariate sets, sometimes even jumping between strata. Retrieve each one and save it into a list, and then sew them together end to end. The \code{n} component is the number of observations in the strata --- but this subject might visit several. We report the first one they were in for printout. The \code{time} component will be cumulative on this subject's scale. %' Counting this is a bit trickier than I first thought. Say that the subject's first interval goes from 1 to 10, with observed time points in that interval at 2, 5, and 7, and a second interval from 12 to 20 with observed time points in the data of 15 and 18. On the subject's time scale things happen at days 1, 4, 6, 12 and 15. The deltas saved below are 2-1, 5-2, 7-5, 3+ 14-12, 17-14. Note the 3+ part, kept in the \code{timeforward} variable. Why all this ``adding up'' nuisance? If the subject spent time in two strata, the second one might be on an internal time scale of `time since entering the strata'. The two intervals in newdata could be 0--10 followed by 0--20. Time for the subject can't go backwards though: the change %` between internal/external time scales is a bit like following someone who was stepping back and forth over the international date line. In the code the \code{indx} variable points to the set of times that the subject was present, for this row of the new data. Note the $>$ on one end and $\le$ on the other. If someone's interval 1 was 0--10 and interval 2 was 10--20, and there happened to be a jump in the baseline survival curve at exactly time 10 (someone else died), that jump is counted only in the first interval. <>= if (missing(id2) || is.null(id2)) result <- lapply(survlist, expand, x2, varmat, se.fit) else { onecurve <- function(slist, x2, y2, strata2, risk2, se.fit) { ntarget <- nrow(x2) #number of different time intervals surv <- vector('list', ntarget) n.event <- n.risk <- n.censor <- varh1 <- varh2 <- time <- surv hazard <- vector('list', ntarget) stemp <- as.integer(strata2) timeforward <- 0 for (i in 1:ntarget) { slist <- survlist[[stemp[i]]] indx <- which(slist$time > y2[i,1] & slist$time <= y2[i,2]) if (length(indx)==0) { timeforward <- timeforward + y2[i,2] - y2[i,1] # No deaths or censors in user interval. Possible # user error, but not uncommon at the tail of the curve. } else { time[[i]] <- diff(c(y2[i,1], slist$time[indx])) #time increments time[[i]][1] <- time[[i]][1] + timeforward timeforward <- y2[i,2] - max(slist$time[indx]) hazard[[i]] <- slist$hazard[indx]*risk2[i] if (survtype==1) surv[[i]] <- slist$surv[indx]^risk2[i] n.event[[i]] <- slist$n.event[indx] n.risk[[i]] <- slist$n.risk[indx] n.censor[[i]]<- slist$n.censor[indx] dt <- outer(slist$cumhaz[indx], x2[i,]) - slist$xbar[indx,,drop=F] varh1[[i]] <- slist$varhaz[indx] *risk2[i]^2 varh2[[i]] <- rowSums((dt %*% varmat)* dt) * risk2[i]^2 } } cumhaz <- cumsum(unlist(hazard)) if (survtype==1) surv <- cumprod(unlist(surv)) #increments (K-M) else surv <- exp(-cumhaz) if (se.fit) list(n=as.vector(table(strata)[stemp[1]]), time=cumsum(unlist(time)), n.risk = unlist(n.risk), n.event= unlist(n.event), n.censor= unlist(n.censor), surv = surv, cumhaz= cumhaz, std.err = sqrt(cumsum(unlist(varh1)) + unlist(varh2))) else list(n=as.vector(table(strata)[stemp[1]]), time=cumsum(unlist(time)), n.risk = unlist(n.risk), n.event= unlist(n.event), n.censor= unlist(n.censor), surv = surv, cumhaz= cumhaz) } if (all(id2 ==id2[1])) { result <- list(onecurve(survlist, x2, y2, strata2, risk2, se.fit)) } else { uid <- unique(id2) result <- vector('list', length=length(uid)) for (i in 1:length(uid)) { indx <- which(id2==uid[i]) result[[i]] <- onecurve(survlist, x2[indx,,drop=FALSE], y2[indx,,drop=FALSE], strata2[indx], risk2[indx], se.fit) } names(result) <- uid } } @ Next is the code for the \code{agsurv} function, which actually does the work. The estimates of survival are the Kalbfleisch-Prentice (KP), Breslow, and Efron. Each has an increment at each unique death time. First a bit of notation: $Y_i(t)$ is 1 if bservation $i$ is ``at risk'' at time $t$ and 0 otherwise. For a simple surivival (\code{ncol(y)==2}) a subject is at risk until the time of censoring or death (first column of \code{y}). For (start, stop] data (\code{ncol(y)==3}) a subject becomes a part of the risk set at start+0 and stays through stop. $dN_i(t)$ will be 1 if subject $i$ had an event at time $t$. The risk score for each subject is $r_i = \exp(X_i \beta)$. The Breslow increment at time $t$ is $\sum w_i dN_i(t) / \sum w_i r_i Y_i(t)$, the number of events at time $t$ over the number at risk at time $t$. The final survival is \code{exp(-cumsum(increment))}. The Kalbfleish-Prentice increment is a multiplicative term $z$ which is the solution to the equation $$ \sum w_i r_i Y_i(t) = \sum dN_i(t) w_i \frac{r_i}{1- z(t)^{r_i}} $$ The left hand side is the weighted number at risk at time $t$, the right hand side is a sum over the tied events at that time. If there is only one event the equation has a closed form solution. If not, and knowing the solution must lie between 0 and 1, we do 35 steps of bisection to get a solution within 1e-8. An alternative is to use the -log of the Breslow estimate as a starting estimate, which is faster but requires a more sophisticated iteration logic. The final curve is $\prod_t z(t)^{r_c}$ where $r_c$ is the risk score for the target subject. The Efron estimate can be viewed as a modified Breslow estimate under the assumption that tied deaths are not really tied -- we just don't know the %' order. So if there are 3 subjects who die at some time $t$ we will have three psuedo-terms for $t$, $t+\epsilon$, and $t+ 2\epsilon$. All 3 subjects are present for the denominator of the first term, 2/3 of each for the second, and 1/3 for the third terms denominator. All contribute 1/3 of the weight to each numerator (1/3 chance they were the one to die there). The formulas will require $\sum w_i dN_i(t)$, $\sum w_ir_i dN_i(t)$, and $\sum w_i X_i dN_i(t)$, i.e., the sums only over the deaths. For simple survival data the risk sum $\sum w_i r_i Y_i(t)$ for all the unique death times $t$ is fast to compute as a cumulative sum, starting at the longest followup time and summing towards the shortest. There are two algorithms for (start, stop] data. \begin{itemize} \item Do a separate sum at each death time. The problem is for very large data sets. For each death time the selection \code{(start=t)} is $O(n)$ and can take more time then all the remaining calculations together. \item Use the difference of two cumulative sums, one ordered by start time and one ordered by stop time. This is $O(2n)$ for the intial sums. The problem here is potential round off error if the sums get large. This issue is mostly precluded by subtracting means first, and avoiding intervals that don't overlap an event time. \end{itemize} We compute the extended number still at risk --- all whose stop time is $\ge$ each unique death time --- in the vector \code{xin}. From this we have to subtract all those who haven't actually entered yet %' found in \code{xout}. Remember that (3,20] enters at time 3+. The total at risk at any time is the difference between them. Output is only for the stop times; a call to approx is used to reconcile the two time sets. The \code{irisk} vector is for the printout, it is a sum of weighted counts rather than weighted risk scores. <>= agsurv <- function(y, x, wt, risk, survtype, vartype) { nvar <- ncol(as.matrix(x)) status <- y[,ncol(y)] dtime <- y[,ncol(y) -1] death <- (status==1) time <- sort(unique(dtime)) nevent <- as.vector(rowsum(wt*death, dtime)) ncens <- as.vector(rowsum(wt*(!death), dtime)) wrisk <- wt*risk rcumsum <- function(x) rev(cumsum(rev(x))) # sum from last to first nrisk <- rcumsum(rowsum(wrisk, dtime)) irisk <- rcumsum(rowsum(wt, dtime)) if (ncol(y) ==2) { temp2 <- rowsum(wrisk*x, dtime) xsum <- apply(temp2, 2, rcumsum) } else { delta <- min(diff(time))/2 etime <- c(sort(unique(y[,1])), max(y[,1])+delta) #unique entry times indx <- approx(etime, 1:length(etime), time, method='constant', rule=2, f=1)$y esum <- rcumsum(rowsum(wrisk, y[,1])) #not yet entered nrisk <- nrisk - c(esum,0)[indx] irisk <- irisk - c(rcumsum(rowsum(wt, y[,1])),0)[indx] xout <- apply(rowsum(wrisk*x, y[,1]), 2, rcumsum) #not yet entered xin <- apply(rowsum(wrisk*x, dtime), 2, rcumsum) # dtime or alive xsum <- xin - (rbind(xout,0))[indx,,drop=F] } ndeath <- rowsum(status, dtime) #unweighted death count @ The KP estimate requires a short C routine to do the iteration efficiently, and the Efron estimate needs a second C routine to efficiently compute the partial sums. <>= ntime <- length(time) if (survtype ==1) { #Kalbfleisch-Prentice indx <- (which(status==1))[order(dtime[status==1])] #deaths km <- .C(Cagsurv4, as.integer(ndeath), as.double(risk[indx]), as.double(wt[indx]), as.integer(ntime), as.double(nrisk), inc = double(ntime)) } if (survtype==3 || vartype==3) { # Efron approx xsum2 <- rowsum((wrisk*death) *x, dtime) erisk <- rowsum(wrisk*death, dtime) #risk score sums at each death tsum <- .C(Cagsurv5, as.integer(length(nevent)), as.integer(nvar), as.integer(ndeath), as.double(nrisk), as.double(erisk), as.double(xsum), as.double(xsum2), sum1 = double(length(nevent)), sum2 = double(length(nevent)), xbar = matrix(0., length(nevent), nvar)) } haz <- switch(survtype, nevent/nrisk, nevent/nrisk, nevent* tsum$sum1) varhaz <- switch(vartype, nevent/(nrisk * ifelse(nevent>=nrisk, nrisk, nrisk-nevent)), nevent/nrisk^2, nevent* tsum$sum2) xbar <- switch(vartype, (xsum/nrisk)*haz, (xsum/nrisk)*haz, nevent * tsum$xbar) result <- list(n= nrow(y), time=time, n.event=nevent, n.risk=irisk, n.censor=ncens, hazard=haz, cumhaz=cumsum(haz), varhaz=varhaz, ndeath=ndeath, xbar=apply(matrix(xbar, ncol=nvar),2, cumsum)) if (survtype==1) result$surv <- km$inc result } @ The arguments to this function are the number of unique times n, which is the length of the vectors ndeath (number at each time), denom, and the returned vector km. The risk and wt vectors contain individual values for the subjects with an event. Their length will be equal to sum(ndeath). <>= #include "survS.h" #include "survproto.h" void agsurv4(Sint *ndeath, double *risk, double *wt, Sint *sn, double *denom, double *km) { int i,j,k, l; int n; /* number of unique death times */ double sumt, guess, inc; n = *sn; j =0; for (i=0; i>= #include "survS.h" void agsurv5(Sint *n2, Sint *nvar2, Sint *dd, double *x1, double *x2, double *xsum, double *xsum2, double *sum1, double *sum2, double *xbar) { double temp; int i,j, k, kk; double d; int n, nvar; n = n2[0]; nvar = nvar2[0]; for (i=0; i< n; i++) { d = dd[i]; if (d==1){ temp = 1/x1[i]; sum1[i] = temp; sum2[i] = temp*temp; for (k=0; k< nvar; k++) xbar[i+ n*k] = xsum[i + n*k] * temp*temp; } else { temp = 1/x1[i]; for (j=0; j>= plot.survfit<- function(x, conf.int, mark.time=FALSE, pch=3, col=1,lty=1, lwd=1, cex=1, log=FALSE, xscale=1, yscale=1, xlim, ylim, xmax, fun, xlab="", ylab="", xaxs='r', conf.times, conf.cap=.005, conf.offset=.012, conf.type=c('log', 'log-log', 'plain', 'logit', "arcsin"), mark, noplot="(s0)", cumhaz=FALSE, firstx, ymin, ...) { dotnames <- names(list(...)) if (any(dotnames =='type')) stop("The graphical argument 'type' is not allowed") x <- survfit0(x, x$start.time) # align data at 0 for plotting <> <> <> <> <> <> <> type <- 's' <> invisible(lastx) } lines.survfit <- function(x, type='s', pch=3, col=1, lty=1, lwd=1, cex=1, mark.time=FALSE, xmax, fun, conf.int=FALSE, conf.times, conf.cap=.005, conf.offset=.012, conf.type=c('log', 'log-log', 'plain', 'logit', "arcsin"), mark, noplot="(s0)", cumhaz=FALSE, ...) { x <- survfit0(x, x$start.time) xlog <- par("xlog") <> <> <> <> # remember a prior xmax if (missing(xmax)) xmax <- getOption("plot.survfit")$xmax <> <> invisible(lastx) } points.survfit <- function(x, fun, censor=FALSE, col=1, pch, noplot="(s0)", cumhaz=FALSE, ...) { conf.int <- conf.times <- FALSE # never draw these with 'points' x <- survfit0(x, x$start.time) <> <> if (ncurve==1 || (length(col)==1 && missing(pch))) { if (censor) points(stime, ssurv, ...) else points(stime[x$n.event>0], ssurv[x$n.event>0], ...) } else { c2 <- 1 #cycles through the colors and characters col <- rep(col, length=ncurve) if (!missing(pch)) { if (length(pch)==1) pch2 <- rep(strsplit(pch, '')[[1]], length=ncurve) else pch2 <- rep(pch, length=ncurve) } for (j in 1:ncol(ssurv)) { for (i in unique(stemp)) { if (censor) who <- which(stemp==i) else who <- which(stemp==i & x$n.event >0) if (missing(pch)) points(stime[who], ssurv[who,j], col=col[c2], ...) else points(stime[who], ssurv[who,j], col=col[c2], pch=pch2[c2], ...) c2 <- c2+1 } } } } @ <>= # decide on logarithmic axes, yes or no if (is.logical(log)) { ylog <- log xlog <- FALSE if (ylog) logax <- 'y' else logax <- "" } else { ylog <- (log=='y' || log=='xy') xlog <- (log=='x' || log=='xy') logax <- log } if (!missing(fun)) { if (is.character(fun)) { if (fun=='log'|| fun=='logpct') ylog <- TRUE if (fun=='cloglog') { xlog <- TRUE if (ylog) logax <- 'xy' else logax <- 'x' } if (fun=="cumhaz" && missing(cumhaz)) cumhaz <- TRUE } } @ <>= # The default for plot and lines is to add confidence limits # if there is only one curve if (missing(conf.int) && missing(conf.times)) conf.int <- (!is.null(x$std.err) && prod(dim(x) ==1)) if (missing(conf.times)) conf.times <- NULL else { if (!is.numeric(conf.times)) stop('conf.times must be numeric') if (missing(conf.int)) conf.int <- TRUE } if (!missing(conf.int)) { if (is.numeric(conf.int)) { conf.level <- conf.int if (conf.level<0 || conf.level > 1) stop("invalid value for conf.int") if (conf.level ==0) conf.int <- FALSE else if (conf.level != x$conf.int) { x$upper <- x$lower <- NULL # force recomputation } conf.int <- TRUE } else conf.level = 0.95 } # Organize data into stime, ssurv, supper, slower stime <- x$time std <- NULL yzero <- FALSE # a marker that we have an "ordinary survival curve" with min 0 smat <- function(x) { # the rest of the routine is simpler if everything is a matrix dd <- dim(x) if (is.null(dd)) as.matrix(x) else if (length(dd) ==2) x else matrix(x, nrow=dd[1]) } if (cumhaz) { # plot the cumulative hazard instead if (is.null(x$cumhaz)) stop("survfit object does not contain a cumulative hazard") if (is.numeric(cumhaz)) { dd <- dim(x$cumhaz) if (is.null(dd)) nhazard <- 1 else nhazard <- prod(dd[-1]) if (cumhaz != floor(cumhaz)) stop("cumhaz argument is not integer") if (any(cumhaz < 1 | cumhaz > nhazard)) stop("subscript out of range") ssurv <- smat(x$cumhaz)[,cumhaz, drop=FALSE] if (!is.null(x$std.chaz)) std <- smat(x$std.chaz)[,cumhaz, drop=FALSE] } else if (is.logical(cumhaz)) { ssurv <- smat(x$cumhaz) if (!is.null(x$std.chaz)) std <- smat(x$std.chaz) } else stop("invalid cumhaz argument") } else if (inherits(x, "survfitms")) { i <- !(x$states %in% noplot) if (all(i) || !any(i)) { # the !any is a failsafe, in case none are kept we ignore noplot ssurv <- smat(x$pstate) if (!is.null(x$std.err)) std <- smat(x$std.err) if (!is.null(x$lower)) { slower <- smat(x$lower) supper <- smat(x$upper) } } else { i <- which(i) # the states to keep # we have to be careful about subscripting if (length(dim(x$pstate)) ==3) { ssurv <- smat(x$pstate[,,i, drop=FALSE]) if (!is.null(x$std.err)) std <- smat(x$std.err[,,i, drop=FALSE]) if (!is.null(x$lower)) { slower <- smat(x$lower[,,i, drop=FALSE]) supper <- smat(x$upper[,,i, drop=FALSE]) } } else { ssurv <- x$pstate[,i, drop=FALSE] if (!is.null(x$std.err)) std <- x$std.err[,i, drop=FALSE] if (!is.null(x$lower)) { slower <- smat(x$lower[,i, drop=FALSE]) supper <- smat(x$upper[,i, drop=FALSE]) } } } } else { yzero <- TRUE ssurv <- as.matrix(x$surv) # x$surv will have one column if (!is.null(x$std.err)) std <- as.matrix(x$std.err) # The fun argument usually applies to single state survfit objects # First deal with the special case of fun='cumhaz', which is here for # backwards compatability; people should use the cumhaz argument if (!missing(fun) && is.character(fun) && fun=="cumhaz") { cumhaz <- TRUE if (!is.null(x$cumhaz)) { ssurv <- as.matrix(x$cumhaz) if (!is.null(x$std.chaz)) std <- as.matrix(x$std.chaz) } else { ssurv <- as.matrix(-log(x$surv)) if (!is.null(x$std.err)) { if (x$logse) std <- as.matrix(x$std.err) else std <- as.matrix(x$std.err/x$surv) } } } } # set up strata if (is.null(x$strata)) { nstrat <- 1 stemp <- rep(1, length(x$time)) # same length as stime } else { nstrat <- length(x$strata) stemp <- rep(1:nstrat, x$strata) # same length as stime } ncurve <- nstrat * ncol(ssurv) @ If confidence limits are to be plotted, and they were not part of the data that is passed in, create them. Confidence limits for the cumulative hazard must always be created, and they don't use transforms. <>= conf.type <- match.arg(conf.type) if (conf.type=="none") conf.int <- FALSE if (conf.int== "none") conf.int <- FALSE if (conf.int=="only") { plot.surv <- FALSE conf.int <- TRUE } else plot.surv <- TRUE if (conf.int) { if (is.null(std)) stop("object does not have standard errors, CI not possible") if (cumhaz) { if (missing(conf.type)) conf.type="plain" temp <- survfit_confint(ssurv, std, logse=FALSE, conf.type, conf.level, ulimit=FALSE) supper <- as.matrix(temp$upper) slower <- as.matrix(temp$lower) } else if (is.null(x$upper)) { if (missing(conf.type) && !is.null(x$conf.type)) conf.type <- x$conf.type temp <- survfit_confint(ssurv, std, logse= x$logse, conf.type, conf.level, ulimit=FALSE) supper <- as.matrix(temp$upper) slower <- as.matrix(temp$lower) } else if (!inherits(x, "survfitms")) { supper <- as.matrix(x$upper) slower <- as.matrix(x$lower) } } else supper <- slower <- NULL @ The functional form of the fun argument can be whatever the user wants. For the character form we try to thin out the obvious mistakes. If fun=='cumhaz', the code above has already replaced ssurv with the cumulative hazard, so this part of the code should plug in an identity function. <>= if (!missing(fun)){ if (is.character(fun)) { if (cumhaz) { tfun <- switch(tolower(fun), 'log' = function(x) x, 'cumhaz'=function(x) x, 'identity'= function(x) x, stop("Invalid function argument") ) } else if (inherits(x, "survfitms")) { tfun <-switch(tolower(fun), 'log' = function(x) log(x), 'event'=function(x) x, 'cloglog'=function(x) log(-log(1-x)), 'cumhaz' = function(x) x, 'pct' = function(x) x*100, 'identity'= function(x) x, stop("Invalid function argument") ) } else { yzero <- FALSE tfun <- switch(tolower(fun), 'log' = function(x) x, 'event'=function(x) 1-x, 'cumhaz'=function(x) x, 'cloglog'=function(x) log(-log(x)), 'pct' = function(x) x*100, 'logpct'= function(x) 100*x, #special case further below 'identity'= function(x) x, 'f' = function(x) 1-x, 's' = function(x) x, 'surv' = function(x) x, stop("Unrecognized function argument") ) } } else if (is.function(fun)) tfun <- fun else stop("Invalid 'fun' argument") ssurv <- tfun(ssurv ) if (!is.null(supper)) { supper <- tfun(supper) slower <- tfun(slower) } } @ The \code{mark} argument is a holdover from S, when pch could not have numeric values; mark has since disappeared from the manual page for \code{par}. We honor it for backwards compatability. To be consistent with matplot and others, we allow pch to be a character string or a vector of characters. <>= if (missing(mark.time) & !missing(mark)) mark.time <- TRUE if (missing(pch) && !missing(mark)) pch <- mark if (length(pch)==1 && is.character(pch)) pch <- strsplit(pch, "")[[1]] # Marks are not placed on confidence bands pch <- rep(pch, length.out=ncurve) mcol <- rep(col, length.out=ncurve) if (is.numeric(mark.time)) mark.time <- sort(mark.time) # The actual number of curves is ncurve*3 if there are confidence bands, # unless conf.times has been given. Colors and line types in the latter # match the curves # If the number of line types is 1 and lty is an integer, then use lty # for the curve and lty+1 for the CI # If the length(lty) <= length(ncurve), use the same color for curve and CI # otherwise assume the user knows what they are about and has given a full # vector of line types. # Colors and line widths work like line types, excluding the +1 rule. if (conf.int & is.null(conf.times)) { if (length(lty)==1 && is.numeric(lty)) lty <- rep(c(lty, lty+1, lty+1), ncurve) else if (length(lty) <= ncurve) lty <- rep(rep(lty, each=3), length.out=(ncurve*3)) else lty <- rep(lty, length.out= ncurve*3) if (length(col) <= ncurve) col <- rep(rep(col, each=3), length.out=3*ncurve) else col <- rep(col, length.out=3*ncurve) if (length(lwd) <= ncurve) lwd <- rep(rep(lwd, each=3), length.out=3*ncurve) else lwd <- rep(lwd, length.out=3*ncurve) } else { col <- rep(col, length.out=ncurve) lty <- rep(lty, length.out=ncurve) lwd <- rep(lwd, length.out=ncurve) } @ Create the frame for the plot. We draw an empty figure, letting R figure out the limits. <>= # check consistency if (!missing(xlim)) { if (!missing(xmax)) warning("cannot have both xlim and xmax arguments, xmax ignored") if (!missing(firstx)) stop("cannot have both xlim and firstx arguments") } if (!missing(ylim)) { if (!missing(ymin)) stop("cannot have both ylim and ymin arguments") } # Do axis range computations if (!missing(xlim) && !is.null(xlim)) { tempx <- xlim xmax <- xlim[2] if (xaxs == 'S') tempx[2] <- tempx[1] + diff(tempx)*1.04 } else { temp <- stime[is.finite(stime)] if (!missing(xmax) && missing(xlim)) temp <- pmin(temp, xmax) else xmax <- NULL if (xaxs=='S') { rtemp <- range(temp) delta <- diff(rtemp) #special x- axis style for survival curves if (xlog) tempx <- c(min(rtemp[rtemp>0]), min(rtemp)+ delta*1.04) else tempx <- c(min(rtemp), min(rtemp)+ delta*1.04) } else if (xlog) tempx <- range(temp[temp > 0]) else tempx <- range(temp) } if (!missing(xlim) || !missing(xmax)) options(plot.survfit = list(xmax=tempx[2])) else options(plot.survfit = NULL) if (!missing(ylim) && !is.null(ylim)) tempy <- ylim else { skeep <- is.finite(stime) & stime >= tempx[1] & stime <= tempx[2] if (ylog) { if (!is.null(supper)) tempy <- range(c(slower[is.finite(slower) & slower>0 & skeep], supper[is.finite(supper) & skeep])) else tempy <- range(ssurv[is.finite(ssurv)& ssurv>0 & skeep]) if (tempy[2]==1) tempy[2] <- .99 # makes for a prettier axis if (any(c(ssurv, slower)[skeep] ==0)) { tempy[1] <- tempy[1]*.8 ssurv[ssurv==0] <- tempy[1] if (!is.null(slower)) slower[slower==0] <- tempy[1] } } else { if (!is.null(supper)) tempy <- range(c(supper[skeep], slower[skeep]), finite=TRUE, na.rm=TRUE) else tempy <- range(ssurv[skeep], finite=TRUE, na.rm= TRUE) if (yzero) tempy <- range(c(0, tempy)) } } if (!missing(ymin)) tempy[1] <- ymin # # Draw the basic box # temp <- if (xaxs=='S') 'i' else xaxs plot(range(tempx, finite=TRUE, na.rm=TRUE)/xscale, range(tempy, finite=TRUE, na.rm=TRUE)*yscale, type='n', log=logax, xlab=xlab, ylab=ylab, xaxs=temp,...) if(yscale != 1) { if (ylog) par(usr =par("usr") -c(0, 0, log10(yscale), log10(yscale))) else par(usr =par("usr")/c(1, 1, yscale, yscale)) } if (xscale !=1) { if (xlog) par(usr =par("usr") -c(log10(xscale), log10(xscale), 0,0)) else par(usr =par("usr")*c(xscale, xscale, 1, 1)) } @ The use of [[par(usr)]] just above is a bit sneaky. I want the lines and points routines to be able to add to the plot, \emph{without} passing them a global parameter that determines the y-scale or forcing the user to repeat it. The next functions do the actual drawing. <>= # Create a step function, removing redundancies that sometimes occur in # curves with lots of censoring. dostep <- function(x,y) { keep <- is.finite(x) & is.finite(y) if (!any(keep)) return() #all points were infinite or NA if (!all(keep)) { # these won't plot anyway, so simplify (CI values are often NA) x <- x[keep] y <- y[keep] } n <- length(x) if (n==1) list(x=x, y=y) else if (n==2) list(x=x[c(1,2,2)], y=y[c(1,1,2)]) else { # replace verbose horizonal sequences like # (1, .2), (1.4, .2), (1.8, .2), (2.3, .2), (2.9, .2), (3, .1) # with (1, .2), (.3, .2),(3, .1). # They are slow, and can smear the looks of the line type. temp <- rle(y)$lengths drops <- 1 + cumsum(temp[-length(temp)]) # points where the curve drops #create a step function if (n %in% drops) { #the last point is a drop xrep <- c(x[1], rep(x[drops], each=2)) yrep <- rep(y[c(1,drops)], c(rep(2, length(drops)), 1)) } else { xrep <- c(x[1], rep(x[drops], each=2), x[n]) yrep <- c(rep(y[c(1,drops)], each=2)) } list(x=xrep, y=yrep) } } drawmark <- function(x, y, mark.time, censor, cex, ...) { if (!is.numeric(mark.time)) { xx <- x[censor>0] yy <- y[censor>0] if (any(censor >1)) { # tied death and censor, put it on the midpoint j <- pmax(1, which(censor>1) -1) i <- censor[censor>0] yy[i>1] <- (yy[i>1] + y[j])/2 } } else { #interpolate xx <- mark.time yy <- approx(x, y, xx, method="constant", f=0)$y } points(xx, yy, cex=cex, ...) } @ The code to draw the lines and confidence bands. <>= c1 <- 1 # keeps track of the curve number c2 <- 1 # keeps track of the lty, col, etc xend <- yend <- double(ncurve) if (length(conf.offset) ==1) temp.offset <- (1:ncurve - (ncurve+1)/2)* conf.offset* diff(par("usr")[1:2]) else temp.offset <- rep(conf.offset, length=ncurve) * diff(par("usr")[1:2]) temp.cap <- conf.cap * diff(par("usr")[1:2]) for (j in 1:ncol(ssurv)) { for (i in unique(stemp)) { #for each strata who <- which(stemp==i) # if n.censor is missing, then assume any line that does not have an # event would not be present but for censoring, so there must have # been censoring then # otherwise categorize is 0= no censor, 1=censor, 2=censor and death if (is.null(x$n.censor)) censor <- ifelse(x$n.event[who]==0, 1, 0) else censor <- ifelse(x$n.censor[who]==0, 0, 1 + (x$n.event[who] > 0)) xx <- stime[who] yy <- ssurv[who,j] if (conf.int) { ylower <- (slower[who,j]) yupper <- (supper[who,j]) } if (!is.null(xmax) && max(xx) > xmax) { # truncate on the right xn <- min(which(xx > xmax)) xx <- xx[1:xn] yy <- yy[1:xn] xx[xn] <- xmax yy[xn] <- yy[xn-1] if (conf.int) { ylower <- ylower[1:xn] yupper <- yupper[1:xn] ylower[xn] <- ylower[xn-1] yupper[xn] <- yupper[xn-1] } } if (plot.surv) { if (type=='s') lines(dostep(xx, yy), lty=lty[c2], col=col[c2], lwd=lwd[c2]) else lines(xx, yy, type=type, lty=lty[c2], col=col[c2], lwd=lwd[c2]) if (is.numeric(mark.time) || mark.time) drawmark(xx, yy, mark.time, censor, pch=pch[c1], col=mcol[c1], cex=cex) } xend[c1] <- max(xx) yend[c1] <- yy[length(yy)] if (conf.int && !is.null(conf.times)) { # add vertical bars at the specified times x2 <- conf.times + temp.offset[c1] templow <- approx(xx, ylower, x2, method='constant', f=1)$y temphigh<- approx(xx, yupper, x2, method='constant', f=1)$y segments(x2, templow, x2, temphigh, lty=lty[c2], col=col[c2], lwd=lwd[c2]) if (conf.cap>0) { segments(x2-temp.cap, templow, x2+temp.cap, templow, lty=lty[c2], col=col[c2], lwd=lwd[c2] ) segments(x2-temp.cap, temphigh, x2+temp.cap, temphigh, lty=lty[c2], col=col[c2], lwd=lwd[c2]) } } c1 <- c1 +1 c2 <- c2 +1 if (conf.int && is.null(conf.times)) { if (type == 's') { lines(dostep(xx, ylower), lty=lty[c2], col=col[c2],lwd=lwd[c2]) c2 <- c2 +1 lines(dostep(xx, yupper), lty=lty[c2], col=col[c2], lwd= lwd[c2]) c2 <- c2 + 1 } else { lines(xx, ylower, lty=lty[c2], col=col[c2],lwd=lwd[c2], type=type) c2 <- c2 +1 lines(xx, yupper, lty=lty[c2], col=col[c2], lwd= lwd[c2], type= type) c2 <- c2 + 1 } } } } lastx <- list(x=xend, y=yend) @ survival/noweb/msurv.nw0000644000176200001440000003430713722523645015001 0ustar liggesusers\subsubsection{C-code} (This is set up as a separate file in the source code directory since it is easier to make emacs stay in C-mode if the file has a .nw extension.) <>= #include "survS.h" #include "survproto.h" #include SEXP survfitci(SEXP ftime2, SEXP sort12, SEXP sort22, SEXP ntime2, SEXP status2, SEXP cstate2, SEXP wt2, SEXP id2, SEXP p2, SEXP i02, SEXP sefit2) { <> <> <> } @ Arguments to the routine are the following. For an R object ``zed'' I use the convention of [[zed2]] to refer to the object and [[zed]] to the contents of the object. \begin{description} \item[ftime] A two column matrix containing the entry and exit times for each subject. \item[sort1] Order vector for the entry times. The first element of sort1 points to the first entry time, etc. \item[sort2] Order vector for the event times. \item[ntime] Number of unique event time values. This fixes the size of the output arrays. \item[status] Status for each observation. 0= censored \item[cstate] The initial state for each subject, which will be updated during computation to always be the current state. \item[wt] Case weight for each observation. \item[id] The subject id for each observation. \item[p] The initial distribution of states. This will be updated during computation to be the current distribution. \item[i0] The initial influence matrix, number of subjects by number of states \item[sefit] If 1 then do the se compuatation, if 2 also return the full influence matrix upon which it is based, if 0 the se is not needed. \end{description} Note that code is called with id and not cluster: there is a basic premise that each id is a single subject and thus has a unique "current state" at any given time point. The history of this is that before the survcheck routine, we did not have a good way for a user to normalize the 'current state' variable for a subject, so this routine takes care of that tracking process. When multi-state Cox models were added we became more formal about this, and users can now have data sets with quite odd patterns of transitions and current state, ones that survcheck calls a teleport. At some point this routine should be updated as well. Cumulative hazard estimates make at least some sense when a subject has a hole, though P(state |t) curves do not. Declare all of the variables. <>= int i, j, k, kk; /* generic loop indices */ int ck, itime, eptr; /*specific indices */ double ctime; /*current time of interest, in the main loop */ int oldstate, newstate; /*when changing state */ double temp, *temp2; /* scratch double, and vector of length nstate */ double *dptr; /* reused in multiple contexts */ double *p; /* current prevalence vector */ double **hmat; /* hazard matrix at this time point */ double **umat=0; /* per subject leverage at this time point */ int *atrisk; /* 1 if the subject is currently at risk */ int *ns; /* number curently in each state */ int *nev; /* number of events at this time, by state */ double *ws; /* weighted count of number state */ double *wtp; /* case weights indexed by subject */ double wevent; /* weighted number of events at current time */ int nstate; /* number of states */ int n, nperson; /*number of obs, subjects*/ double **chaz; /* cumulative hazard matrix */ /* pointers to the R variables */ int *sort1, *sort2; /*sort index for entry time, event time */ double *entry,* etime; /*entry time, event time */ int ntime; /* number of unique event time values */ int *status; /*0=censored, 1,2,... new states */ int *cstate; /* current state for each subject */ int *dstate; /* the next state, =cstate if not an event time */ double *wt; /* weight for each observation */ double *i0; /* initial influence */ int *id; /* for each obs, which subject is it */ int sefit; /* returned objects */ SEXP rlist; /* the returned list and variable names of same */ const char *rnames[]= {"nrisk","nevent","ncensor", "p", "cumhaz", "std", "influence.pstate", ""}; SEXP setemp; double **pmat, **vmat=0, *cumhaz, *usave=0; /* =0 to silence -Wall warning */ int *ncensor, **nrisk, **nevent; @ Now set up pointers for all of the R objects sent to us. The two that will be updated need to be replaced by duplicates. <>= ntime= asInteger(ntime2); nperson = LENGTH(cstate2); /* number of unique subjects */ n = LENGTH(sort12); /* number of observations in the data */ PROTECT(cstate2 = duplicate(cstate2)); cstate = INTEGER(cstate2); entry= REAL(ftime2); etime= entry + n; sort1= INTEGER(sort12); sort2= INTEGER(sort22); status= INTEGER(status2); wt = REAL(wt2); id = INTEGER(id2); PROTECT(p2 = duplicate(p2)); /*copy of initial prevalence */ p = REAL(p2); nstate = LENGTH(p2); /* number of states */ i0 = REAL(i02); sefit = asInteger(sefit2); /* allocate space for the output objects ** Ones that are put into a list do not need to be protected */ PROTECT(rlist=mkNamed(VECSXP, rnames)); setemp = SET_VECTOR_ELT(rlist, 0, allocMatrix(INTSXP, ntime, nstate)); nrisk = imatrix(INTEGER(setemp), ntime, nstate); /* time by state */ setemp = SET_VECTOR_ELT(rlist, 1, allocMatrix(INTSXP, ntime, nstate)); nevent = imatrix(INTEGER(setemp), ntime, nstate); /* time by state */ setemp = SET_VECTOR_ELT(rlist, 2, allocVector(INTSXP, ntime)); ncensor = INTEGER(setemp); /* total at each time */ setemp = SET_VECTOR_ELT(rlist, 3, allocMatrix(REALSXP, ntime, nstate)); pmat = dmatrix(REAL(setemp), ntime, nstate); setemp = SET_VECTOR_ELT(rlist, 4, allocMatrix(REALSXP, nstate*nstate, ntime)); cumhaz = REAL(setemp); if (sefit >0) { setemp = SET_VECTOR_ELT(rlist, 5, allocMatrix(REALSXP, ntime, nstate)); vmat= dmatrix(REAL(setemp), ntime, nstate); } if (sefit >1) { /* the max space is larger for a matrix than a vector ** This is pure sneakiness: if I allocate a vector then n*nstate*(ntime+1) ** may overflow, as it is an integer argument. Using the rows and cols of ** a matrix neither overflows. But once allocated, I can treat setemp ** like a vector since usave is a pointer to double, which is bigger than ** integer and won't overflow. */ setemp = SET_VECTOR_ELT(rlist, 6, allocMatrix(REALSXP, n*nstate, ntime+1)); usave = REAL(setemp); } /* allocate space for scratch vectors */ ws = (double *) R_alloc(2*nstate, sizeof(double)); /*weighted number in state */ temp2 = ws + nstate; ns = (int *) R_alloc(2*nstate, sizeof(int)); nev = ns + nstate; atrisk = (int *) R_alloc(2*nperson, sizeof(int)); dstate = atrisk + nperson; wtp = (double *) R_alloc(nperson, sizeof(double)); hmat = (double**) dmatrix((double *)R_alloc(nstate*nstate, sizeof(double)), nstate, nstate); chaz = (double**) dmatrix((double *)R_alloc(nstate*nstate, sizeof(double)), nstate, nstate); if (sefit >0) umat = (double**) dmatrix((double *)R_alloc(nperson*nstate, sizeof(double)), nstate, nperson); /* R_alloc does not zero allocated memory */ for (i=0; i>= if (sefit ==1) { dptr = i0; for (j=0; j1) { /* copy influence, and save it */ dptr = i0; for (j=0; j>= itime =0; /*current time index, for output arrays */ eptr = 0; /*index to sort1, the entry times */ for (i=0; i> <> /* Take the current events and censors out of the risk set */ for (; i0) cstate[id[j]] = status[j]-1; /*new state */ atrisk[id[j]] =0; } else break; } itime++; } @ The key variables for the computation are the matrix $H$ and the current prevalence vector $P$. $H$ is created anew at each unique time point. Row $j$ of $H$ concerns everyone in state $j$ just before the time point, and contains the transitions at that time point. So the $jk$ element is the (weighted) fraction who change from state $j$ to state $k$, and the $jj$ element the fraction who stay put. Each row of $H$ by definition sums to 1. If no one is in the state then the $jj$ element is set to 1. A second version which we call H2 has 1 subtracted from each diagonal giving row sums are 0, we go back and forth depending on which is needed at the moment. If there are no events at this time point $P$ and $U$ do not update. <>= for (j=0; j0) { newstate = status[k] -1; /* 0 based subscripts */ oldstate = cstate[id[k]]; if (oldstate != newstate) { /* A "move" to the same state does not count */ dstate[id[k]] = newstate; nev[newstate]++; wevent += wt[k]; hmat[oldstate][newstate] += wt[k]; } } else ncensor[itime]++; } else break; } if (wevent > 0) { /* there was at least one move with weight > 0 */ /* finish computing H */ for (j=0; j0) { temp =0; for (k=0; k0) { <> } <> } @ The most complicated part of the code is the update of the per subject influence matrix $U$. The influence for a subject is the derivative of the current estimates wrt the case weight of that subject. Since $p$ is a vector the influence $U$ is easily represented as a matrix with one row per subject and one column per state. Refer to equation \eqref{ci} for the derivation. Let $m$ and $n$ be the old and new states for subject $i$, and $n_m$ the sum of weights for all subjects at risk in state $m$. Then \begin{equation*} U_{ij}(t) = \sum_k \left[ U_{ik}(t-)H_{kj}\right] + p_m(t-)(I_{n=j} - H_{mj})/ n_m \end{equation*} \begin{enumerate} \item The first term above is simple matrix multiplication. \item The second adds a vector with mean zero. \end{enumerate} If standard errors are not needed we can skip this calculation. <>= /* Update U, part 1 U = U %*% H -- matrix multiplication */ for (j=0; j>= /* Finally, update chaz and p. */ for (j=0; j>= /* store into the matrices that will be passed back */ for (j=0; j0) { temp =0; for (k=0; k 1) for (k=0; k>= /* return a list */ UNPROTECT(3); return(rlist); @ survival/noweb/ratetable.Rnw0000644000176200001440000004320213537676563015720 0ustar liggesusers\documentclass{article} \usepackage{noweb} \usepackage[pdftex]{graphicx} \addtolength{\textwidth}{1in} \addtolength{\oddsidemargin}{-.5in} \setlength{\evensidemargin}{\oddsidemargin} \newcommand{\myfig}[1]{\resizebox{\textwidth}{!} {\includegraphics{#1.pdf}}} \SweaveOpts{keep.source=TRUE} \title{Rate tables in the Survival Package} \author{Terry Therneau and Megan O'Byrne} \begin{document} \maketitle <>= options(width=60, continue=" ") makefig <- function(file, top=1, right=1, left=4) { pdf(file, width=9.5, height=7, pointsize=18) par(mar=c(4, left, top, right) +.1) } @ \section{Introduction} This is a short introduction to how the United States rate tables found in the \emph{survival} package are generated. Data for new calendar years is regularly added; this is stored in the data directory and processed by a function there into the current rate table. Much more detail on how the rate tables are used can be found in a series of technical reports from the Department of Health Science Research, Mayo Clinic. \section{Data} The US death rates for 1940 to 2000 are based on the US Decennial Rate tables, published by the National Center for Health Statistics, within the Centers for Disease Control. The detailed address of the web pages changes, at the time of typing this \texttt{www.cdc.gov/nchs} will reach the top level, from which the documents can be found by using \emph{rate tables} in the search box. The structure of the tables has changed slightly over the years $$ \begin{tabular}{c|l|l} Year & Age & Race \\ \hline 1940 & 0--110 & total, white, black, other \\ 1950--1960 & 0--109 + first year & total, white, non-white \\ 1970--1990 & 0-1d, 1-7d, 7-28d, 28-365d, 1-109 & total, white, nowhite, black \\ 2000 & 0-1d, 1-7d, 7-28d, 28-365d, 1-109 & total, white, black \\ \end{tabular} $$ The first year data for 1950-1960 is a separate table, with much more detail than the other years. Publication dates of the decennial tables has lagged, however, with the 2000 table not appearing until late in 2008. The 2000 decennial data for individual states has not yet appeared as of this writing (Feb 2011). Data for 2001 and forward is taken from the annual life tables, which appear more promptly. The annual tables contain total, white, and black, do not break up the first year of life, and extend only to age 99. First year of life data for the later years is obtained from Report GMWK264A: Deaths under 1 Year, by Month, Age, Race, and Sex: United States, 1999-2007, which can be found in the National Vital Statistics System (within NCHS). The log(hazard) for the later ages is extrapolated from ages 70-99 using a smoothing spline. The source code for the \emph{survival} package contains a \texttt{noweb} directory with the code for this report, amongst other things, and a sub-directory \texttt{noweb/rates} containing the raw data for the rate tables. The decennial files have been subset and normalized into a common form, and found as \texttt{usdecennial.dat} and \texttt{minndecennial.dat}. The US data contains the $q$ values for all combinations of age (0-1 day, 2-7 d, 7-28 d, 28-365 d), sex (male, female) and race (total, white, black). The Minnesota table does not subdivide the first year of life, and contains data only for total and white. For individual years the \texttt{noweb/rates} directory contains individual files for each year. As new rate tables are published a new file is added with the following procedure. \begin{enumerate} \item The NCHS source is a pdf file, with tables for each combinations of race and sex. These will be saved as individual files with names like \texttt{us2006tf.csv}, \texttt{us2006wf.csv}, etc for the total female and white female tables, respectively. \item The code below, with names modified appropriately for the particular year, is used to create a single file containing the $q$ values for each age. \end{enumerate} <>= basename <- "us2006" suffix <- c("tm", "tf", "wm", "wf", "bm", "bf") #total male, ..., black female newdata <- list(age=paste(0:99, 1:100, sep='-')) for (i in 1:6) { tdata <- read.csv(paste(basename, suffix[j], '.csv', sep=''), col.names=c('age', 'q', 'n', 'deaths', 'L', "n.older", "expect", 'dummy'), sep=',', header=FALSE) newdata[[suffix[j]]] <- 10000* tdata$q } write.table(newdata, file=paste(basename, "dat", sep='.')) @ The infant mortality data is also updated as new information becomes available, usually by hand editing new lines into the file. Updates of the life tables and the infant mortality tables on the NCHS site are not coordinated. \section{Rate tables} A rate table is an array containing daily hazard rates $\lambda$. The US tables contain annual death rates $q = 1 - exp(\lambda* 365.25)$. (Technically one could keep track of leap year, using either 365 or 366 as appropriate for each calendar year. We do not.) They can be cross-classified in any way that is desired, various attributes of the table describe this cross classification. The attributes are \begin{description} \item[dim] The dimensions of the array \item[dimnames] The labels for each dimension. These two attributes act identically to those for a normal array. \item[dimid] A name for each dimension. For the survexp.us table these are ``age'', ``sex'', and ``year''. For survexp.usr they are ``age'', ``sex'', ``race'', and ``year''. \item[type] A vector giving the type of each dimension. One of \begin{itemize} \item 1: a categorical trait such as sex, race, smoking status, etc. As a particular subject is followed forward in time, this trait does not change. \item 2: a continuous trait that changes over time such as age. As a subject is followed forward, different rows of the ratetable apply when calculating the expected hazard. If I start at age 2 with 5 years of follow-up, the rates for ages 2--6 will be used. \item 3: a continuous trait, as in 2, but represented as a date. This allows the routine to be more intelligent in handling the multiple date formats available. \item 4: the calendar year dimension of a US rate table. \end{itemize} \item[cutpoints] A list with one element for each dimension \begin{itemize} \item If \texttt{type==1} the element should be NULL \item Otherwise it contains the vector of starting points for each row of the dimension, e.g., for the age dimension this defines the age range over which each row of the table applies. Because we only define the start of each row, the last row of the table implicitly extends to infinity. \end{itemize} \item[summary] a function to summarize data. \end{description} \section{Creating the US table} \subsection{Data} First read in the US Decennial data for 1940 to 2000, and fill the $q$ values into a temporary array. The first 3 ages are the q's for days 0-1, 1-7, and 7-28, the fourth is the q for the entire first year. Change the array to one of daily hazard rates. For the 4th row, make it so the sum of the first year's hazard is correct, i.e., 1*row1 + 6*row2 + 21*row3 + 337.25* row4 = -log(1-q) <>= decdata <- read.table('rates/usdecennial.dat', header=TRUE) temp <- array(decdata$q[decdata$race=='total'], dim=c(113,2,7)) usd <- -log(1- temp) usd[4,,] <- usd[4,,] - (usd[1,,] + usd[2,,] + usd[3,,]) usd[2,,] <- usd[2,,] /6 #days 1-7 usd[3,,] <- usd[3,,] /21 #days 7-28 usd[4,,] <- usd[4,,] /337.25 usd[5:113,,] <- usd[5:113,,]/365.25 @ Note a change from some earlier releases of the code. There are 36524 days per century, so I used 365.24. However the year 2000 is the exception to the exception: ``Every 4 years is a leap year, unless divisible by 100; unless divisible by 1000". So over the lifetime that these tables will be used 365.25 is the right number. (If they are still in use in the year 2100, some one else will be maintaining the code.) Plus, using .24 confused everyone. Now pull in the single year tables. The \texttt{temp3} array is made full size, even though the single year data is missing the subdivision of year 1 of life, and the older ages of 100-109. Thus the 4:103 subscript below. We keep the data for all races in \texttt{usy} but kept only the total column for \texttt{usd}, the reason why will appear when creating the table by race. <>= i <- 2004 while(file.exists(paste('rates/us', i, '.dat', sep=''))) i <- i+1 singleyear <- 1996:(i-1) #the data we have nsingle <- length(singleyear) temp <- array(0, dim=c(113, 2, 3, nsingle)) #age, sex, race, year for (i in 1:nsingle) { tdata <- read.table(paste("rates/us", singleyear[i], ".dat", sep=''), header=TRUE) temp[4:103,,,i] <- -log(1- c(as.matrix(tdata[,-1]))/100000) } @ Pull in the first year of life breakdown. We use that to compute the proportion of the first year hazard that falls into each interval. Then rescale to daily hazards. <>= infant <- read.csv('rates/usinfant.dat') iyears <- unique(infant$year) # extract the deaths for total (all races) # then scale each year/sex group out as proportions deaths <- array(as.matrix(infant[,3:8]), dim=c(4, length(iyears), 2,3), dimnames=list(c('0', '1-6','7-27', '28-365'), iyears, c("Male", "Female"), c("total", "white", "black"))) for (i in 1:length(iyears)) { for (j in 1:2) { for (k in 1:3) { deaths[,i,j,k] <- deaths[,i,j,k]/sum(deaths[,i,j,k]) } } } # Partition out the total 1 year hazard, and then rescale to daily hazards usy <- array(0., dim=c(113, 2,3, nsingle)) indx <- match(singleyear, iyears) indx[singleyear < min(iyears)] <- 1 for (i in 1:nsingle) { for (j in 1:2) { for (k in 1:3) { usy[1:4,j,k,i] <- temp[4,j,k,i] * deaths[,indx[i],j,k]/c(1,6,21, 337.25) } } } usy[5:113,,,] <- temp[5:113,,,] /365.25 @ \begin{figure} \myfig{extrapolate} \caption{Plot of selected decennial and annual year data, along with a spline fit from ages 70--99 and the extrapolation of that fit to age 109.} \label{fig:extrapolate} \end{figure} Now we want to extend the yearly data to ages 100--109. There are two reasons for this, the simple is so that we can match the decennial data. The more compelling one is to note how the expected survival routines make use of the rate tables: namely that for any continuous variable the largest dimension of the rate table is used for values exceeding the dimension. If a rate table extends to the year 2007, then for follow-up in 2008 and later the 2007 data is used; if age extends to 99 then the age 99 values are used for all earlier ages. Thus it is not a question of \emph{whether} we will extrapolate for age but \emph{how} we do so, with a constant hazard after age 99 or one with a more rational basis. We have noticed that the log(hazard) is remarkably linear after age 95, and so do the extrapolation on that scale. Figure \ref{fig:extrapolate} shows the results for the last 4 decennial years, along with 2006. % Because I like to manage my own plots as floating figures, I need to % use plot=F. Otherwise Sweave tries to do it. % <>= for (i in 1:2) { for (j in 1:3) { for (k in 1:nsingle) { loghaz <- log(usy[4+ 80:99, i, j, k]) tfit <- smooth.spline(80:99, loghaz, df=8) usy[4+ 100:109, i, j, k] <- exp(predict(tfit, 100:109)$y) } } } makefig("extrapolate.pdf") matplot(80:109, 100000* usd[4+ 80:109, 2, 4:7], log='y', xlab="Age", ylab="Daily hazard * 100,000", pch='7890') for (i in 4:7) { loghaz <- log(usd[4+ 80:99,2, i] * 100000) tfit <- smooth.spline(80:99, loghaz, df=8) lines(80:109, exp(predict(tfit, 80:109)$y), col=i-3) } temp <- usd[4+99,2,6]*100000 segments(99, temp, 109, temp, col=3, lty=2) ty <- match(2006, singleyear) points(80:99, 100000*usy[4+80:99,2,1,ty], col=1, pch=2) lines(80:109, 100000*usy[4+80:109,2,1,ty]) dev.off() @ \subsection{US total table} The US total table will have an entry for each single calendar year, and dimensions of age, sex, and year. The data for 1940 to 2000 is based on decennial values, interpolated across years, and for 2001 and onward on single year data. <>= years <- seq(1940, max(singleyear)) survexp.us <- array(0., dim=c(113, 2, length(years))) single2 <- singleyear[singleyear > 2000] xtemp <- c(1940, 1950, 1960, 1970, 1980, 1990, 2000, single2) for (i in 1:nrow(usd)) { for (j in 1:2) { #so what if loops are slow, we only do this once ytemp <- c(usd[i,j,], usy[i,j,1, match(single2, singleyear)]) survexp.us[i,j,] <- approx(xtemp, ytemp, xout=years)$y } } @ Rate tables store dates as the number of days since Jan 1 1960. This is historical and not every going to change. Users should make use of the \texttt{ratetableDate} function. I can't use it here, unfortunately, because this code is run in the process of making the survival library, so I cannot count on the function's existence. <>= if (exists('as.Date')) { # R datecut <- as.Date(paste(years, '/01/01', sep=''))- as.Date('1960/01/01') datecut <- as.integer(datecut) }else if (exists('month.day.year')) { #Splus datecut <- julian(1,1, years, origin=c(1,1,1960)) }else stop("Cannot find appropriate routine for dates") @ Adding the attributes is the fussy part, since these are what define a rate table. Users making their own table ``mytable''should always follow the exercise with \texttt{is.ratetable(mytable)}, but again I cannot count on the function being present. The summary function is called with a data frame containing values for each of the dimensions, and prints a message giving the observed range of each. Its primary function is provide a warning to users to invalid input; the most common is when someone uses age in years instead of in days and all the subjects are treated as being 1-100 days old. <>= attributes(survexp.us) <- list( dim= c(113,2, length(years)), dimnames = list(c('0-1d','1-7d', '7-28d', '28-365d', as.character(1:109)), c("male", "female"), years), dimid =c("age", "sex", "year"), type = c(2,1,4), cutpoints= list(c(0,1,7,28,1:109 * 365.25), NULL, datecut), summary = function(R) { x <- c(format(round(min(R[,1]) /365.25, 1)), format(round(max(R[,1]) /365.25, 1)), sum(R[,2]==1), sum(R[,2]==2)) x2<- as.Date(c(min(R[,3]), max(R[,3])), origin='1960/01/01') paste(" age ranges from", x[1], "to", x[2], "years\n", " male:", x[3], " female:", x[4], "\n", " date of entry from", x2[1], "to", x2[2], "\n") }) class(survexp.us) <- 'ratetable' @ \subsection{US race table} The US tables have not been consistent in their race breakdown. \begin{itemize} \item Decennial tables \begin{itemize} \item 1940: total, white, nonwhite, black \item 1950--60: total, white, nonwhite, \item 1970--90: total, white, nonwhite, black \item 2000: total, white, black \end{itemize} \item 1997--2006 Annual tables: total, white, black \end{itemize} We will create a table for white/black only, and use the nonwhite data for 1950 and 1960. Then create our usd array as was done for the total rates. <>= temp <-array(0., dim=c(113,2,2,7)) #age, sex, race, year temp[,,1,] <- decdata$q[decdata$race=='white'] temp[,,2,c(1,4,5,6,7)] <- decdata$q[decdata$race=='black'] temp[,,2, 2:3] <- decdata$q[decdata$race=='nonwhite' & (decdata$year==1950 |decdata$year==1970)] usd <- -log(1- temp) usd[4,,,] <- usd[4,,,] - (usd[1,,,] + usd[2,,,] + usd[3,,,]) usd[2,,,] <- usd[2,,,]/6 #days 2-7 usd[3,,,] <- usd[3,,,]/21 #days 8-28 usd[4,,,] <- usd[4,,,]/337.25 #days 29-365.25 usd[5:113,,,] <- usd[5:113,,,] / 365.25 @ Building the remainder of the table is almost identical to the prior code. <>= survexp.usr <- array(0., dim=c(113, 2, 2, length(years))) for (i in 1:113) { for (j in 1:2) { #so what if loops are slow, we only do this once for (k in 1:2) { #race ytemp <- c(usd[i,j,k,], usy[i,j,k+1, match(single2, singleyear)]) survexp.usr[i,j,k,] <- approx(xtemp, ytemp, xout=years)$y } } } attributes(survexp.usr) <- list( dim= c(113,2,2, length(years)), dimnames = list(c('0-1d','1-7d', '7-28d', '28-365d', as.character(1:109)), c("male", "female"), c("white", "black"), years), dimid =c("age", "sex", "race", "year"), type = c(2,1,1,4), cutpoints= list(c(0,1,7,28,1:109 * 365.25), NULL, NULL, datecut), summary = function(R) { x <- c(format(round(min(R[,1]) /365.25, 1)), format(round(max(R[,1]) /365.25, 1)), sum(R[,2]==1), sum(R[,2]==2), sum(R[,3]==1), sum(R[,3]==2)) if (is.R()) x2<- as.Date(c(min(R[,4]), max(R[,4])), origin='1960/01/01') else x2 <- timeDate(julian=c(min(R[,4]), max(R[,4]))) paste(" age ranges from", x[1], "to", x[2], "years\n", " male:", x[3], " female:", x[4], "\n", " date of entry from", x2[1], "to", x2[2], "\n", " white:",x[7], " black:", x[8], "\n") }) oldClass(survexp.usr) <- "ratetable" @ And finally, save the tables away for use in the survival package. <>= save(survexp.us, survexp.usr, file="survexp.rda") @ \end{document} survival/noweb/concordance.Rnw0000644000176200001440000016460714053747041016230 0ustar liggesusers\section{Concordance} \subsection{Main routine} The concordance statistic is the most used measure of goodness-of-fit in survival models. In general let $y_i$ and $x_i$ be observed and predicted data values. A pair of obervations $i$, $j$ is considered condordant if either $y_i > y_j, x_i > x_j$ or $y_i < y_j, x_i < x_j$. The concordance is the fraction of concordant pairs. For a Cox model remember that the predicted survival $\hat y$ is longer if the risk score $X\beta$ is lower, so we have to flip the definition and count ``discordant'' pairs, this is done at the end of the routine. One wrinkle is what to do with ties in either $y$ or $x$. Such pairs can be ignored in the count (treated as incomparable), treated as discordant, or given a score of 1/2. \begin{itemize} \item Kendall's $\tau$-a scores ties as 0. \item Kendall's $\tau$-b and the Goodman-Kruskal $\gamma$ ignore ties in either $y$ or $x$. \item Somers' $d$ treats ties in $y$ as incomparable, pairs that are tied in $x$ (but not $y$) score as 1/2. The AUC from logistic regression is equal to Somers' $d$. \end{itemize} All three of the above range from -1 to 1, the concordance is $(d +1)/2$. For survival data any pairs which cannot be ranked with certainty are considered incomparable. For instance $y_i$ is censored at time 10 and $y_j$ is an event (or censor) at time 20. Subject $i$ may or may not survive longer than subject $j$. Note that if $y_i$ is censored at time 10 and $y_j$ is an event at time 10 then $y_i > y_j$. Observations that are in different strata are also incomparable, since the Cox model only compares within strata. The program creates 4 variables, which are the number of concordant pairs, discordant, tied on time, and tied on $x$ but not on time. The default concordance is based on the Somers'/AUC definition, but all 4 values are reported back so that a user can recreate Kendall's or Goodmans values if desired. Here is the main routine. <>= concordance <- function(object, ...) UseMethod("concordance") concordance.formula <- function(object, data, weights, subset, na.action, cluster, ymin, ymax, timewt=c("n", "S", "S/G", "n/G", "n/G2", "I"), influence=0, ranks=FALSE, reverse=FALSE, timefix=TRUE, keepstrata=10, ...) { Call <- match.call() # save a copy of of the call, as documentation timewt <- match.arg(timewt) if (missing(ymin)) ymin <- NULL if (missing(ymax)) ymax <- NULL index <- match(c("data", "weights", "subset", "na.action", "cluster"), names(Call), nomatch=0) temp <- Call[c(1, index)] temp[[1L]] <- quote(stats::model.frame) special <- c("strata", "cluster") temp$formula <- if(missing(data)) terms(object, special) else terms(object, special, data=data) mf <- eval(temp, parent.frame()) # model frame if (nrow(mf) ==0) stop("No (non-missing) observations") Terms <- terms(mf) Y <- model.response(mf) if (inherits(Y, "Surv")) { if (timefix) Y <- aeqSurv(Y) } else { if (is.factor(Y) && (is.ordered(Y) || length(levels(Y))==2)) Y <- Surv(as.numeric(Y)) else if (is.numeric(Y) && is.vector(Y)) Y <- Surv(Y) else stop("left hand side of the formula must be a numeric vector, survival object, or an orderable factor") if (timefix) Y <- aeqSurv(Y) } n <- nrow(Y) wt <- model.weights(mf) offset<- attr(Terms, "offset") if (length(offset)>0) stop("Offset terms not allowed") stemp <- untangle.specials(Terms, "strata") if (length(stemp$vars)) { if (length(stemp$vars)==1) strat <- mf[[stemp$vars]] else strat <- strata(mf[,stemp$vars], shortlabel=TRUE) Terms <- Terms[-stemp$terms] } else strat <- NULL # if "cluster" was an argument, use it, otherwise grab it from the model group <- model.extract(mf, "cluster") cluster<- attr(Terms, "specials")$cluster if (length(cluster)) { tempc <- untangle.specials(Terms, 'cluster', 1:10) ord <- attr(Terms, 'order')[tempc$terms] if (any(ord>1)) stop ("Cluster can not be used in an interaction") cluster <- strata(mf[,tempc$vars], shortlabel=TRUE) #allow multiples Terms <- Terms[-tempc$terms] # toss it away } if (length(group)) cluster <- group x <- model.matrix(Terms, mf)[,-1, drop=FALSE] #remove the intercept if (ncol(x) > 1) stop("Only one predictor variable allowed") if (!is.null(ymin) & (length(ymin)> 1 || !is.numeric(ymin))) stop("ymin must be a single number") if (!is.null(ymax) & (length(ymax)> 1 || !is.numeric(ymax))) stop("ymax must be a single number") if (!is.logical(reverse)) stop ("the reverse argument must be TRUE/FALSE") fit <- concordancefit(Y, x, strat, wt, ymin, ymax, timewt, cluster, influence, ranks, reverse, keepstrata=keepstrata) na.action <- attr(mf, "na.action") if (length(na.action)) fit$na.action <- na.action fit$call <- Call class(fit) <- 'concordance' fit } print.concordance <- function(x, digits= max(1L, getOption("digits") - 3L), ...) { if(!is.null(cl <- x$call)) { cat("Call:\n") dput(cl) cat("\n") } omit <- x$na.action if(length(omit)) cat("n=", x$n, " (", naprint(omit), ")\n", sep = "") else cat("n=", x$n, "\n") if (length(x$concordance) > 1) { # result of a call with multiple fits tmat <- cbind(concordance= x$concordance, se=sqrt(diag(x$var))) print(round(tmat, digits=digits), ...) cat("\n") } else cat("Concordance= ", format(x$concordance, digits=digits), " se= ", format(sqrt(x$var), digits=digits), '\n', sep='') if (!is.matrix(x$count) || nrow(x$count < 11)) print(round(x$count,2)) invisible(x) } <> <> @ The concordancefit function is broken out separately, since it is called by all of the methods. It is also called directly by the the \code{coxph} routine. If $y$ is not a survival quantity, then all of the options for the \code{timewt} parameter lead to the same result. <>= concordancefit <- function(y, x, strata, weights, ymin=NULL, ymax=NULL, timewt=c("n", "S", "S/G", "n/G", "n/G2", "I"), cluster, influence=0, ranks=FALSE, reverse=FALSE, timefix=TRUE, keepstrata=10, robustse =TRUE) { # The coxph program may occassionally fail, and this will kill the C # routine further below. So check for it. if (any(is.na(x)) || any(is.na(y))) return(NULL) timewt <- match.arg(timewt) if (!robustse) {ranks <- FALSE; influence =0;} # these should only occur if something other package calls this routine if (!is.Surv(y)) { if (is.factor(y) && (is.ordered(y) || length(levels(y))==2)) y <- Surv(as.numeric(y)) else if (is.numeric(y) && is.vector(y)) y <- Surv(y) else stop("left hand side of the formula must be a numeric vector, survival object, or an orderable factor") if (timefix) y <- aeqSurv(y) } n <- length(y) if (length(x) != n) stop("x and y are not the same length") if (missing(strata) || length(strata)==0) strata <- rep(1L, n) if (length(strata) != n) stop("y and strata are not the same length") if (missing(weights) || length(weights)==0) weights <- rep(1.0, n) else if (length(weights) != n) stop("y and weights are not the same length") type <- attr(y, "type") if (type %in% c("left", "interval")) stop("left or interval censored data is not supported") if (type %in% c("mright", "mcounting")) stop("multiple state survival is not supported") nstrat <- length(unique(strata)) if (!is.logical(keepstrata)) { if (!is.numeric(keepstrata)) stop("keepstrat argument must be logical or numeric") else keepstrata <- (nstrat <= keepstrata) } if (timewt %in% c("n", "I") && nstrat > 10 && !keepstrata) { # Special trickery for matched case-control data, where the # number of strata is huge, n per strata is small, and compute # time becomes excessive. Make the data all one strata, but over # disjoint time intervals stemp <- as.numeric(as.factor(strata)) -1 if (ncol(y) ==3) { delta <- 2+ max(y[,2]) - min(y[,1]) y[,1] <- y[,1] + stemp*delta y[,2] <- y[,2] + stemp*delta } else { delta <- max(y[,1]) +2 m1 <- rep(-1L, nrow(y)) y <- Surv(m1 + stemp*delta, y[,1] + stemp*delta, y[,2]) } strata <- rep(1L, n) nstrat <- 1 } # This routine is called once per stratum docount <- function(y, risk, wts, timeopt= 'n', timefix) { n <- length(risk) # this next line is mostly invoked in stratified logistic, where # only 1 event per stratum occurs. All time weightings are the same # don't waste time even if the user asked for something different if (sum(y[,ncol(y)]) <2) timeopt <- 'n' sfit <- survfit(y~1, weights=wts, se.fit=FALSE, timefix=timefix) etime <- sfit$time[sfit$n.event > 0] esurv <- sfit$surv[sfit$n.event > 0] if (length(etime)==0) { # the special case of a stratum with no events (it happens) # No need to do any more work return(list(count= rep(0.0, 6), influence=matrix(0.0, n, 5), resid=NULL)) } if (timeopt %in% c("S/G", "n/G", "n/G2")) { temp <- y temp[,ncol(temp)] <- 1- temp[,ncol(temp)] # switch event/censor gfit <- survfit(temp~1, weights=wts, se.fit=FALSE, timefix=timefix) # G has the exact same time values as S gsurv <- c(1, gfit$surv) # We want G(t-) gsurv <- gsurv[which(sfit$n.event > 0)] } npair <- (sfit$n.risk- sfit$n.event)[sfit$n.event>0] temp <- ifelse(esurv==0, 0, esurv/npair) # avoid 0/0 timewt <- switch(timeopt, "S" = sum(wts)*temp, "S/G" = sum(wts)* temp/ gsurv, "n" = rep(1.0, length(npair)), "n/G" = 1/gsurv, "n/G2"= 1/gsurv^2, "I" = rep(1.0, length(esurv)) ) if (!is.null(ymin)) timewt[etime < ymin] <- 0 if (!is.null(ymax)) timewt[etime > ymax] <- 0 timewt <- ifelse(is.finite(timewt), timewt, 0) # 0 at risk case # order the data: reverse time, censors before deaths if (ncol(y)==2) { sort.stop <- order(-y[,1], y[,2], risk) -1L } else { sort.stop <- order(-y[,2], y[,3], risk) -1L #order by endpoint sort.start <- order(-y[,1]) -1L } # match each prediction score to the unique set of scores # (to deal with ties) utemp <- match(risk, sort(unique(risk))) bindex <- btree(max(utemp))[utemp] storage.mode(y) <- "double" # just in case y is integer storage.mode(wts) <- "double" if (robustse) { if (ncol(y) ==2) fit <- .Call(Cconcordance3, y, bindex, wts, rev(timewt), sort.stop, ranks) else fit <- .Call(Cconcordance4, y, bindex, wts, rev(timewt), sort.start, sort.stop, ranks) # The C routine gives back an influence matrix which has columns for # concordant, discordant, tied on x but not y, tied on y, and tied # on both x and y. dimnames(fit$influence) <- list(NULL, c("concordant", "discordant", "tied.x", "tied.y", "tied.xy")) if (ranks) { if (ncol(y)==2) dtime <- y[y[,2]==1, 1] else dtime <- y[y[,3]==1, 2] temp <- data.frame(time= sort(dtime), fit$resid) names(temp) <- c("time", "rank", "timewt", "casewt", "variance") fit$resid <- temp[temp[,3] > 0,] # don't return zeros } } else { if (ncol(y) ==2) fit <- .Call(Cconcordance5, y, bindex, wts, rev(timewt), sort.stop) else fit <- .Call(Cconcordance6, y, bindex, wts, rev(timewt), sort.start, sort.stop) } fit } if (nstrat < 2) { fit <- docount(y, x, weights, timewt, timefix=timefix) count2 <- fit$count[1:5] vcox <- fit$count[6] fit$count <- fit$count[1:5] if (robustse) imat <- fit$influence if (ranks) resid <- fit$resid } else { strata <- as.factor(strata) ustrat <- levels(strata)[table(strata) >0] #some strata may have 0 obs tfit <- lapply(ustrat, function(i) { keep <- which(strata== i) docount(y[keep,,drop=F], x[keep], weights[keep], timewt, timefix=timefix) }) temp <- t(sapply(tfit, function(x) x$count)) fit <- list(count = temp[,1:5]) count2 <- colSums(fit$count) if (!keepstrata) fit$count <- count2 vcox <- sum(temp[,6]) if (robustse) { imat <- do.call("rbind", lapply(tfit, function(x) x$influence)) # put it back into data order index <- match(1:n, (1:n)[order(strata)]) imat <- imat[index,] if (ranks) { nr <- lapply(tfit, function(x) nrow(x$resid)) resid <- do.call("rbind", lapply(tfit, function(x) x$resid)) resid$strata <- rep(ustrat, nr) } } } npair <- sum(count2[1:3]) if (!keepstrata && is.matrix(fit$count)) fit$count <- colSums(fit$count) somer <- (count2[1] - count2[2])/npair if (robustse) { dfbeta <- weights*((imat[,1]- imat[,2])/npair - (somer/npair)* rowSums(imat[,1:3])) if (!missing(cluster) && length(cluster)>0) { dfbeta <- tapply(dfbeta, cluster, sum) dfbeta <- ifelse(is.na(dfbeta),0, dfbeta) # if cluster is a factor } var.somer <- sum(dfbeta^2) rval <- list(concordance = (somer+1)/2, count=fit$count, n=n, var = var.somer/4, cvar=vcox/(4*npair^2)) } else rval <- list(concordance = (somer+1)/2, count=fit$count, n=n, cvar=vcox/(4*npair^2)) if (is.matrix(rval$count)) colnames(rval$count) <- c("concordant", "discordant", "tied.x", "tied.y", "tied.xy") else names(rval$count) <- c("concordant", "discordant", "tied.x", "tied.y", "tied.xy") if (influence == 1 || influence==3) rval$dfbeta <- dfbeta/2 if (influence >=2) rval$influence <- imat if (ranks) rval$ranks <- resid if (reverse) { # flip concordant/discordant values but not the labels rval$concordance <- 1- rval$concordance if (!is.null(rval$dfbeta)) rval$dfbeta <- -rval$dfbeta if (!is.null(rval$influence)) { rval$influence <- rval$influence[,c(2,1,3,4,5)] colnames(rval$influence) <- colnames(rval$influence)[c(2,1,3,4,5)] } if (is.matrix(rval$count)) { rval$count <- rval$count[, c(2,1,3,4,5)] colnames(rval$count) <- colnames(rval$count)[c(2,1,3,4,5)] } else { rval$count <- rval$count[c(2,1,3,4,5)] names(rval$count) <- names(rval$count)[c(2,1,3,4,5)] } if (ranks) rval$ranks$rank <- -rval$ranks$rank } rval } @ \subsection{Methods} Methods are defined for lm, survfit, and coxph objects. Detection of strata, weights, or clustering is the main nuisance, since those are not passed back as part of coxph or survreg objects. Glm and lm objects have the model frame by default, but that can be turned off by a user. This routine gets the X, Y, and other portions from the result of a particular fit object. <>= cord.getdata <- function(object, newdata=NULL, cluster=NULL, need.wt, timefix=TRUE) { # For coxph object, don't reconstruct the model frame unless we must. # This will occur if weights, strata, or cluster are needed, or if # there is a newdata argument. Of course, if the model frame is # already present, then use it! Terms <- terms(object) specials <- attr(Terms, "specials") if (!is.null(specials$tt)) stop("cannot yet handle models with tt terms") if (!is.null(newdata)) { mf <- model.frame(object, data=newdata) y <- model.response(mf) if (!is.Surv(y)) { if (is.numeric(y) && is.vector(y)) y <- Surv(y) else stop("left hand side of the formula must be a numeric vector or a survival object") } if (timefix) y <- aeqSurv(y) rval <- list(y= y, x= predict(object, newdata)) # the type of prediction does not matter, as long as it is a # monotone transform of the linear predictor } else { mf <- object$model y <- object$y if (is.null(y)) { if (is.null(mf)) mf <- model.frame(object) y <- model.response(mf) } if (!is.Surv(y)) { y <- Surv(y) if (timefix) y <- aeqSurv(y) } # survival models will have already called timefix x <- object$linear.predictors # used by most if (is.null(x)) x <- object$fitted.values # used by lm if (is.null(x)) {object$na.action <- NULL; x <- predict(object)} rval <- list(y = y, x= x) } if (need.wt) { if (is.null(mf)) mf <- model.frame(object) rval$weights <- model.weights(mf) } if (!is.null(specials$strata)) { if (is.null(mf)) mf <- model.frame(object) stemp <- untangle.specials(Terms, 'strata', 1) if (length(stemp$vars)==1) rval$strata <- mf[[stemp$vars]] else rval$strata <- strata(mf[,stemp$vars], shortlabel=TRUE) } if (is.null(cluster)) { if (!is.null(specials$cluster)) { if (is.null(mf)) mf <- model.frame(object) tempc <- untangle.specials(Terms, 'cluster', 1:10) ord <- attr(Terms, 'order')[tempc$terms] rval$cluster <- strata(mf[,tempc$vars], shortlabel=TRUE) } else if (!is.null(object$call$cluster)) { if (is.null(mf)) mf <- model.frame(object) rval$cluster <- model.extract(mf, "cluster") } } else rval$cluster <- cluster rval } @ The methods themselves, which are near clones of each other. There is one portion of these that is not very clear. I use the trick from nearly all calls to model.frame to deal with arguments that might be there or might not, such as newdata. Construct a call by hand by first subsetting this call as Call[...], then replace the first element with the name of what I really want to call -- quote(cord.work) --, add any other args I want, and finally execute it with eval(). The problem is that this doesn't work; the routine can't find cord.work since it is not an exported function. A simple call to cord.work is okay, since function calls inherit from the survival namespace, but cfun isn't a function call, it is an expression. There are 3 possible solutions \begin{itemize} \item bad: change eval(cfun, parent.frame()) to eval(cfun, evironment(coxph)), or any other function from the survival library which has namespace::survival as its environment. If the user calls concordance with ymax=zed, say, we might not be able to find 'zed'. Especially if they had called concordance from within a function. We need the call chain. \item okay: use cfun[[1]] <- cord.work, which makes a copy of the entire cord.work function and stuffs it in. The function isn't too long, so this is okay. If cord.work fails, the label on its error message won't be as nice since it won't have ``cord.work'' in it. \item speculative: make a function and invoke it. This creates a new function in the survival namespace, but evaluates it in the current context. Using parent.frame() is important so that I don't accidentally pick up 'nfit' say, if the user had used a variable of that name as one of their arguments. \\ temp <- function(){} \\ body(temp, environment(coxph)) <- cfun\\ rval <- eval(temp(), parent.frame()) \end{itemize} <>= concordance.lm <- function(object, ..., newdata, cluster, ymin, ymax, influence=0, ranks=FALSE, timefix=TRUE, keepstrata=10) { Call <- match.call() fits <- list(object, ...) nfit <- length(fits) fname <- as.character(Call) # like deparse(substitute()) but works for ... fname <- fname[1 + 1:nfit] notok <- sapply(fits, function(x) !inherits(x, "lm")) if (any(notok)) { # a common error is to mistype an arg, "ramk=TRUE" for instance, # and it ends up in the ... list # try for a nice message in this case: the name of the arg if it # has one other than "object", fname otherwise indx <- which(notok) id2 <- names(Call)[indx+1] temp <- ifelse(id2 %in% c("","object"), fname, id2) stop(temp, " argument is not an appropriate fit object") } cargs <- c("ymin", "ymax","influence", "ranks", "keepstrata") cfun <- Call[c(1, match(cargs, names(Call), nomatch=0))] cfun[[1]] <- cord.work # or quote(survival:::cord.work) cfun$fname <- fname if (missing(newdata)) newdata <- NULL if (missing(cluster)) cluster <- NULL need.wt <- any(sapply(fits, function(x) !is.null(x$call$weights))) cfun$data <- lapply(fits, cord.getdata, newdata=newdata, cluster=cluster, need.wt=need.wt, timefix=timefix) rval <- eval(cfun, parent.frame()) rval$call <- Call rval } concordance.survreg <- function(object, ..., newdata, cluster, ymin, ymax, timewt=c("n", "S", "S/G", "n/G", "n/G2", "I"), influence=0, ranks=FALSE, timefix=FALSE, keepstrata=10) { Call <- match.call() fits <- list(object, ...) nfit <- length(fits) fname <- as.character(Call) # like deparse(substitute()) but works for ... fname <- fname[1 + 1:nfit] notok <- sapply(fits, function(x) !inherits(x, "survreg")) if (any(notok)) { # a common error is to mistype an arg, "ramk=TRUE" for instance, # and it ends up in the ... list # try for a nice message in this case: the name of the arg if it # has one other than "object", fname otherwise indx <- which(notok) id2 <- names(Call)[indx+1] temp <- ifelse(id2 %in% c("","object"), fname, id2) stop(temp, " argument is not an appropriate fit object") } cargs <- c("ymin", "ymax","influence", "ranks", "timewt", "keepstrata") cfun <- Call[c(1, match(cargs, names(Call), nomatch=0))] cfun[[1]] <- cord.work cfun$fname <- fname if (missing(newdata)) newdata <- NULL if (missing(cluster)) cluster <- NULL need.wt <- any(sapply(fits, function(x) !is.null(x$call$weights))) cfun$data <- lapply(fits, cord.getdata, newdata=newdata, cluster=cluster, need.wt=need.wt, timefix=timefix) rval <- eval(cfun, parent.frame()) rval$call <- Call rval } concordance.coxph <- function(object, ..., newdata, cluster, ymin, ymax, timewt=c("n", "S", "S/G", "n/G", "n/G2", "I"), influence=0, ranks=FALSE, timefix=FALSE, keepstrata=10) { Call <- match.call() fits <- list(object, ...) nfit <- length(fits) fname <- as.character(Call) # like deparse(substitute()) but works for ... fname <- fname[1 + 1:nfit] notok <- sapply(fits, function(x) !inherits(x, "coxph")) if (any(notok)) { # a common error is to mistype an arg, "ramk=TRUE" for instance, # and it ends up in the ... list # try for a nice message in this case: the name of the arg if it # has one other than "object", fname otherwise indx <- which(notok) id2 <- names(Call)[indx+1] temp <- ifelse(id2 %in% c("","object"), fname, id2) stop(temp, " argument is not an appropriate fit object") } # the cargs trick is a nice one, but it only copies over arguments that # are present. If 'ranks' was not specified, the default of FALSE is # not set. We keep it in the arg list only to match the documentation. cargs <- c("ymin", "ymax","influence", "ranks", "timewt", "keepstrata") cfun <- Call[c(1, match(cargs, names(Call), nomatch=0))] cfun[[1]] <- cord.work # a copy of the function cfun$fname <- fname cfun$reverse <- TRUE if (missing(newdata)) newdata <- NULL if (missing(cluster)) cluster <- NULL need.wt <- any(sapply(fits, function(x) !is.null(x$call$weights))) cfun$data <- lapply(fits, cord.getdata, newdata=newdata, cluster=cluster, need.wt=need.wt, timefix=timefix) rval <- eval(cfun, parent.frame()) rval$call <- Call rval } @ The next routine does all of the actual work for a set of models. Note that because of the call-through trick (fargs) exactly and only those arguments that are passed in are passed through to concordancefit. Default argument values for that function are found there. The default value for inflence found below is used in this routine, so it is important that they match. <>= cord.work <- function(data, timewt, ymin, ymax, influence=0, ranks=FALSE, reverse, fname, keepstrata) { Call <- match.call() fargs <- c("timewt", "ymin", "ymax", "influence", "ranks", "reverse", "keepstrata") fcall <- Call[c(1, match(fargs, names(Call), nomatch=0))] fcall[[1L]] <- concordancefit nfit <- length(data) if (nfit==1) { dd <- data[[1]] fcall$y <- dd$y fcall$x <- dd$x fcall$strata <- dd$strata fcall$weights <- dd$weights fcall$cluster <- dd$cluster rval <- eval(fcall, parent.frame()) } else { # Check that all of the models used the same data set, in the same # order, to the best of our abilities n <- length(data[[1]]$x) for (i in 2:nfit) { if (length(data[[i]]$x) != n) stop("all models must have the same sample size") if (!identical(data[[1]]$y, data[[i]]$y)) warning("models do not have the same response vector") if (!identical(data[[1]]$weights, data[[i]]$weights)) stop("all models must have the same weight vector") } if (influence==2) fcall$influence <-3 else fcall$influence <- 1 flist <- lapply(data, function(d) { temp <- fcall temp$y <- d$y temp$x <- d$x temp$strata <- d$strata temp$weights <- d$weights temp$cluster <- d$cluster eval(temp, parent.frame()) }) for (i in 2:nfit) { if (length(flist[[1]]$dfbeta) != length(flist[[i]]$dfbeta)) stop("models must have identical clustering") } count = do.call(rbind, lapply(flist, function(x) { if (is.matrix(x$count)) colSums(x$count) else x$count})) concordance <- sapply(flist, function(x) x$concordance) dfbeta <- sapply(flist, function(x) x$dfbeta) names(concordance) <- fname rownames(count) <- fname wt <- data[[1]]$weights if (is.null(wt)) vmat <- crossprod(dfbeta) else vmat <- t(wt * dfbeta) %*% dfbeta rval <- list(concordance=concordance, count=count, n=flist[[1]]$n, var=vmat, cvar= sapply(flist, function(x) x$cvar)) if (influence==1) rval$dfbeta <- dfbeta else if (influence ==2) { temp <- unlist(lapply(flist, function(x) x$influence)) rval$influence <- array(temp, dim=c(dim(flist[[1]]$influence), nfit)) } if (ranks) { temp <- lapply(flist, function(x) x$ranks) rdat <- data.frame(fit= rep(fname, sapply(temp, nrow)), do.call(rbind, temp)) row.names(rdat) <- NULL rval$ranks <- rdat } } class(rval) <- "concordance" rval } @ Last, a few miscellaneous methods <>= coef.concordance <- function(object, ...) object$concordance vcov.concordance <- function(object, ...) object$var @ The C routine returns an influence matrix with one row per subject $i$, and columns giving the partial with respect to $w_i$ for the number of concordant, discordant, tied on $x$ and ties on $y$ pairs. Somers' $d$ is $(C-D)/m$ where $m= C + D + T$ is the total number of %' comparable pairs, which does not count the tied-on-y column. For any given subject or cluster $k$ (for grouped jackknife) the IJ estimate of the variance is \begin{align*} V &\ \sum_k \left(\frac{\partial d}{\partial w_k}\right)^2 \\ \frac{\partial d}{\partial w_k} &= \frac{1}{m} \left[\frac{\partial{C-D}}{\partial w_k} - d \frac{\partial C+D+T}{\partial w_k} \right] \\ \end{align*} The C code looks a lot like a Cox model: walk forward through time, keep track of the risk sets, and add something to the totals at each death. What needs to be summed is the rank of the event subject's $x$ value, as compared to the value for all others at risk at this time point. For notational simplicity let $Y_j(t_i)$ be an indicator that subject $j$ is at risk at event time $t_i$, and $Y^*_j(t_i)$ the more restrictive one that subject $j$ is both at risk and not a tied event time. The values we want at time $t_i$ are \begin{align} C_i &= v_i \delta_i w_i \sum_j w_j Y^*_j(t_i) \left[I(x_i < x_j) \right] \label{C} \\ D_i &= v_i \delta_i w_i \sum_j w_j Y^*_j(t_i) \left[I(x_i > x_j)\right] \label{D} \\ T_i &= v_i \delta_i w_i \sum_j w_j Y^*_j(t_i) \left[I(x_i = x_j) \right] \label{T} \\ \end{align} In the above $v$ is an optional time weight, which we will discuss later. The normal concordance definition has $v=1$. $C$, $D$, and $T$ are the number of concordant, discordant, and tied pairs, respectively, and $m= C+D+T$ will be the total number of concordant pairs. Somers' $d$ is $(C-D)/m$ and the concordance is $(d+1)/2 = (C + T/2)/m$. The primary compuational question is how to do this efficiently, i.e., better than a naive algorithm that loops across all $n(n-1)/2$ possible pairs. There are two key ideas. \begin{enumerate} \item Rearrange the counting so that we do it by death times. For each death we count the number of other subjects in the risk set whose score is higher, lower, or tied and add it into the totals. This neatly solves the question of time-dependent covariates. \item Counting the number with higher, lower, and tied $x$ can be done in $O(\log_2 n)$ time if the $x$ data is kept in a binary tree. \end{enumerate} \begin{figure} \myfig{balance} \caption{A balanced tree of 13 nodes.} \label{treefig} \end{figure} Figure \ref{treefig} shows a balanced binary tree containing 13 risk scores. For each node the left child and all its descendants have a smaller value than the parent, the right child and all its descendents have a larger value. Each node in figure \ref{treefig} is also annotated with the total weight of observations in that node and the weight for itself plus all its children (not shown on graph). Assume that the tree shown represents all of the subjects still alive at the time a particular subject ``Smith'' expires, and that Smith has the risk score of 19 in the tree. The concordant pairs are those with a risk score $>19$, i.e., both $\hat y=x$ and $y$ are larger, discordant are $<19$, and we have no ties. The totals can be found by \begin{enumerate} \item Initialize the counts for discordant, concordant and tied to the values from the left children, right children, and ties at this node, respectively, which will be $(C,D,T) = (1,1,0)$. \item Walk up the tree, and at each step add the (parent + left child) or (parent + right child) to either D or C, depending on what part of the tree has not yet been totaled. At the next node (8) $D= D+4$, and at the top node $C=C + 6$. \end{enumerate} There are 5 concordant and 7 discordant pairs. This takes a little less than $\log_2(n)$ steps on average, as compared to an average of $n/2$ for the naive method. The difference can matter when $n$ is large since this traversal must be done for each event. The classic way to store trees is as a linked list. There are several algorithms for adding and subtracting nodes from a tree while maintaining the balance (red-black trees, AA trees, etc) but we take a different approach. Since we need to deal with case weights in the model and we know all the risk score at the outset, the full set of risk scores is organised into a tree at the beginning, updating the sums of weights at each node as observations are added or removed from the risk set. If we internally index the nodes of the tree as 1 for the top, 2--3 for the next horizontal row, 4--7 for the next, \ldots then the parent-child traversal becomes particularly easy. The parent of node $i$ is $i/2$ (integer arithmetic) and the children of node $i$ are $2i$ and $2i +1$. In C code the indices start at 0 of course. The following bit of code arranges data into such a tree. <>= btree <- function(n) { tfun <- function(n, id, power) { if (n==1L) id else if (n==2L) c(2L *id + 1L, id) else if (n==3L) c(2L*id + 1L, id, 2L*id +2L) else { nleft <- if (n== power*2L) power else min(power-1L, n-power%/%2L) c(tfun(nleft, 2L *id + 1L, power%/%2), id, tfun(n-(nleft+1L), 2L*id +2L, power%/%2)) } } tfun(as.integer(n), 0L, as.integer(2^(floor(logb(n-1,2))))) } @ Referring again to figure \ref{treefig}, \code{btree(13)} yields the vector \code{7 3 8 1 9 4 10 0 11 5 12 2 6} meaning that the smallest element will be in position 8 of the tree, the next smallest in position 4, etc, and using indexing that starts at 0 since the results will be passed to a C routine. The code just above takes care to do all arithmetic as integer. This actually made almost no difference in the compute time, but it was an interesting exercise to find that out. The next question is how to compute a variance for the result. One approach is to compute an infinitesimal jackknife (IJ) estimate, for which we need derivatives with respect to the weights. Looking back at equation \eqref{C} we have \begin{align} C &= \sum_i w_i \delta_i \sum_j Y^*_j(t_i) w_j I(x_i < x_j) \nonumber\\ % \frac{\partial C}{\partial w_k} &= % (v_k/m_k)\delta_k \sum_j Y^*_{j}(t_k) I(x_k < x_j) + % \sum_i (v_i/m_i) w_i Y^*_k(t_i) I(x_i < x_k) \label{partialC} \end{align} A given subject's weight appears multiple times, once when they are an event ($w_i \delta_i)$, and then as part of the risk set for other's events. I avoided this for some time because it looked like an $O(nd)$ process to separately update each subject's influence for each risk set they inhabit, but David Watson pointed out a path forward. The solution is to keep two trees. Tree 1 contains all of the subjects at risk. We traverse it when each subject is added in, updating the tree, and traverse it again at each death, pulling off values to update our sums. The second tree holds only the deaths and is updated at each death; it is read out twice per subject, once just after they enter the risk set and once when they leave. The basic algorithm is to move through an outer and inner loop. The outer loop moves across unique times, the inner for all obs that share a death time. We progress from largest to smallest time. Dealing with tied deaths is a bit subtle. \begin{itemize} \item All of the tied deaths need to be added to the event tree before subtracting the tree values from the ``initial'' influence matrix, since none of the tied subjects are in the comparison set for each other. \item Changes to the overall concordance/discordance counts need to be done for all the ties before adding them into the main tree, for the same reason. \item The Cox model variance just below has to be added up sequentially, one terms after each addition to the main tree. \end{itemize} Thus the inner loop must be repeated at least twice. A second variance computation treats the data as a Cox model. Create zero-centered scores for all subjects in the risk set: \begin{align} z_i(t) &= \sum_{j \in R(t)} w_j \sign(x_i - x_j) \nonumber \\ D-C &= \sum_i \delta_i z_i(t_i) \label{zcord} \end{align} At any event time $\sum w_i z_i =0$. Equation \eqref{zcord} is the score equation for a Cox model with time-dependent covariate $z$. When two subjects have an event at the same time, this formulation treats each of them as being in the other's risk set whereas the concordance treats them as incomparable --- how can they be the same? The trick is that $D-C$ does not change: the tied pairs add equally to $D$ and $C$. Under the null hypothesis that the risk score is not related to outcome, each term in \eqref{zcord} is a random selection from the $z$ scores in the risk set, and the variance of the addition is the variance of $z$, the sum of these over deaths is the Cox model information matrix, which is also the variance of the score statistic. The mean of $z$ is always zero, so we need to keep track of $\sum w_i z^2$. How can we do this efficiently? First note that $z_i$ can be written as sum(weights for smaller x) - sum(weights for larger x), and in fact the weighted mean for any slice of $x$, $a < x < b$, is exactly the same: mean = sum(weights for x values below the range) - sum(weights above the range). The second trick is to use an ANOVA decomposition of the variance of $z$ into within-slice and between-slice sums of squares, where the 3 slices are the $z$ scores at a given $x$ value (node of the tree), weights for score below that cutpoint, and above. Assume that a new observation $k$ has just been added to the tree. This will add $w_k$ to all the $z$ values above, and to the weighted mean of all those above, $-w_k$ to the values and means below, and 0 to the values and means of any tied observations. Thus none of the current `within' SS change. Let $s_a$, $s_b$ and $s_0$ be the current sum of weights above, below, and at the node of the tree. The mean for the above group was $(s_b + s_0)$ with between SS contribution of $s_a (s_b + s_0)^2$. The below mean was $-(s_a + s_0)$ with between SS contribution of $s_b(s_a + s_0)^2$. The change to the between SS from adding the new subject is $$ s_a\left( (s_b+s_0 + w_k)^2 - (s_b + s_0)^2 \right) = s_a (2w_k (s_b + s_0) + w_k^2) $$ while the change in between SS for the below group is $s_b(2w_k(s_a + s_0) + w_k^2)$, and there is no change for the prior observations in the middle group. Last we add $w_kz_k^2 = w_k(s_b- s_a)^2$ to the sum for the new observation. Putting all this together the change is $$ w_k \left(s_a (w_k + (s_b + s_c)) + s_b(w_k + (s_a + s_c)) + (s_a-s_b)^2 \right) $$ We can now define the C-routine that does the bulk of the work. First we give the outline shell of the code and then discuss the parts one by one. This routine is for ordinary survival data, and will be called once per stratum. Input variables are \begin{description} \item[n] the number of observations \item[y] matrix containing the time and status, data is sorted by descending time, with censorings precedint deaths. \item[x] the tree node at which this observation's risk score resides %' \item[wt] case weight for the observation \end{description} The routine will return list with three components: \begin{itemize} \item count, a vector containing the weighted number of concordant, discordant, tied on $x$ but not $y$, and tied on y pairs. The weight for a pair is $w_iw_j$. \item resid, a three column matrix with one row per event, containing the score residual at that event, its variance, and the sum of weights. The score residual is a rescaled $z_i$ so as to lie between 0 and 1: $(1+ z/\sum(w))/2$. The concordance is then a weighted sum of the residuals. \item influence, a matrix with one row per observation and 4 columns, giving that observation's first derivative with respect to the count vector. \end{itemize} <>= #include "survS.h" #include "survproto.h" <> SEXP concordance3(SEXP y, SEXP x2, SEXP wt2, SEXP timewt2, SEXP sortstop, SEXP doresid2) { int i, j, k, ii, jj, kk, j2; int n, ntree, nevent; double *time, *status; int xsave; /* sum of weights for a node (nwt), sum of weights for the node and ** all of its children (twt), then the same again for the subset of ** deaths */ double *nwt, *twt, *dnwt, *dtwt; double z2; /* sum of z^2 values */ int ndeath; /* total number of deaths at this point */ int utime; /* number of unique event times seen so far */ double dwt, dwt2; /* sum of weights for deaths and deaths tied on x */ double wsum[3]; /* the sum of weights that are > current, <, or equal */ double temp, adjtimewt; /* the second accounts for npair and timewt*/ SEXP rlist, count2, imat2, resid2; double *count, *imat[5], *resid[4]; double *wt, *timewt; int *x, *sort2; int doresid; static const char *outnames1[]={"count", "influence", "resid", ""}, *outnames2[]={"count", "influence", ""}; n = nrows(y); doresid = asLogical(doresid2); x = INTEGER(x2); wt = REAL(wt2); timewt = REAL(timewt2); sort2 = INTEGER(sortstop); time = REAL(y); status = time + n; /* if there are tied predictors, the total size of the tree will be < n */ ntree =0; nevent =0; for (i=0; i= ntree) ntree = x[i] +1; nevent += status[i]; } nwt = (double *) R_alloc(4*ntree, sizeof(double)); twt = nwt + ntree; dnwt = twt + ntree; dtwt = dnwt + ntree; for (i=0; i< 4*ntree; i++) nwt[i] =0.0; if (doresid) PROTECT(rlist = mkNamed(VECSXP, outnames1)); else PROTECT(rlist = mkNamed(VECSXP, outnames2)); count2 = SET_VECTOR_ELT(rlist, 0, allocVector(REALSXP, 6)); count = REAL(count2); for (i=0; i<6; i++) count[i]=0.0; imat2 = SET_VECTOR_ELT(rlist, 1, allocMatrix(REALSXP, n, 5)); for (i=0; i<5; i++) { imat[i] = REAL(imat2) + i*n; for (j=0; j> UNPROTECT(1); return(rlist); } @ The key part of our computation is to update the vectors of weights. We don't actually pass the risk score values $r$ into the routine, %' it is enough for each observation to point to the appropriate tree node. The tree contains the weights for everyone whose survival is larger than the time currently under review, so starts with all weights equal to zero. For any pair of observations $i,j$ we need to add $w_iw_j$ to the appropriate count, $w_j$ to subject $i$'s row of the leverage matrix and $w_i$ to subject $j$'s row. We use two trees to do this efficiently, one with all the observations to date, one with the events to date. Starting at the largest time (which is sorted last), walk through the tree. \begin{itemize} \item If the current observation is a censoring time, in order: \begin{itemize} \item Subtract event tree information from the influence matrix \item Update the Cox variance \item Add them into the main tree \end{itemize} \item If the current observation is a death, care for all deaths tied at this time point. Each pass covers all the deaths. \begin{itemize} \item Pass 1: In any order \begin{itemize} \item Add up the total number of deaths \item Update the tied.y count and tied.xy count \\ tied.xy subtotals reset each time x changes \item Count concordant, discordant, tied.x counts, both total and for the observation's influence \item Add the subject to the event tree \item Compute the first 3 columns of the residuals. \end{itemize} \item Finish up the tied.xy influence, for the last unique x in this set. \item Pass 2: \begin{itemize} \item Subtract the event tree information from the influence matrix \item Add the tied.y part of the influence for each obs \item Increment the Cox variance \item Add the subject into the main tree \end{itemize} \end{itemize} \item When all the subjects have been added to the tree, then add the final death tree's data for to the influence matrix. \end{itemize} For concordant, discordant, and tied.x there are three readouts: the total tree before any additions, the death tree after the addition of the tied events, and the death tree at the very end. Increments to the Cox variance occur just before each addition to the total tree, and are saved out after each batch of events. The above discussion counts up all pairs that are not tied on the response $y$. Though not used in the concordance the routine counts up tied.y pairs as well, with a separate count for those that are tied on both $x$ and $y$. The algorithm for this part is simpler since the data is sorted by $y$. Say that there were 5 obs tied at some time point with weights of $w_1$ to $w_5$. The total count for ties involves all 5-choose-2 pairs and can be written as $$ w_1 w_2 + (w_1 + w_2)w_3 + (w_1 + w_2 + w_3)w_4 + (w_1 + w_2 + w_3 + w_4)w_5 $$ which immediately suggests a simple summation algorithm as we go through the loop. In the below \code{dwt} contains the running sum 0, $w_1$, $w_1 + w_2$, etc and we add \code{w[i]*dwt} to the total just before incrementing the sum. The influence for observation 1 is $w_2 + w_3 + w_4 + w_5$, which can be done at the end as \code{dwt - wt[i]}. The temporary accumulator \code{dwt} is reset to 0 with each new $y$ value. To compute ties on both $x$ and $y$ the data set is sorted by $x$ within $y$, and we use the same algorithm, but reset \code{dwt2} to zero whenever either $x$ or $y$ changes. <>= z2 =0; utime=0; for (i=0; i>= void walkup(double *nwt, double* twt, int index, double sums[3], int ntree) { int i, j, parent; for (i=0; i<3; i++) sums[i] = 0.0; sums[2] = nwt[index]; /* tied on x */ j = 2*index +2; /* right child */ if (j < ntree) sums[0] += twt[j]; if (j <=ntree) sums[1]+= twt[j-1]; /*left child */ while(index > 0) { /* for as long as I have a parent... */ parent = (index-1)/2; if (index%2 == 1) sums[0] += twt[parent] - twt[index]; /* left child */ else sums[1] += twt[parent] - twt[index]; /* I am a right child */ index = parent; } } void addin(double *nwt, double *twt, int index, double wt) { nwt[index] += wt; while (index >0) { twt[index] += wt; index = (index-1)/2; } twt[0] += wt; } @ The code for [start, stop) data is almost identical, the primary call simply has one more index. As in the agreg routines there are two sort indices, the first indexes the data by stop time, longest to earliest, and the second by start time. The [[y]] variable now has three columns. <>= SEXP concordance4(SEXP y, SEXP x2, SEXP wt2, SEXP timewt2, SEXP sortstart, SEXP sortstop, SEXP doresid2) { int i, j, k, ii, jj, kk, i2, j2; int n, ntree, nevent; double *time1, *time2, *status; int xsave; /* sum of weights for a node (nwt), sum of weights for the node and ** all of its children (twt), then the same again for the subset of ** deaths */ double *nwt, *twt, *dnwt, *dtwt; double z2; /* sum of z^2 values */ int ndeath; /* total number of deaths at this point */ int utime; /* number of unique event times seen so far */ double dwt; /* weighted number of deaths at this point */ double dwt2; /* tied on both x and y */ double wsum[3]; /* the sum of weights that are > current, <, or equal */ double temp, adjtimewt; /* the second accounts for npair and timewt*/ SEXP rlist, count2, imat2, resid2; double *count, *imat[5], *resid[4]; double *wt, *timewt; int *x, *sort2, *sort1; int doresid; static const char *outnames1[]={"count", "influence", "resid", ""}, *outnames2[]={"count", "influence", ""}; n = nrows(y); doresid = asLogical(doresid2); x = INTEGER(x2); wt = REAL(wt2); timewt = REAL(timewt2); sort2 = INTEGER(sortstop); sort1 = INTEGER(sortstart); time1 = REAL(y); time2 = time1 + n; status = time2 + n; /* if there are tied predictors, the total size of the tree will be < n */ ntree =0; nevent =0; for (i=0; i= ntree) ntree = x[i] +1; nevent += status[i]; } /* ** nwt and twt are the node weight and total =node + all children for the ** tree holding all subjects. dnwt and dtwt are the same for the tree ** holding all the events */ nwt = (double *) R_alloc(4*ntree, sizeof(double)); twt = nwt + ntree; dnwt = twt + ntree; dtwt = dnwt + ntree; for (i=0; i< 4*ntree; i++) nwt[i] =0.0; if (doresid) PROTECT(rlist = mkNamed(VECSXP, outnames1)); else PROTECT(rlist = mkNamed(VECSXP, outnames2)); count2 = SET_VECTOR_ELT(rlist, 0, allocVector(REALSXP, 6)); count = REAL(count2); for (i=0; i<6; i++) count[i]=0.0; imat2 = SET_VECTOR_ELT(rlist, 1, allocMatrix(REALSXP, n, 5)); for (i=0; i<5; i++) { imat[i] = REAL(imat2) + i*n; for (j=0; j> UNPROTECT(1); return(rlist); } @ As we move from the longest time to the shortest observations are added into the tree of weights whenever we encounter their stop time. This is just as before. Weights now also need to be removed from the tree whenever we encounter an observation's start time. %' It is convenient ``catch up'' on this second task whenever we encounter a death. <>= z2 =0; utime=0; i2 =0; /* i2 tracks the start times */ for (i=0; i= time2[ii]); i2++) { jj = sort1[i2]; /* influence */ walkup(dnwt, dtwt, x[jj], wsum, ntree); imat[0][jj] += wsum[1]; imat[1][jj] += wsum[0]; imat[2][jj] += wsum[2]; addin(nwt, twt, x[jj], -wt[jj]); /*remove from main tree */ /* Cox variance */ walkup(nwt, twt, x[jj], wsum, ntree); z2 -= wt[jj]*(wsum[0]*(wt[jj] + 2*(wsum[1] + wsum[2])) + wsum[1]*(wt[jj] + 2*(wsum[0] + wsum[2])) + (wsum[0]-wsum[1])*(wsum[0]-wsum[1])); } ndeath=0; dwt=0; dwt2 =0; xsave=x[ii]; j2= i; adjtimewt = timewt[utime++]; /* pass 1 */ for (j=i; j>= tfun <- function(start, gap) { as.numeric(start)/365.25 - as.numeric(start + gap)/365.25 } test <- logical(200) for (i in 1:200) { test[i] <- tfun(as.Date("2010/01/01"), 29) == tfun(as.Date("2010/01/01") + i, 29) } table(test) @ The number of FALSE entries in the table depends on machine, compiler, and a host of other issues. There is discussion of this general issue in the R FAQ: ``why doesn't R think these numbers are equal''. The Kaplan-Meier and Cox model both pay careful attention to ties, and so both now use the \code{aeqSurv} routine to first preprocess the time data. It uses the same rules as \code{all.equal} to adjudicate ties and near ties. <>= survfit <- function(formula, ...) { UseMethod("survfit") } <> <> <> @ The result of a survival curve will have a \code{surv} or \code{pstate} component that is a vector or a matrix, and an optional strata component. From a user's point of view this is an object with [strata, newdata, state] as dimensions, where only 1, 2 or all three of these may appear. The first is always present, and is essentially the number of distinct curves created by the right-hand side of the equation (or by the strata in a coxph model). The newdata portion appears for survival curves from a Cox model, when curves for multiple covariate patterns were requested; the state portion only from a multi-state model; or both for a multi-state Cox model. The \code{surv} component contains the time points for the first stratum, the second, third, etc stacked one above the other. As with R matrices, if only 1 subscript is given for an array or matrix of curves, we treat the collection of curves as a vector of curves. We need to make sure that the new object has the same order of elements as the old -- users count on this. <>= dim.survfit <- function(x) { d1name <- "strata" d2name <- "data" d3name <- "states" if (is.null(x$strata)) {d1 <- d1name <- NULL} else d1 <- length(x$strata) if (is.null(x$newdata)) {d2 <- d2name <- NULL} else d2 <- nrow(x$newdata) if (is.null(x$states)) {d3 <- d3name <- NULL} else d3 <- length(x$states) if (inherits(x, "survfitcox") && is.null(d2) && is.null(d3) && is.matrix(x$surv)) { # older style survfit.coxph object, before I added newdata to the output d2name <- "data" d2 <- ncol(x$surv) } dd <- c(d1, d2, d3) names(dd) <- c(d1name, d2name, d3name) dd } # there is a separate function for survfitms objects "[.survfit" <- function(x, ... , drop=TRUE) { nmatch <- function(indx, target) { # This function lets R worry about character, negative, or # logical subscripts. # It always returns a set of positive integer indices temp <- 1:length(target) names(temp) <- target temp[indx] } if (!inherits(x, "survfit")) stop("[.survfit called on non-survfit object") ndots <- ...length() # the simplest, but not avail in R 3.4 # ndots <- length(list(...))# fails if any are missing, e.g. fit[,2] # ndots <- if (missing(drop)) nargs()-1 else nargs()-2 # a workaround dd <- dim(x) # for dd=NULL, an object with only one curve, x[1] is always legal if (is.null(dd)) dd <- c(strata=1L) # survfit object with only one curve dtype <- match(names(dd), c("strata", "data", "states")) if (ndots >0 && !missing(..1)) i <- ..1 else i <- NULL if (ndots> 1 && !missing(..2)) j <- ..2 else j <- NULL if (ndots > length(dd)) stop("incorrect number of dimensions") if (length(dtype) > 2) stop("invalid survfit object") # should never happen if (is.null(i) && is.null(j)) { # called with no subscripts given -- return x untouched return(x) } # Code below is easier if "i" is always the strata if (dtype[1] !=1) { dtype <- c(1, dtype) j <- i; i <- NULL dd <- c(1, dd) ndots <- ndots +1 } # We need to make a new one newx <- vector("list", length(x)) names(newx) <- names(x) for (k in c("logse", "version", "conf.int", "conf.type", "type", "call")) if (!is.null(x[[k]])) newx[[k]] <- x[[k]] class(newx) <- class(x) if (ndots== 1 && length(dd)==2) { # one subscript given for a two dimensional object # If one of the dimensions is 1, it is easier for me to fill in i and j if (dd[1]==1) {j <- i; i<- 1} else if (dd[2]==1) j <- 1 else { # the user has a mix of rows/cols index <- 1:prod(dd) itemp <- matrix(index, nrow=dd[1]) keep <- itemp[i] # illegal subscripts will generate an error if (length(keep) == length(index) && all(keep==index)) return(x) ii <- row(itemp)[keep] jj <- col(itemp)[keep] # at this point we have a matrix subscript of (ii, jj) # expand into a long pair of rows and cols temp <- split(seq(along.with=x$time), rep(1:length(x$strata), x$strata)) indx1 <- unlist(temp[ii]) # rows of the surv object indx2 <- rep(jj, x$strata[ii]) # return with each curve as a separate strata newx$n <- x$n[ii] for (k in c("time", "n.risk", "n.event", "n.censor", "n.enter")) if (!is.null(x[[k]])) newx[[k]] <- (x[[k]])[indx1] k <- cbind(indx1, indx2) for (j in c("surv", "std.err", "upper", "lower", "cumhaz", "std.chaz", "influence.surv", "influence.chaz")) if (!is.null(x[[j]])) newx[[j]] <- (x[[j]])[k] temp <- x$strata[ii] names(temp) <- 1:length(ii) newx$strata <- temp return(newx) } } # irow will be the rows that need to be taken # j the columns (of present) if (is.null(x$strata)) { if (is.null(i) || all(i==1)) irow <- seq(along.with=x$time) else stop("subscript out of bounds") newx$n <- x$n } else { if (is.null(i)) indx <- seq(along.with= x$strata) else indx <- nmatch(i, names(x$strata)) #strata to keep if (any(is.na(indx))) stop(paste("strata", paste(i[is.na(indx)], collapse=' '), 'not matched')) # Now, indx may not be in order: some can use curve[3:2] to reorder # The list/unlist construct will reorder the data temp <- split(seq(along.with =x$time), rep(1:length(x$strata), x$strata)) irow <- unlist(temp[indx]) if (length(indx) <=1 && drop) newx$strata <- NULL else newx$strata <- x$strata[i] newx$n <- x$n[indx] if (length(indx) ==1 & drop) x$strata <- NULL else newx$strata <- x$strata[indx] } if (length(dd)==1) { # no j dimension for (k in c("time", "n.risk", "n.event", "n.censor", "n.enter", "surv", "std.err", "cumhaz", "std.chaz", "upper", "lower", "influence.surv", "influence.chaz")) if (!is.null(x[[k]])) newx[[k]] <- (x[[k]])[irow] } else { # 2 dimensional object if (is.null(j)) j <- seq.int(ncol(x$surv)) # If the curve has been selected by strata and keep has only # one row, we don't want to lose the second subscript too if (length(irow)==1) drop <- FALSE for (k in c("time", "n.risk", "n.event", "n.censor", "n.enter")) if (!is.null(x[[k]])) newx[[k]] <- (x[[k]])[irow] for (k in c("surv", "std.err", "cumhaz", "std.chaz", "upper", "lower", "influence.surv", "influence.chaz")) if (!is.null(x[[k]])) newx[[k]] <- (x[[k]])[irow, j, drop=drop] } newx } @ \subsection{Kaplan-Meier} The most common use of the survfit function is with a formula as the first argument, and the most common outcome of such a call is a Kaplan-Meier curve. The id argument is from an older version of the competing risks code; most people will use [[cluster(id)]] in the formula instead. The istate argument only applies to competing risks, but don't print an error message if it is accidentally there. <>= survfit.formula <- function(formula, data, weights, subset, na.action, stype=1, ctype=1, id, cluster, robust, istate, timefix=TRUE, etype, error, ...) { Call <- match.call() Call[[1]] <- as.name('survfit') #make nicer printout for the user <> # Deal with the near-ties problem if (!is.logical(timefix) || length(timefix) > 1) stop("invalid value for timefix option") if (timefix) newY <- aeqSurv(Y) else newY <- Y if (missing(robust)) robust <- NULL # Call the appropriate helper function if (attr(Y, 'type') == 'left' || attr(Y, 'type') == 'interval') temp <- survfitTurnbull(X, newY, casewt, ...) else if (attr(Y, 'type') == "right" || attr(Y, 'type')== "counting") temp <- survfitKM(X, newY, casewt, stype=stype, ctype=ctype, id=id, cluster=cluster, robust=robust, ...) else if (attr(Y, 'type') == "mright" || attr(Y, "type")== "mcounting") temp <- survfitCI(X, newY, weights=casewt, stype=stype, ctype=ctype, id=id, cluster=cluster, robust=robust, istate=istate, ...) else { # This should never happen stop("unrecognized survival type") } # If a stratum had no one beyond start.time, the length 0 gives downstream # failure, e.g., there is no sensible printout for summary(fit, time= 100) # for such a curve temp$strata <- temp$strata[temp$strata >0] if (is.null(temp$states)) class(temp) <- 'survfit' else class(temp) <- c("survfitms", "survfit") if (!is.null(attr(mf, 'na.action'))) temp$na.action <- attr(mf, 'na.action') temp$call <- Call temp } @ This chunk of code is shared with resid.survfit <>= # create a copy of the call that has only the arguments we want, # and use it to call model.frame() indx <- match(c('formula', 'data', 'weights', 'subset','na.action', 'istate', 'id', 'cluster', "etype"), names(Call), nomatch=0) #It's very hard to get the next error message other than malice # eg survfit(wt=Surv(time, status) ~1) if (indx[1]==0) stop("a formula argument is required") temp <- Call[c(1, indx)] temp[[1L]] <- quote(stats::model.frame) mf <- eval.parent(temp) Terms <- terms(formula, c("strata", "cluster")) ord <- attr(Terms, 'order') if (length(ord) & any(ord !=1)) stop("Interaction terms are not valid for this function") n <- nrow(mf) Y <- model.response(mf) if (inherits(Y, "Surv2")) { # this is Surv2 style data # if there are any obs removed due to missing, remake the model frame if (length(attr(mf, "na.action"))) { temp$na.action <- na.pass mf <- eval.parent(temp) } if (!is.null(attr(Terms, "specials")$cluster)) stop("cluster() cannot appear in the model statement") new <- surv2data(mf) mf <- new$mf istate <- new$istate id <- new$id Y <- new$y if (anyNA(mf[-1])) { #ignore the response variable still found there if (missing(na.action)) temp <- get(getOption("na.action"))(mf[-1]) else temp <- na.action(mf[-1]) omit <- attr(temp, "na.action") mf <- mf[-omit,] Y <- Y[-omit] id <- id[-omit] istate <- istate[-omit] } n <- nrow(mf) } else { if (!is.Surv(Y)) stop("Response must be a survival object") id <- model.extract(mf, "id") istate <- model.extract(mf, "istate") } if (n==0) stop("data set has no non-missing observations") casewt <- model.extract(mf, "weights") if (is.null(casewt)) casewt <- rep(1.0, n) else { if (!is.numeric(casewt)) stop("weights must be numeric") if (any(!is.finite(casewt))) stop("weights must be finite") if (any(casewt <0)) stop("weights must be non-negative") casewt <- as.numeric(casewt) # transform integer to numeric } if (!is.null(attr(Terms, 'offset'))) warning("Offset term ignored") cluster <- model.extract(mf, "cluster") temp <- untangle.specials(Terms, "cluster") if (length(temp$vars)>0) { if (length(cluster) >0) stop("cluster appears as both an argument and a model term") if (length(temp$vars) > 1) stop("can not have two cluster terms") cluster <- mf[[temp$vars]] Terms <- Terms[-temp$terms] } ll <- attr(Terms, 'term.labels') if (length(ll) == 0) X <- factor(rep(1,n)) # ~1 on the right else X <- strata(mf[ll]) # Backwards support for the now-depreciated etype argument etype <- model.extract(mf, "etype") if (!is.null(etype)) { if (attr(Y, "type") == "mcounting" || attr(Y, "type") == "mright") stop("cannot use both the etype argument and mstate survival type") if (length(istate)) stop("cannot use both the etype and istate arguments") status <- Y[,ncol(Y)] etype <- as.factor(etype) temp <- table(etype, status==0) if (all(rowSums(temp==0) ==1)) { # The user had a unique level of etype for the censors newlev <- levels(etype)[order(-temp[,2])] #censors first } else newlev <- c(" ", levels(etype)[temp[,1] >0]) status <- factor(ifelse(status==0,0, as.numeric(etype)), labels=newlev) if (attr(Y, 'type') == "right") Y <- Surv(Y[,1], status, type="mstate") else if (attr(Y, "type") == "counting") Y <- Surv(Y[,1], Y[,2], status, type="mstate") else stop("etype argument incompatable with survival type") } @ Once upon a time I allowed survfit to be called without the `\textasciitilde 1' portion of the formula. This was a mistake for multiple reasons, but the biggest problem is timing. If the subject has a data statement but the first argument is not a formula, R needs to evaluate Surv(t,s) to know that it is a survival object, but it also needs to know that this is a survival object before evaluation in order to dispatch the correct method. The method below helps give a useful error message in some cases. <>= survfit.Surv <- function(formula, ...) stop("the survfit function requires a formula as its first argument") @ The last peice in this file is the function to create confidence intervals. It is called from multiple different places so it is well to have one copy. If $p$ is the survival probability and $s(p)$ its standard error, we can do confidence intervals on the simple scale of $ p \pm 1.96 s(p)$, but that does not have very good properties. Instead use a transformation $y = f(p)$ for which the standard error is $s(p) f'(p)$, leading to the confidence interval \begin{equation*} f^{-1}\left(f(p) +- 1.96 s(p)f'(p) \right) \end{equation*} Here are the supported transformations. \begin{center} \begin{tabular}{rccc} &$f$& $f'$ & $f^{-1}$ \\ \hline log & $\log(p)$ & $1/p$ & $ \exp(y)$ \\ log-log & $\log(-\log(p))$ & $1/\left[ p \log(p) \right]$ & $\exp(-\exp(y)) $ \\ logit & $\log(p/1-p)$ & $1/[p (1-p)]$ & $1- 1/\left[1+ \exp(y)\right]$ \\ arcsin & $\arcsin(\sqrt{p})$ & $1/(2 \sqrt{p(1-p)})$ &$\sin^2(y)$ \\ \end{tabular} \end{center} Plain intervals can give limits outside of (0,1), we truncate them when this happens. The log intervals can give an upper limit greater than 1, but the lower limit is always valid, and the log-log and logit. The arcsin require truncation in the middle of the formula. In all cases we return NA as the CI for survival=0: it makes the graphs look better. Some of the underlying routines compute the standard error of $p$ and some the standard error of $\log(p)$. The \code{selow} argument is used for the modified lower limits of Dory and Korn. When this is used for cumulative hazards the ulimit arg will be FALSE: no upper limit of 1. <>= survfit_confint <- function(p, se, logse=TRUE, conf.type, conf.int, selow, ulimit=TRUE) { zval <- qnorm(1- (1-conf.int)/2, 0,1) if (missing(selow)) scale <- 1.0 else scale <- ifelse(selow==0, 1.0, selow/se) # avoid 0/0 at the origin if (!logse) se <- ifelse(se==0, 0, se/p) # se of log(survival) = log(p) if (conf.type=='plain') { se2 <- se* p * zval # matches equation 4.3.1 in Klein & Moeschberger if (ulimit) list(lower= pmax(p -se2*scale, 0), upper = pmin(p + se2, 1)) else list(lower= pmax(p -se2*scale, 0), upper = p + se2) } else if (conf.type=='log') { #avoid some "log(0)" messages xx <- ifelse(p==0, NA, p) se2 <- zval* se temp1 <- exp(log(xx) - se2*scale) temp2 <- exp(log(xx) + se2) if (ulimit) list(lower= temp1, upper= pmin(temp2, 1)) else list(lower= temp1, upper= temp2) } else if (conf.type=='log-log') { xx <- ifelse(p==0 | p==1, NA, p) se2 <- zval * se/log(xx) temp1 <- exp(-exp(log(-log(xx)) - se2*scale)) temp2 <- exp(-exp(log(-log(xx)) + se2)) list(lower = temp1 , upper = temp2) } else if (conf.type=='logit') { xx <- ifelse(p==0, NA, p) # avoid log(0) messages se2 <- zval * se *(1 + xx/(1-xx)) temp1 <- 1- 1/(1+exp(log(p/(1-p)) - se2*scale)) temp2 <- 1- 1/(1+exp(log(p/(1-p)) + se2)) list(lower = temp1, upper=temp2) } else if (conf.type=="arcsin") { xx <- ifelse(p==0, NA, p) se2 <- .5 *zval*se * sqrt(xx/(1-xx)) list(lower= (sin(pmax(0, asin(sqrt(xx)) - se2*scale)))^2, upper= (sin(pmin(pi/2, asin(sqrt(xx)) + se2)))^2) } else stop("invalid conf.int type") } @ survival/noweb/zph.Rnw0000644000176200001440000003670214060757746014560 0ustar liggesusers\section{The cox.zph function} The simplest test of proportional hazards is to use a time dependent coefficient $\beta(t) = a + bt$. Then $\beta(t) x = ax + b*(tx)$, and the extended coefficients $a$ and $b$ can be obtained from a Cox model with an extra 'fake' covariate $tx$. More generally, replace $t$ with some function $g(t)$, which gives rise to an entire family of tests. An efficient assessment of this extended model can be done using a score test. \begin{itemize} \item Augment the original variables $x_1, \ldots x_k$ with $k$ new ones $g(t)x_1, \ldots, g(t)x_k$ \item Compute the first and second derivatives $U$ and $H$ of the Cox model at the starting estimate of $(\hat\beta, 0)$; prior covariates at their prior values, and the new covariates at 0. No iteration is done. This can be done efficiently with a modified version of the primary C routines for coxph. \item By design, the first $k$ elements of $U$ will be zero. Thus the first iteration of the new coefficients, and the score tests for them, are particularly easy. \end{itemize} The information or Hessian matrix for a Cox model is $$ \sum_{j \in deaths} V(t_j) = \sum_jV_j$$ where $V_j$ is the variance matrix of the weighted covariate values, over all subjects at risk at time $t_j$. Then the expanded information matrix for the score test is \begin{align*} H &= \left(\begin{array}{cc} H_1 & H_2 \\ H_2' & H_3 \end{array} \right) \\ H_1 &= \sum V(t_j) \\ H_2 &= \sum V(t_j) g(t_j) \\ H_3 &= \sum V(t_j) g^2(t_j) \end{align*} The inverse of the matrix will be more numerically stable if $g(t)$ is centered at zero, and this does not change the test statistic. In the usual case $V(t)$ is close to constant in time --- the variance of $X$ does not change rapidly --- and then $H_2$ is approximately zero. The original cox.zph used an approximation, which is to assume that $V(t)$ is exactly constant. In that case $H_2=0$ and $H_3= \sum V(t_j) \sum g^2(t_j)$ and the test is particularly easy to compute. This assumption of identical components can fail badly for models with a covariate by strata interaction, and for some models with covariate dependent censoring. Multi-state models finally forced a change. The newer version of the routine has two separate tracks: for the formal test and another for the residuals. <>= cox.zph <- function(fit, transform='km', terms=TRUE, singledf =FALSE, global=TRUE) { Call <- match.call() if (!inherits(fit, "coxph") && !inherits(fit, "coxme")) stop ("argument must be the result of Cox model fit") if (inherits(fit, "coxph.null")) stop("there are no score residuals for a Null model") if (!is.null(attr(terms(fit), "specials")[["tt"]])) stop("function not defined for models with tt() terms") if (inherits(fit, "coxme")) { # drop all mention of the random effects, before getdata fit$formula <- fit$formula$fixed fit$call$formula <- fit$formula } cget <- coxph.getdata(fit, y=TRUE, x=TRUE, stratax=TRUE, weights=TRUE) y <- cget$y ny <- ncol(y) event <- (y[,ny] ==1) if (length(cget$strata)) istrat <- as.integer(cget$strata) - 1L # number from 0 for C else istrat <- rep(0L, nrow(y)) # if terms==FALSE the singledf argument is moot, but setting a value # leads to a simpler path through the code if (!terms) singledf <- FALSE <> <> <> <> rval$transform <- tname rval$call <- Call class(rval) <- "cox.zph" return(rval) } print.cox.zph <- function(x, digits = max(options()$digits - 4, 3), signif.stars=FALSE, ...) { invisible(printCoefmat(x$table, digits=digits, signif.stars=signif.stars, P.values=TRUE, has.Pvalue=TRUE, ...)) } @ The user can use $t$ or $g(t)$ as the multiplier of the covariates. The default is to use the KM, only because that seems to be best at avoiding edge cases. <>= times <- y[,ny-1] if (is.character(transform)) { tname <- transform ttimes <- switch(transform, 'identity'= times, 'rank' = rank(times), 'log' = log(times), 'km' = { temp <- survfitKM(factor(rep(1L, nrow(y))), y, se.fit=FALSE) # A nuisance to do left continuous KM indx <- findInterval(times, temp$time, left.open=TRUE) 1.0 - c(1, temp$surv)[indx+1] }, stop("Unrecognized transform")) } else { tname <- deparse(substitute(transform)) if (length(tname) >1) tname <- 'user' ttimes <- transform(times) } gtime <- ttimes - mean(ttimes[event]) # Now get the U, information, and residuals if (ny==2) { ord <- order(istrat, y[,1]) -1L resid <- .Call(Czph1, gtime, y, X, eta, cget$weights, istrat, fit$method=="efron", ord) } else { ord1 <- order(-istrat, -y[,1]) -1L # reverse time for zph2 ord <- order(-istrat, -y[,2]) -1L resid <- .Call(Czph2, gtime, y, X, eta, cget$weights, istrat, fit$method=="efron", ord1, ord) } @ The result has a score vector of length $2p$ where $p$ is the number of variables and an information matrix that is $2p$ by $2p$. This is done with C code that is a simple variation on iteration 1 for a coxph model. If \code{singledf} is TRUE then treat each term as a single degree of freedom test, otherwise as a multi-degree of freedom. If terms=FALSE test each covariate individually. If all the variables are univariate this is a moot point. The survival routines return Splus style assign components, that is a list with one element per term, each element an integer vector of coefficient indices. The asgn vector is our main workhorse: loop over asgn to process term by term. \begin{itemize} \item if term=FALSE, set make a new asgn with one coef per term \item if a coefficient is NA, remove it from the relevant asgn vector \item frailties and penalized coxme coefficients are ignored: remove their element from the asgn list \end{itemize} For random effects models, including both frailty and coxme results, the random effect is included in the linear.predictors component of the fit. This allows us to do score tests for the other terms while effectively holding the random effect fixed. If there are any NA coefficients these are redundant variables. It's easiest to simply get rid of them at the start by fixing up X, varnames, asgn, and nvar. <>= eta <- fit$linear.predictors X <- cget$x varnames <- names(fit$coefficients) nvar <- length(varnames) if (!terms) { # create a fake asgn that has one value per coefficient asgn <- as.list(1:nvar) names(asgn) <- names(fit$coefficients) } else if (inherits(fit, "coxme")) { asgn <- attrassign(cget$x, terms(fit)) # allow for a spelling inconsistency in coxme, later fixed if (is.null(fit$linear.predictors)) eta <- fit$linear.predictor fit$df <- NULL # don't confuse later code } else asgn <- fit$assign if (!is.list(asgn)) stop ("unexpected assign component") frail <- grepl("frailty(", names(asgn), fixed=TRUE) if (any(frail)) { dcol <- unlist(asgn[frail]) # remove these columns from X X <- X[, -dcol, drop=FALSE] asgn <- asgn[!frail] # frailties don't appear in the varnames, so no change there } nterm <- length(asgn) termname <- names(asgn) if (any(is.na(fit$coefficients))) { keep <- !is.na(fit$coefficients) varnames <- varnames[keep] X <- X[,keep] # fix up assign new <- unname(unlist(asgn))[keep] # the ones to keep asgn <- sapply(asgn, function(x) { i <- match(x, new, nomatch=0) i[i>0]}) asgn <- asgn[sapply(asgn, length)>0] # drop any that were lost termname <- names(asgn) nterm <- length(asgn) # asgn will be a list nvar <- length(new) } @ The zph1 and zph2 functions do not consider penalties, so we need to add those back in after the call. Nothing needs to be done wrt the first derivative: we already ignore the first ncoef elements of the returned first derivative (u) vector, which would have had a penalty. The second portion of u is for beta=0, and all of the penalties that currently are implemented have first derivative 0 at 0. For the second derivative, the current penalties (frailty, rigde, pspline) have a second derivative penalty that is independent of beta-hat. The coxph result contains the numeric value of the penalty at the solution, and we use a score test that would penalize the new time*pspline() term in the same way as the pspline term was penalized. If no coefficients were missing then allvar will be 1:n, otherwise it will have holes. <>= test <- double(nterm+1) df <- rep(1L, nterm+1) u0 <- rep(0, nvar) if (!is.null(fit$coxlist2)) { # there are penalized terms pmat <- matrix(0., 2*nvar, 2*nvar) # second derivative penalty pmat[1:nvar, 1:nvar] <- fit$coxlist2$second pmat[1:nvar + nvar, 1:nvar + nvar] <- fit$coxlist2$second imatr <- resid$imat + pmat } else imatr <- resid$imat for (ii in 1:nterm) { jj <- asgn[[ii]] kk <- c(1:nvar, jj+nvar) imat <- imatr[kk, kk] u <- c(u0, resid$u[jj+nvar]) if (singledf && length(jj) >1) { vv <- solve(imat)[-(1:nvar), -(1:nvar)] t1 <- sum(fit$coef[jj] * resid$u[jj+nvar]) test[ii] <- t1^2 * (fit$coef[jj] %*% vv %*% fit$coef[jj]) df[ii] <- 1 } else { test[ii] <- drop(solve(imat,u) %*% u) if (is.null(fit$df)) df[ii] <- length(jj) else df[ii] <- fit$df[ii] } } #Global test if (global) { u <- c(u0, resid$u[-(1:nvar)]) test[nterm+1] <- solve(imatr, u) %*% u if (is.null(fit$df)) df[nterm+1] <- nvar else df[nterm+1] <- sum(fit$df) tbl <- cbind(test, df, pchisq(test, df, lower.tail=FALSE)) dimnames(tbl) <- list(c(termname, "GLOBAL"), c("chisq", "df", "p")) } else { tbl <- cbind(test, df, pchisq(test, df, lower.tail=FALSE))[1:nterm,, drop=FALSE] dimnames(tbl) <- list(termname, c("chisq", "df", "p")) } # The x, y, residuals part is sorted by time within strata; this is # what the C routine zph1 and zph2 return indx <- if (ny==2) ord +1 else rev(ord) +1 # return to 1 based subscripts indx <- indx[event[indx]] # only keep the death times rval <- list(table=tbl, x=unname(ttimes[indx]), time=unname(y[indx, ny-1])) if (length(cget$strata)) rval$strata <- cget$strata[indx] @ The matrix of scaled Schoenfeld residuals is created one stratum at a time. The ideal for the residual $r(t_i)$, contributed by an event for subject $i$ at time $t_i$ is to use $r_iV^{-1}(t_i)$, the inverse of the variance matrix of $X$ at that time and for the relevant stratum. What is returned as \code{resid\$imat} is $\sum_i V(t_i)$. One option would have been to return all the individual $\hat V_i$ matrices, but that falls over when the number at risk is too small and it cannot be inverted. Option 2 would be to use a per stratum averge of the $V_i$, but that falls flat for models with a large number of strata, a nested case-control model for instance. We take a different average that may not be the best, but seems to be good enough and doesn't seem to fail. \begin{enumerate} \item The \code{resid\$used} matrix contains the number of deaths for each strata (row) that contributed to the sum for each variable (column). The value is either 0 or the number of events in the stratum, zero for those variables that are constant within the stratum. From this we can get the number of events that contributed to each element of the \code{imat} total. Dividing by this gives a per-element average \code{vmean}. \item For a given stratum, some of the covariates may have been unused. For any of those set the scaled Schoenfeld residual to NA, and use the other rows/columns of the \code{vmean} matrix to scale the rest. \end{enumerate} Now if some variable $x_1$ has a large variance at some time points and a small variance at others, or a large variance in one stratum and a small variance in another, the above smoothing won't catch that subtlety. However we expect such an issue to be rare. The common problem of strata*covariate interactions is the target of the above manipulations. <>= # Watch out for a particular edge case: there is a factor, and one of the # strata happens to not use one of its levels. The element of resid$used will # be zero, but it really should not. used <-resid$used for (i in asgn) { if (length(i) > 1 && any(used[,i] ==0)) used[,i] <- apply(used[,i,drop=FALSE], 1, max) } # Make the weight matrix wtmat <- matrix(0, nvar, nvar) for (i in 1:nrow(used)) wtmat <- wtmat + outer(used[i,], used[i,], pmin) # with strata*covariate interactions (multi-state models for instance) the # imatr matrix will be block diagonal. Don't divide these off diagonal zeros # by a wtmat value of zero. vmean <- imatr[1:nvar, 1:nvar, drop=FALSE]/ifelse(wtmat==0, 1, wtmat) sresid <- resid$schoen if (terms && any(sapply(asgn, length) > 1)) { # collase multi-column terms temp <- matrix(0, ncol(sresid), nterm) for (i in 1:nterm) { j <- asgn[[i]] if (length(j) ==1) temp[j, i] <- 1 else temp[j, i] <- fit$coefficients[j] } sresid <- sresid %*% temp vmean <- t(temp) %*% vmean %*% temp used <- used[, sapply(asgn, function(x) x[1]), drop=FALSE] } dimnames(sresid) <- list(signif(rval$time, 4), termname) # for each stratum, rescale the Schoenfeld residuals in that stratum sgrp <- rep(1:nrow(used), apply(used, 1, max)) for (i in 1:nrow(used)) { k <- which(used[i,] > 0) if (length(k) >0) { # there might be no deaths in the stratum j <- which(sgrp==i) if (length(k) ==1) sresid[j,k] <- sresid[j,k]/vmean[k,k] else sresid[j, k] <- t(solve(vmean[k, k], t(sresid[j, k, drop=FALSE]))) sresid[j, -k] <- NA } } # Add in beta-hat. For a term with multiple columns we are testing zph for # the linear predictor X\beta, which always has a coefficient of 1 for (i in 1:nterm) { j <- asgn[[i]] if (length(j) ==1) sresid[,i] <- sresid[,i] + fit$coefficients[j] else sresid[,i] <- sresid[,i] +1 } rval$y <- sresid rval$var <- solve(vmean) @ <>= "[.cox.zph" <- function(x, ..., drop=FALSE) { i <- ..1 if (!is.null(x$strata)) { y2 <- x$y[,i,drop=FALSE] ymiss <- apply(is.na(y2), 1, all) if (any(ymiss)) { # some deaths played no role in these coefficients # due to a strata * covariate interaction, drop unneeded rows z<- list(table=x$table[i,,drop=FALSE], x=x$x[!ymiss], time= x$time[!ymiss], strata = x$strata[!ymiss], y = y2[!ymiss,,drop=FALSE], var=x$var[i,i, drop=FALSE], transform=x$transform, call=x$call) } else z<- list(table=x$table[i,,drop=FALSE], x=x$x, time= x$time, strata = x$strata, y = y2, var=x$var[i,i, drop=FALSE], transform=x$transform, call=x$call) } else z<- list(table=x$table[i,,drop=FALSE], x=x$x, time= x$time, y = x$y[,i,drop=FALSE], var=x$var[i,i, drop=FALSE], transform=x$transform, call=x$call) class(z) <- class(x) z } @ survival/noweb/survfitCI.Rnw0000644000176200001440000005455214041423076015661 0ustar liggesusers\subsection{Competing risks} \newcommand{\Twid}{\mbox{\(\tt\sim\)}} The competing risks routine is very general, allowing subjects to enter or exit states multiple times. Early on I used the label \emph{current prevalence} estimate, since it estimates what fraction of the subjects are in any given state across time. However the word ``prevalence'' is likely to generate confusion whenever death is one of the states, due to its historic use as the fraction of living subjects who have a particular condition. We will use the phrase \emph{probability in state} or simply $P$ from this point forward. The easiest way to understand the estimate is to consider first the case of no censoring. In that setting the estimate of $p_k(t)$ for all states is obtained from a simple table of the current state at time $t$ of the subjects, divided by $n$, the original sample size. When there is censoring the conceptually simple way to extend this is via the redistribute-to-the-right algorithm, which allocates the case weight for a censored subject evenly to all the others in the same state at the time of censoring. The literature refers to these as ``cumulative incidence'' curves, which is confusing since P(state) is not the integral of incidence, but the routine name survfitCI endures. The cannonical call is one of \begin{verbatim} fit <- survfit(Surv(time, status) ~ sex, data=mine) fit <- survfit(Surv(time1, time2, status) ~ sex, id= id, data=mine) \end{verbatim} where \code{status} is a factor variable. Optionally, there can be an id statement or cluster term to indicate a data set with multiple transitions per subject. For multi-state survival the status variable has multiple levels, the first of which by default is censoring, and others indicating the type of transition that occured. The result will be a matrix of survival curves, one for each event type. If no initial state is specified then subjects are assumed to start in a "null" state, which gets listed last and by default will not be printed or plotted. (But it is present, with a name of `'); The first part of the code is standard, parsing out options and checking the data. <>= <> survfitCI <- function(X, Y, weights, id, cluster, robust, istate, stype=1, ctype=1, se.fit=TRUE, conf.int= .95, conf.type=c('log', 'log-log', 'plain', 'none', 'logit', "arcsin"), conf.lower=c('usual', 'peto', 'modified'), influence = FALSE, start.time, p0, type){ if (!missing(type)) { if (!missing(ctype) || !missing(stype)) stop("cannot have both an old-style 'type' argument and the stype/ctype arguments that replaced it") if (!is.character(type)) stop("type argument must be character") # older style argument is allowed temp <- charmatch(type, c("kaplan-meier", "fleming-harrington", "fh2")) if (is.na(temp)) stop("invalid value for 'type'") type <- c(1,3,4)[temp] } else { if (!(ctype %in% 1:2)) stop("ctype must be 1 or 2") if (!(stype %in% 1:2)) stop("stype must be 1 or 2") type <- as.integer(2*stype + ctype -2) } if (type != 1) warning("only stype=1, ctype=1 currently implimented for multi-state data") conf.type <- match.arg(conf.type) conf.lower<- match.arg(conf.lower) if (conf.lower != "usual") warning("conf.lower is ignored for multi-state data") if (is.logical(conf.int)) { # A common error is for users to use "conf.int = FALSE" # it's illegal per documentation, but be kind if (!conf.int) conf.type <- "none" conf.int <- .95 } if (is.logical(influence)) { # TRUE/FALSE is treated as all or nothing if (!influence) influence <- 0L else influence <- 3L } else if (!is.numeric(influence)) stop("influence argument must be numeric or logical") if (!(influence %in% 0:3)) stop("influence argument must be 0, 1, 2, or 3") else influence <- as.integer(influence) if (!se.fit) { # if the user asked for no standard error, skip any robust computation ncluster <- 0L influence <- 0L } type <- attr(Y, "type") # This line should be unreachable, unless they call "surfitCI" directly if (type !='mright' && type!='mcounting') stop(paste("multi-state computation doesn't support \"", type, "\" survival data", sep='')) # If there is a start.time directive, start by removing any prior events if (!missing(start.time)) { if (!is.numeric(start.time) || length(start.time) !=1 || !is.finite(start.time)) stop("start.time must be a single numeric value") toss <- which(Y[,ncol(Y)-1] <= start.time) if (length(toss)) { n <- nrow(Y) if (length(toss)==n) stop("start.time has removed all observations") Y <- Y[-toss,,drop=FALSE] X <- X[-toss] weights <- weights[-toss] if (length(id) ==n) id <- id[-toss] if (!missing(istate) && length(istate)==n) istate <- istate[-toss] } } n <- nrow(Y) status <- Y[,ncol(Y)] ncurve <- length(levels(X)) # The user can call with cluster, id, robust or any combination. # If only id, treat it as the cluster too if (missing(robust) || length(robust)==0) robust <- TRUE if (!robust) stop("multi-state survfit supports only a robust variance") has.cluster <- !(missing(cluster) || length(cluster)==0) has.id <- !(missing(id) || length(id)==0) if (has.id) id <- as.factor(id) else { if (ncol(Y) ==3) stop("an id statement is required for start,stop data") id <- 1:n # older default, which could lead to invalid curves } if (influence && !(has.cluster || has.id)) { cluster <- seq(along.with=X) has.cluster <- TRUE } if (has.cluster) { if (is.factor(cluster)) { clname <- levels(cluster) cluster <- as.integer(cluster) } else { clname <- sort(unique(cluster)) cluster <- match(cluster, clname) } ncluster <- length(clname) } else { if (has.id) { # treat the id as both identifier and clustering clname <- levels(id) cluster <- as.integer(id) ncluster <- length(clname) } else { ncluster <- 0 # has neither clname <- NULL } } if (missing(istate) || is.null(istate)) mcheck <- survcheck2(Y, id) else mcheck <- survcheck2(Y, id, istate) if (any(mcheck$flag > 0)) stop("one or more flags are >0 in survcheck") states <- mcheck$states istate <- mcheck$istate nstate <- length(states) smap <- c(0, match(attr(Y, "states"), states)) Y[,ncol(Y)] <- smap[Y[,ncol(Y)] +1] # new states may be a superset status <- Y[,ncol(Y)] if (mcheck$flag["overlap"] > 0) stop("a subject has overlapping time intervals") # if (mcheck$flag["gap"] > 0 || mcheck$flag["jump"] > 0) # warning("subject(s) with time gaps, results may be questionable") # The states of the status variable are the first columns in the output # any extra initial states are later in the list. # Now that we know the names, verify that p0 is correct (if present) if (!missing(p0) && !is.null(p0)) { if (length(p0) != nstate) stop("wrong length for p0") if (!is.numeric(p0) || abs(1-sum(p0)) > sqrt(.Machine$double.eps)) stop("p0 must be a numeric vector that adds to 1") } else p0 <- NULL @ The status vector will have values of 0 for censored. <>= curves <- vector("list", ncurve) names(curves) <- levels(X) if (ncol(Y)==2) { # 1 transition per subject # dummy entry time that is < any event time t0 <- min(0, Y[,1]) entry <- rep(t0-1, nrow(Y)) for (i in levels(X)) { indx <- which(X==i) curves[[i]] <- docurve2(entry[indx], Y[indx,1], status[indx], istate[indx], weights[indx], states, id[indx], se.fit, influence, p0) } } else { <> <> } <> } @ In the multi-state case we can calculate the current P(state) vector $p(t)$ using the product-limit form, while the cumulative hazard $c(t)$ is a sum. \begin{align*} p(t) &= p(0)\prod_{s<=t} [I + dA(s)] \\ &= p(0) \prod_{s<=t} H(s) \\ c(t) &= \sum_{s<=t} dA(s) \end{align*} Where $p$ is a row vector and $H$ is the multi-state hazard matrix. $H(t)$ is a simple transition matrix. Row $j$ of $H$ describes the outcome of everyone who was in state $j$ at time $t-0$; and is the fraction of them who are in states $1, 2, \ldots$ at time $t+0$. Let $Y_{ij}(t)$ be the indicator function which is 1 if subject $i$ is in state $j$ at time $t-0$, then \begin{equation} H_{jk}(t) = \frac{\sum_i w_i Y_{ij}(t) Y_{ik}(t+)} {\sum_i w_i Y_{ij}(t)} \label{H} \end{equation} Each row of $H$ sums to 1: everyone has to go somewhere. This formula collapses to the Kaplan-Meier in the simple case where $p(t)$ is a vector of length 2 with state 1 = alive and state 2 = dead. The variance is based on per-subject influence. Since $p(t)$ is a vector the influence can be written as a matrix with one row per subject and one column per state. $$ U_{ij}(t) \equiv \frac{\partial p_j(t)}{\partial w_i}. $$ This can be calculate using a recursive formula. First, the derivative of a matrix product $AB$ is $d(A)B + Ad(B)$ where $d(A)$ is the elementwise derivative of $A$ and similarly for $B$. (Write out each element of the matrix product.) Since $p(t) = p(t-)H(t)$, the $i$th row of U satisfies \begin{align} U_i(t) &= \frac{\partial p(t)}{\partial w_i} \nonumber \\ &= \frac{\partial p(t-)}{\partial w_i} H(t) + p(t-) \frac{\partial H(t)}{\partial w_i} \nonumber \\ &= U_i(t-) H(t) + p(t-) \frac{\partial H(t)}{\partial w_i} \label{ci} \end{align} The first term of \ref{ci} collapses to ordinary matrix multiplication. The second term does not: each at risk subject has a unique matrix derivative $\partial H$; $n$ vectors of length $p$ can be arranged into a matrix, making the code simple, but $n$ $p$ by $p$ matrices are not so neat. However, note that \begin{enumerate} \item $\partial H$ is zero for anyone not in the risk set, since their weight does not appear in $H$. \item Each subject who is at risk will be in one (and only one) of the states at the event time, their weight only appears in that row of $H$. Thus for each at risk subject $\partial H$ has only one non-zero row. \end{enumerate} Say that the subject enters the given event time in state $j$ and ends it in state $k$. (For most subjects at most time poinnts $k=j$: if there are 100 at risk at time $t$ and 1 changes state, the other 99 stay put.) Let $n_j(t)= \sum_i Y_{ij}(t)w_i$ be the weighted number of subjects in state $j$, these are the contributers to row $j$ of $H$. Using equation \ref{H}, the derivative of row $j$ with respect to the subject is $(1_k - H_j)/n_j$ where $1_k$ is a vector with 1 in position $k$. The product of $p(t)$ with this matrix is the vector $p_j(t)(1_k - H_j)/n_j$. The second term thus turns out to be fairly simple to compute, but I have not seen a way to write it in a compact matrix form The weighted sum of each column of $U$ will be zero (if computed correctly) and the weighted sum of squares for each column will be the infinitesimal jackknife estimate of variance for the elements of $p$. The entire variance-covariance matrix for the states is $U'W^2U$ where $W$ is a diagonal matrix of weights, but we currently don't report that back. Note that this is for sampling weights. If one has real case weights, where an integer weight of 2 means 2 observations that were collapsed in to one row of data to save space, then the variance is $U'WU$. Case weights were somewhat common in my youth due to small computer memory, but I haven't seen such data in 20 years. The residuals for the cumulative hazard are an easier computation, since each hazard function stands alone. In a multistate model with $k$ states there are potentially $k(k-1)$ hazard functions arranged in a $k$ by $k$ matrix, i.e., as used for the NA update; in the code both the hazard, the IJ scores and the standard errors are kept as matrices with a column for each combination that does occur. At each event time only the rows of U2 that correspond to the risk set will be updated. Below is the function for a single curve. For the status variable a value if 0 is ``no event''. One nuisance in the function is that we need to ensure the tapply command gives totals for all states, not just the ones present in the data --- a call using the \code{subset} argument might not have all the states --- which leads to using factor commands. Another more confusing one is for multiple rows per subject data, where the cstate and U objects have only one row per subject; any given subject is only in one state at a time. This leads to indices of [[atrisk]] for the set of rows in the risk set but [[aindx]] for the subjects in the risk set, [[death]] for the rows that have an event this time and [[dindx]] for the corresponding subjects. The setup for (start, stop] data is a bit more work. We want to ensure that a given subject remains in the same group and that they have a continuous period of observation. If the input data was the result of a tmerge call, say, it might have a lot of extra 'censored' rows. For instance a subject whose state pattern is (0, 5, 1), (5,10, 2), i.e., a transition to state 1 at day 5 and state 2 on day 10 might input as (0,2,0), (2,5,1), (5,6,0), (6,8,0), (8,10,2) instead. These extra censors cause an unnecessary row of output on days 2, 6, and 8. Remove these before going further. <>= # extra censors indx <- order(id, Y[,2]) # in stop order extra <- (survflag(Y[indx,], id[indx]) ==0 & (Y[indx,3] ==0)) # If a subject had obs of (a, b)(b,c)(c,d), and c was a censoring # time, that is an "extra" censoring/entry at c that we don't want # to count. Deal with it by changing that subject # to (a,b)(b,d). Won't change S(t), only the n.censored/n.enter count. if (any(extra)) { e2 <- indx[extra] Y <- cbind(Y[-(1+e2),1], Y[-e2,-1]) status <- status[-e2] X <- X[-e2] id <- id[-e2] istate <- istate[-e2] weights <- weights[-e2] indx <- order(id, Y[,2]) } @ <>= # Now to work for (i in levels(X)) { indx <- which(X==i) # temp <- docurve1(Y[indx,1], Y[indx,2], status[indx], # istate[indx], weights[indx], states, id[indx]) curves[[i]] <- docurve2(Y[indx,1], Y[indx,2], status[indx], istate[indx], weights[indx], states, id[indx], se.fit, influence, p0) } @ <>= # Turn the result into a survfit type object grabit <- function(clist, element) { temp <-(clist[[1]][[element]]) if (is.matrix(temp)) { do.call("rbind", lapply(clist, function(x) x[[element]])) } else { xx <- as.vector(unlist(lapply(clist, function(x) x[element]))) if (inherits(temp, "table")) matrix(xx, byrow=T, ncol=length(temp)) else xx } } # we want to rearrange the cumulative hazard to be in time order # with one column for each observed transtion. nstate <- length(states) temp <- matrix(0, nstate, nstate) indx1 <- match(rownames(mcheck$transitions), states) indx2 <- match(colnames(mcheck$transitions), states, nomatch=0) #ignore censor temp[indx1, indx2[indx2>0]] <- mcheck$transitions[,indx2>0] ckeep <- which(temp>0) names(ckeep) <- outer(1:nstate, 1:nstate, paste, sep='.')[ckeep] #browser() if (length(curves) ==1) { keep <- c("n", "time", "n.risk", "n.event", "n.censor", "pstate", "p0", "cumhaz", "influence.pstate") if (se.fit) keep <- c(keep, "std.err", "sp0") kfit <- (curves[[1]])[match(keep, names(curves[[1]]), nomatch=0)] names(kfit$p0) <- states if (se.fit) kfit$logse <- FALSE kfit$cumhaz <- t(kfit$cumhaz[ckeep,,drop=FALSE]) colnames(kfit$cumhaz) <- names(ckeep) } else { kfit <- list(n = as.vector(table(X)), #give it labels time = grabit(curves, "time"), n.risk= grabit(curves, "n.risk"), n.event= grabit(curves, "n.event"), n.censor=grabit(curves, "n.censor"), pstate = grabit(curves, "pstate"), p0 = grabit(curves, "p0"), strata= unlist(lapply(curves, function(x) if (is.null(x$time)) 0L else length(x$time)))) kfit$p0 <- matrix(kfit$p0, ncol=nstate, byrow=TRUE, dimnames=list(names(curves), states)) if (se.fit) { kfit$std.err <- grabit(curves, "std.err") kfit$sp0<- matrix(grabit(curves, "sp0"), ncol=nstate, byrow=TRUE) kfit$logse <- FALSE } # rearrange the cumulative hazard to be in time order, with columns # for each transition kfit$cumhaz <- do.call(rbind, lapply(curves, function(x) t(x$cumhaz[ckeep,,drop=FALSE]))) colnames(kfit$cumhaz) <- names(ckeep) if (influence) kfit$influence.pstate <- lapply(curves, function(x) x$influence.pstate) } if (!missing(start.time)) kfit$start.time <- start.time kfit$transitions <- mcheck$transitions @ <>= # # Last bit: add in the confidence bands: # if (se.fit && conf.type != "none") { ci <- survfit_confint(kfit$pstate, kfit$std.err, logse=FALSE, conf.type, conf.int) kfit <- c(kfit, ci, conf.type=conf.type, conf.int=conf.int) } kfit$states <- states kfit$type <- attr(Y, "type") kfit @ The updated docurve function is here. One issue that was not recognized originally is delayed entry. If most of the subjects start at time 0, say, but one of them starts at day 100 then that last subject is not a part of $p_0$. We will define $p_0$ as the distribution of states just before the first event. The code above has already ensured that each subject has a unique value for istate, so we don't have to search for the right one. The initial vector and leverage are \begin{align*} p_0 &= (\sum I{s_i=1}w_i, \sum I{s_i=2}w_i, \ldots)/ \sum w_i \\ \frac{\partial p_0}{\partial w_k} &= [(I{s_k=1}, I{s_k=2}, ...)- p_0]/\sum w_i \end{align*} The input data set is not necessarily sorted by time or subject. The data has been checked so that subjects don't have gaps, however. The cstate variable for each subject contains their first istate value. Only those intervals that overlap the first event time contribute to $p_0$. Now: what to report as the ``time'' for the initial row. The values for it come from (first event time -0), i.e. all who are at risk at the smallest \code{etime} with status $>0$. But for normal plotting the smallest start time seems to be a good default. In the usual (start, stop] data a large chunk of the subjects have a common start time. However, if the first event doesn't happen for a while and subjects are dribbling in, then the best point to start a plot is open to debate. Que sera sera. <>= docurve2 <- function(entry, etime, status, istate, wt, states, id, se.fit, influence=FALSE, p0) { timeset <- sort(unique(etime)) nstate <- length(states) uid <- sort(unique(id)) index <- match(id, uid) # Either/both of id and cstate might be factors. Data may not be in # order. Get the initial state for each subject temp1 <- order(id, entry) temp2 <- match(uid, id[temp1]) cstate <- (as.numeric(istate)[temp1])[temp2] # initial state for each # The influence matrix can be huge, make sure we have enough memory if (influence) { needed <- max(nstate * length(uid), 1 + length(timeset)) if (needed > .Machine$integer.max) stop("number of rows for the influence matrix is > the maximum integer") } storage.mode(wt) <- "double" # just in case someone had integer weights # Compute p0 (unless given by the user) if (is.null(p0)) { if (all(status==0)) t0 <- max(etime) #failsafe else t0 <- min(etime[status!=0]) # first transition event at.zero <- (entry < t0 & etime >= t0) wtsum <- sum(wt[at.zero]) # weights for a subject may change p0 <- tapply(wt[at.zero], istate[at.zero], sum) / wtsum p0 <- ifelse(is.na(p0), 0, p0) #for a state not in at.zero, tapply =NA } # initial leverage matrix nid <- length(uid) i0 <- matrix(0., nid, nstate) if (all(p0 <1)) { #actually have to compute it who <- index[at.zero] # this will have no duplicates for (j in 1:nstate) i0[who,j] <- (ifelse(istate[at.zero]==states[j], 1, 0) - p0[j])/wtsum } storage.mode(cstate) <- "integer" storage.mode(status) <- "integer" # C code has 0 based subscripts if (influence) se.fit <- TRUE # se.fit is free in this case fit <- .Call(Csurvfitci, c(entry, etime), order(entry) - 1L, order(etime) - 1L, length(timeset), status, as.integer(cstate) - 1L, wt, index -1L, p0, i0, as.integer(se.fit) + 2L*as.integer(influence)) if (se.fit) out <- list(n=length(etime), time= timeset, p0 = p0, sp0= sqrt(colSums(i0^2)), pstate = fit$p, std.err=fit$std, n.risk = fit$nrisk, n.event= fit$nevent, n.censor=fit$ncensor, cumhaz = fit$cumhaz) else out <- list(n=length(etime), time= timeset, p0=p0, pstate = fit$p, n.risk = fit$nrisk, n.event = fit$nevent, n.censor= fit$ncensor, cumhaz= fit$cumhaz) if (influence) { temp <- array(fit$influence, dim=c(length(uid), nstate, 1+ length(timeset)), dimnames=list(uid, NULL, NULL)) out$influence.pstate <- aperm(temp, c(1,3,2)) } out } @ survival/.Rinstignore0000644000176200001440000000002613537676562014453 0ustar liggesusersinst/doc/validate.tex survival/data/0000755000176200001440000000000014073031214013031 5ustar liggesuserssurvival/data/flchain.rda0000644000176200001440000015174013734345570015155 0ustar liggesusersý7zXZi"Þ6!ÏXÌé$éÓ£])ThänRÊ 3Å$nµ/X¨sêZæœTØjöB°r”¦Â€‰Á™¨• Ã"Øñ!Ç©[awþ¿¿èLYå"’Í”g\Êñæ„c¬! 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The default is a step function for \code{survfit} objects, and a connected line for \code{survexp} objects. All other arguments for \code{lines.survexp} are identical to those for \code{lines.survfit}. } \item{col, lty, lwd, cex}{ vectors giving the mark symbol, color, line type, line width and character size for the added curves. Of this set only color is applicable to \code{points}. } \item{pch}{plotting characters for points, in the style of \code{matplot}, i.e., either a single string of characters of which the first will be used for the first curve, etc; or a vector of characters or integers, one element per curve. } \item{mark}{a historical alias for \code{pch}} \item{censor}{should censoring times be displayed for the \code{points} function? } \item{mark.time}{ controls the labeling of the curves. If \code{FALSE}, no labeling is done. If \code{TRUE}, then curves are marked at each censoring time. If \code{mark.time} is a numeric vector, then curves are marked at the specified time points. } \item{xmax}{optional cutoff for the right hand of the curves.} \item{fun}{ an arbitrary function defining a transformation of the survival curve. For example \code{fun=log} is an alternative way to draw a log-survival curve (but with the axis labeled with log(S) values). Four often used transformations can be specified with a character argument instead: "log" is the same as using the \code{log=T} option, "event" plots cumulative events (f(y) = 1-y), "cumhaz" plots the cumulative hazard function (f(y) = -log(y)) and "cloglog" creates a complimentary log-log survival plot (f(y) = log(-log(y))) along with log scale for the x-axis. } \item{conf.int}{ if \code{TRUE}, confidence bands for the curves are also plotted. If set to \code{"only"}, then only the CI bands are plotted, and the curve itself is left off. This can be useful for fine control over the colors or line types of a plot. A numeric value, e.g. \code{conf.int = .90}, can be used to } \item{conf.times}{optional vector of times at which to place a confidence bar on the curve(s). If present, these will be used instead of confidence bands.} \item{conf.cap}{width of the horizontal cap on top of the confidence bars; only used if conf.times is used. A value of 1 is the width of the plot region.} \item{conf.offset}{the offset for confidence bars, when there are multiple curves on the plot. A value of 1 is the width of the plot region. If this is a single number then each curve's bars are offset by this amount from the prior curve's bars, if it is a vector the values are used directly.} \item{conf.type}{ One of \code{"plain"}, \code{"log"} (the default), \code{"log-log"}, \code{"logit"}, or \code{"none"}. Only enough of the string to uniquely identify it is necessary. The first option causes confidence intervals not to be generated. The second causes the standard intervals \code{curve +- k *se(curve)}, where k is determined from \code{conf.int}. The log option calculates intervals based on the cumulative hazard or log(survival). The log-log option bases the intervals on the log hazard or log(-log(survival)), and the logit option on log(survival/(1-survival)). } \item{noplot}{for multi-state models, curves with this label will not be plotted. The default corresponds to an unspecified state.} \item{cumhaz}{plot the cumulative hazard, rather than the survival or probability in state.} \item{...}{other graphical parameters} } \value{ a list with components \code{x} and \code{y}, containing the coordinates of the last point on each of the curves (but not of the confidence limits). This may be useful for labeling. } \section{Side Effects}{ one or more curves are added to the current plot. } \seealso{ \code{\link{lines}}, \code{\link{par}}, \code{\link{plot.survfit}}, \code{\link{survfit}}, \code{\link{survexp}}. } \details{ When the \code{survfit} function creates a multi-state survival curve the resulting object has class `survfitms'. The only difference in the plots is that that it defaults to a curve that goes from lower left to upper right (starting at 0), where survival curves default to starting at 1 and going down. All other options are identical. If the user set an explicit range in an earlier \code{plot.survfit} call, e.g. via \code{xlim} or \code{xmax}, subsequent calls to this function remember the right hand cutoff. This memory can be erased by \code{options(plot.survfit) <- NULL}. } \examples{ fit <- survfit(Surv(time, status==2) ~ sex, pbc,subset=1:312) plot(fit, mark.time=FALSE, xscale=365.25, xlab='Years', ylab='Survival') lines(fit[1], lwd=2) #darken the first curve and add marks # Add expected survival curves for the two groups, # based on the US census data # The data set does not have entry date, use the midpoint of the study efit <- survexp(~sex, data=pbc, times= (0:24)*182, ratetable=survexp.us, rmap=list(sex=sex, age=age*365.35, year=as.Date('1979/01/01'))) temp <- lines(efit, lty=2, lwd=2:1) text(temp, c("Male", "Female"), adj= -.1) #labels just past the ends title(main="Primary Biliary Cirrhosis, Observed and Expected") } \keyword{survival} survival/man/reliability.Rd0000644000176200001440000000537514013462065015513 0ustar liggesusers\name{reliability} \docType{data} \alias{reliability} \alias{capacitor} \alias{cracks} \alias{genfan} \alias{ifluid} \alias{imotor} \alias{turbine} \alias{valveSeat} \title{Reliability data sets} \description{ A set of data for simple reliablility analyses, taken from the book by Meeker and Escobar. } \usage{data(reliability, package="survival") } \details{ \itemize{ \item \code{capacitor}: Data from a factorial experiment on the life of glass capacitors as a function of voltage and operating temperature. There were 8 capacitors at each combination of temperature and voltage. Testing at each combination was terminated after the fourth failure. \itemize{ \item \code{temperature}: temperature in degrees celcius \item \code{voltage}: applied voltage \item \code{time}: time to failure \item \code{status}: 1=failed, 0=censored } \item \code{cracks}: Data on the time until the development of cracks in a set of 167 identical turbine parts. The parts were inspected at 8 selected times. \itemize{ \item day: time of inspection \item fail: number of fans found to have cracks, at this inspection } \item Data set \code{genfan}: Time to failure of 70 diesel engine fans. \itemize{ \item \code{hours}: hours of service \item \code{status}: 1=failure, 0=censored } Data set \code{ifluid}: A data frame with two variables describing the time to electrical breakdown of an insulating fluid. \itemize{ \item \code{time}: hours to breakdown \item \code{voltage}: test voltage in kV } \item Data set \code{imotor}: Breakdown of motor insulation as a function of temperature. \itemize{ \item temp: temperature of the test \item time: time to failure or censoring \item status: 0=censored, 1=failed } \item Data set \code{turbine}: Each of 432 turbine wheels was inspected once to determine whether a crack had developed in the wheel or not. \itemize{ \item hours: time of inspection (100s of hours) \item inspected: number that were inspected \item failed: number that failed } Data set \code{valveSeat}: Time to replacement of valve seats for 41 diesel engines. More than one seat may be replaced at a particular service, leading to duplicate times in the data set. The final inspection time for each engine will have status=0. \itemize{ \item id: engine identifier \item time: time of the inspection, in days \item status: 1=replacement occured, 0= not } } } \references{ Meeker and Escobar, Statistical Methods for Reliability Data, 1998. } \examples{ survreg(Surv(time, status) ~ temperature + voltage, capacitor) } \keyword{datasets} survival/man/ridge.Rd0000644000176200001440000000374614013475150014273 0ustar liggesusers\name{ridge} \alias{ridge} \title{ Ridge regression} \usage{ ridge(..., theta, df=nvar/2, eps=0.1, scale=TRUE) } \arguments{ \item{\dots}{predictors to be ridged } \item{theta}{penalty is \code{theta}/2 time sum of squared coefficients } \item{df}{Approximate degrees of freedom } \item{eps}{ Accuracy required for \code{df} } \item{scale}{ Scale variables before applying penalty? } } \description{ When used in a \link{coxph} or \link{survreg} model formula, specifies a ridge regression term. The likelihood is penalised by \code{theta}/2 time the sum of squared coefficients. If \code{scale=T} the penalty is calculated for coefficients based on rescaling the predictors to have unit variance. If \code{df} is specified then \code{theta} is chosen based on an approximate degrees of freedom. } \note{ If the expression \code{ridge(x1, x2, x3, ...)} is too many characters long then the internal terms() function will add newlines to the variable name and then the coxph routine simply gets lost. (Some labels will have the newline and some won't.) One solution is to bundle all of the variables into a single matrix and use that matrix as the argument to \code{ridge} so as to shorten the call, e.g. \code{mdata$many <- as.matrix(mydata[,5:53])}. } \value{ An object of class \code{coxph.penalty} containing the data and control functions. } \references{ Gray (1992) "Flexible methods of analysing survival data using splines, with applications to breast cancer prognosis" JASA 87:942--951 } \seealso{ \code{\link{coxph}},\code{\link{survreg}},\code{\link{pspline}},\code{\link{frailty}} } \examples{ coxph(Surv(futime, fustat) ~ rx + ridge(age, ecog.ps, theta=1), ovarian) lfit0 <- survreg(Surv(time, status) ~1, lung) lfit1 <- survreg(Surv(time, status) ~ age + ridge(ph.ecog, theta=5), lung) lfit2 <- survreg(Surv(time, status) ~ sex + ridge(age, ph.ecog, theta=1), lung) lfit3 <- survreg(Surv(time, status) ~ sex + age + ph.ecog, lung) } \keyword{survival }%-- one or more ... survival/man/cox.zph.Rd0000644000176200001440000000776013561274110014572 0ustar liggesusers\name{cox.zph} \alias{cox.zph} \alias{[.cox.zph} \alias{print.cox.zph} \title{ Test the Proportional Hazards Assumption of a Cox Regression } \description{ Test the proportional hazards assumption for a Cox regression model fit (\code{coxph}). } \usage{ cox.zph(fit, transform="km", terms=TRUE, singledf=FALSE, global=TRUE) } \arguments{ \item{fit}{ the result of fitting a Cox regression model, using the \code{coxph} or \code{coxme} functions. } \item{transform}{ a character string specifying how the survival times should be transformed before the test is performed. Possible values are \code{"km"}, \code{"rank"}, \code{"identity"} or a function of one argument. } \item{terms}{if TRUE, do a test for each term in the model rather than for each separate covariate. For a factor variable with k levels, for instance, this would lead to a k-1 degree of freedom test. The plot for such variables will be a single curve evaluating the linear predictor over time.} \item{singledf}{use a single degree of freedom test for terms that have multiple coefficients, i.e., the test that corresponds most closely to the plot. If \code{terms=FALSE} this argument has no effect.} \item{global}{ should a global chi-square test be done, in addition to the per-variable or per-term tests tests. } } \value{ an object of class \code{"cox.zph"}, with components: \item{table}{ a matrix with one row for each variable, and optionally a last row for the global test. Columns of the matrix contain a score test of for addition of the time-dependent term, the degrees of freedom, and the two-sided p-value. } \item{x}{ the transformed time axis. } \item{time}{the untransformed time values; there is one entry for each event time in the data} \item{strata}{for a stratified \code{coxph model}, the stratum of each of the events} \item{y}{ the matrix of scaled Schoenfeld residuals. There will be one column per term or per variable (depending on the \code{terms} option above), and one row per event. The row labels are a rounded form of the original times. } \item{var}{a variance matrix for the covariates, used to create an approximate standard error band for plots} \item{transform}{the transform of time that was used} \item{call}{ the calling sequence for the routine. }} \details{ The computations require the original \code{x} matrix of the Cox model fit. Thus it saves time if the \code{x=TRUE} option is used in \code{coxph}. This function would usually be followed by both a plot and a print of the result. The plot gives an estimate of the time-dependent coefficient \eqn{\beta(t)}{beta(t)}. If the proportional hazards assumption holds then the true \eqn{\beta(t)}{beta(t)} function would be a horizontal line. The \code{table} component provides the results of a formal score test for slope=0, a linear fit to the plot would approximate the test. Random effects terms such a \code{frailty} or random effects in a \code{coxme} model are not checked for proportional hazards, rather they are treated as a fixed offset in model. If the model contains strata by covariate interactions, then the \code{y} matrix may contain structural zeros, i.e., deaths (rows) that had no role in estimation of a given coefficient (column). These are marked as NA. If an entire row is NA, for instance after subscripting a \code{cox.zph} object, that row is removed. } \note{ In versions of the package before survival3.0 the function computed a fast approximation to the score test. Later versions compute the actual score test. } \references{ P. Grambsch and T. Therneau (1994), Proportional hazards tests and diagnostics based on weighted residuals. \emph{Biometrika,} \bold{81}, 515-26. } \seealso{ \code{\link{coxph}}, \code{\link{Surv}}. } \examples{ fit <- coxph(Surv(futime, fustat) ~ age + ecog.ps, data=ovarian) temp <- cox.zph(fit) print(temp) # display the results plot(temp) # plot curves } \keyword{survival} survival/man/finegray.Rd0000644000176200001440000001047613711602107015001 0ustar liggesusers\name{finegray} \alias{finegray} \title{Create data for a Fine-Gray model} \description{ The Fine-Gray model can be fit by first creating a special data set, and then fitting a weighted Cox model to the result. This routine creates the data set. } \usage{ finegray(formula, data, weights, subset, na.action= na.pass, etype, prefix="fg", count, id, timefix=TRUE) } \arguments{ \item{formula}{a standard model formula, with survival on the left and covariates on the right. } \item{data}{an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. } \item{weights}{optional vector of observation weights} \item{subset}{ an optional vector specifying a subset of observations to be used in the fitting process. } \item{na.action}{ a function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of options. } \item{etype}{ the event type for which a data set will be generated. The default is to use whichever is listed first in the multi-state survival object. } \item{prefix}{the routine will add 4 variables to the data set: a start and end time for each interval, status, and a weight for the interval. The default names of these are "fgstart", "fgstop", "fgstatus", and "fgwt"; the \code{prefix} argument determines the initial portion of the new names. } \item{count}{a variable name in the output data set for an optional variable that will contain the the replication count for each row of the input data. If a row is expanded into multiple lines it will contain 1, 2, etc. } \item{id}{optional, the variable name in the data set which identifies subjects.} \item{timefix}{process times through the \code{aeqSurv} function to eliminate potential roundoff issues.} } \details{ The function expects a multi-state survival expression or variable as the left hand side of the formula, e.g. \code{Surv(atime, astat)} where \code{astat} is a factor whose first level represents censoring and remaining levels are states. The output data set will contain simple survival data (status = 0 or 1) for a single endpoint of interest. In the output data set subjects who did not experience the event of interest become censored subjects whose times are artificially extended over multiple intervals, with a decreasing case weight from interval to interval. The output data set will normally contain many more rows than the input. Time dependent covariates are allowed, but not (currently) delayed entry. If there are time dependent covariates, e.g.., the input data set had \code{Surv(entry, exit, stat)} as the left hand side, then an \code{id} statement is required. The program does data checks in this case, and needs to know which rows belong to each subject. The output data set will often have gaps. Say that there were events at time 50 and 100 (and none between) and censoring at 60, 70, and 80. Formally, a non event subjects at risk from 50 to 100 will have different weights in each of the 3 intervals 50-60, 60-70, and 80-100, but because the middle interval does not span any event times the subsequent Cox model will never use that row. The \code{finegray} output omits such rows. See the competing risks vignette for more details. } \value{a data frame} \references{ Fine JP and Gray RJ (1999) A proportional hazards model for the subdistribution of a competing risk. JASA 94:496-509. Geskus RB (2011). Cause-Specific Cumulative Incidence Estimation and the Fine and Gray Model Under Both Left Truncation and Right Censoring. Biometrics 67, 39-49. } \author{Terry Therneau} \seealso{\code{\link{coxph}}, \code{\link{aeqSurv}}} \examples{ # Treat time to death and plasma cell malignancy as competing risks etime <- with(mgus2, ifelse(pstat==0, futime, ptime)) event <- with(mgus2, ifelse(pstat==0, 2*death, 1)) event <- factor(event, 0:2, labels=c("censor", "pcm", "death")) # FG model for PCM pdata <- finegray(Surv(etime, event) ~ ., data=mgus2) fgfit <- coxph(Surv(fgstart, fgstop, fgstatus) ~ age + sex, weight=fgwt, data=pdata) # Compute the weights separately by sex adata <- finegray(Surv(etime, event) ~ . + strata(sex), data=mgus2, na.action=na.pass) } \keyword{survival} survival/man/diabetic.Rd0000644000176200001440000000346314013461270014737 0ustar liggesusers\name{diabetic} \alias{diabetic} \docType{data} \title{Ddiabetic retinopathy} \description{ Partial results from a trial of laser coagulation for the treatment of diabetic retinopathy. } \usage{diabetic data(diabetic, package="survival") } \format{ A data frame with 394 observations on the following 8 variables. \describe{ \item{\code{id}}{subject id} \item{\code{laser}}{laser type: \code{xenon} or \code{argon}} \item{\code{age}}{age at diagnosis} \item{\code{eye}}{a factor with levels of \code{left} \code{right}} \item{\code{trt}}{treatment: 0 = no treatment, 1= laser} \item{\code{risk}}{risk group of 6-12} \item{\code{time}}{time to event or last follow-up} \item{\code{status}}{status of 0= censored or 1 = visual loss} } } \details{ The 197 patients in this dataset were a 50\% random sample of the patients with "high-risk" diabetic retinopathy as defined by the Diabetic Retinopathy Study (DRS). Each patient had one eye randomized to laser treatment and the other eye received no treatment. For each eye, the event of interest was the time from initiation of treatment to the time when visual acuity dropped below 5/200 two visits in a row. Thus there is a built-in lag time of approximately 6 months (visits were every 3 months). Survival times in this dataset are therefore the actual time to blindness in months, minus the minimum possible time to event (6.5 months). Censoring was caused by death, dropout, or end of the study. } \references{ Huster, Brookmeyer and Self, Biometrics, 1989. American Journal of Ophthalmology, 1976, 81:4, pp 383-396 } \examples{ # juvenile diabetes is defined as and age less than 20 juvenile <- 1*(diabetic$age < 20) coxph(Surv(time, status) ~ trt + juvenile, cluster= id, data= diabetic) } \keyword{datasets} \keyword{survival}survival/man/summary.survfit.Rd0000644000176200001440000001210614041423076016366 0ustar liggesusers\name{summary.survfit} \alias{summary.survfit} \title{ Summary of a Survival Curve } \description{ Returns a list containing the survival curve, confidence limits for the curve, and other information. } \usage{ \method{summary}{survfit}(object, times, censored=FALSE, scale=1, extend=FALSE, rmean=getOption('survfit.rmean'), ...) } \arguments{ \item{object}{ the result of a call to the \code{survfit} function. } \item{times}{ vector of times; the returned matrix will contain 1 row for each time. The vector will be sorted into increasing order; missing values are not allowed. If \code{censored=T}, the default \code{times} vector contains all the unique times in \code{fit}, otherwise the default \code{times} vector uses only the event (death) times. } \item{censored}{ logical value: should the censoring times be included in the output? This is ignored if the \code{times} argument is present. } \item{scale}{ numeric value to rescale the survival time, e.g., if the input data to \code{survfit} were in days, \code{scale = 365.25} would scale the output to years. } \item{extend}{ logical value: if TRUE, prints information for all specified \code{times}, even if there are no subjects left at the end of the specified \code{times}. This is only used if the \code{times} argument is present. } \item{rmean}{Show restricted mean: see \code{\link{print.survfit}} for details} \item{\dots}{for future methods} } \value{ a list with the following components: \item{surv}{ the estimate of survival at time t+0. } \item{time}{ the timepoints on the curve. } \item{n.risk}{ the number of subjects at risk at time t-0 (but see the comments on weights in the \code{survfit} help file). } \item{n.event}{ if the \code{times} argument is missing, then this column is the number of events that occurred at time t. Otherwise, it is the cumulative number of events that have occurred since the last time listed until time t+0. } \item{n.entered}{ This is present only for counting process survival data. If the \code{times} argument is missing, this column is the number of subjects that entered at time t. Otherwise, it is the cumulative number of subjects that have entered since the last time listed until time t. } \item{n.exit.censored}{ if the \code{times} argument is missing, this column is the number of subjects that left without an event at time t. Otherwise, it is the cumulative number of subjects that have left without an event since the last time listed until time t+0. This is only present for counting process survival data. } \item{std.err}{ the standard error of the survival value. } \item{conf.int}{ level of confidence for the confidence intervals of survival. } \item{lower}{ lower confidence limits for the curve. } \item{upper}{ upper confidence limits for the curve. } \item{strata}{ indicates stratification of curve estimation. If \code{strata} is not \code{NULL}, there are multiple curves in the result and the \code{surv}, \code{time}, \code{n.risk}, etc. vectors will contain multiple curves, pasted end to end. The levels of \code{strata} (a factor) are the labels for the curves. } \item{call}{ the statement used to create the \code{fit} object. } \item{na.action}{ same as for \code{fit}, if present. } \item{table}{ table of information that is returned from \code{print.survfit} function. } \item{type}{ type of data censoring. Passed through from the fit object. } } \section{Details}{ This routine has two uses: printing out a survival curve at specified time points (often yearly), or extracting the values at specified time points for further processing. In the first case we normally want \code{extend=FALSE}, i.e., don't print out data past the end of the curve. If the \code{times} option only contains values beyond the last point in the curve then there is nothing to print and an error message will result. For the second usage we almost always want \code{extend=TRUE}, so that the results will have a predictable length. The \code{survfit} object itself will have a row of information at each censoring or event time, it does not save information on each unique entry time. For printout at two time points t1, t2, this function will give the the number at risk at the smallest event times that are >= t1 and >= t2, respectively, the survival curve at the largest recorded times <= t1 and <= t2, and the number of events and censorings in the interval t1 < t <= t2. When the routine is called with counting process data many users are confused by counts that are too large. For example, \code{Surv(c(0,0, 5, 5), c(2, 3, 8, 10), c(1, 0, 1, 0))} followed by a request for the values at time 4. The \code{survfit} object has entries only at times 2, 3, 8, and 10; there are 2 subjects at risk at time 8, so that is what will be printed. } \seealso{ \code{\link{survfit}}, \code{\link{print.summary.survfit}} } \examples{ summary( survfit( Surv(futime, fustat)~1, data=ovarian)) summary( survfit( Surv(futime, fustat)~rx, data=ovarian)) } \keyword{survival} survival/man/model.matrix.coxph.Rd0000644000176200001440000000263313537676563016743 0ustar liggesusers\name{model.matrix.coxph} \Rdversion{1.1} \alias{model.matrix.coxph} \title{ Model.matrix method for coxph models } \description{ Reconstruct the model matrix for a cox model. } \usage{ \method{model.matrix}{coxph}(object, data=NULL, contrast.arg = object$contrasts, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{object}{the result of a \code{coxph} model} \item{data}{optional, a data frame from which to obtain the data} \item{contrast.arg}{optional, a contrasts object describing how factors should be coded} \item{\dots}{other possible argument to \code{model.frame}} } \details{ When there is a \code{data} argument this function differs from most of the other \code{model.matrix} methods in that the response variable for the original formula is \emph{not} required to be in the data. If the data frame contains a \code{terms} attribute then it is assumed to be the result of a call to \code{model.frame}, otherwise a call to \code{model.frame} is applied with the data as an argument. } \value{ The model matrix for the fit } \author{Terry Therneau} \seealso{\code{\link{model.matrix}}} \examples{ fit1 <- coxph(Surv(time, status) ~ age + factor(ph.ecog), data=lung) xfit <- model.matrix(fit1) fit2 <- coxph(Surv(time, status) ~ age + factor(ph.ecog), data=lung, x=TRUE) all.equal(model.matrix(fit1), fit2$x) } \keyword{ survival } survival/man/model.frame.coxph.Rd0000644000176200001440000000140613537676563016526 0ustar liggesusers\name{model.frame.coxph} \Rdversion{1.1} \alias{model.frame.coxph} \title{Model.frame method for coxph objects} \description{ Recreate the model frame of a coxph fit. } \usage{ \method{model.frame}{coxph}(formula, ...) } \arguments{ \item{formula}{the result of a \code{coxph} fit} \item{\dots}{other arguments to \code{model.frame}} } \details{ For details, see the manual page for the generic function. This function would rarely be called by a user, it is mostly used inside functions like \code{residual} that need to recreate the data set from a model in order to do further calculations. } \value{the model frame used in the original fit, or a parallel one for new data. } \author{Terry Therneau} \seealso{\code{\link{model.frame}}} \keyword{ survival } survival/man/logLik.coxph.Rd0000644000176200001440000000275213537676563015563 0ustar liggesusers\name{logLik.coxph} \alias{logLik.coxph} \alias{logLik.survreg} \title{logLik method for a Cox model} \description{The logLik function for survival models} \usage{ \method{logLik}{coxph}(object, ...) \method{logLik}{survreg}(object, ...) } \arguments{ \item{object}{the result of a \code{coxph} or \code{survreg} fit} \item{\dots}{optional arguments for other instances of the method} } \details{ The logLik function is used by summary functions in R such as \code{AIC}. For a Cox model, this method returns the partial likelihood. The number of degrees of freedom (df) used by the fit and the effective number of observations (nobs) are added as attributes. Per Raftery and others, the effective number of observations is the taken to be the number of events in the data set. For a \code{survreg} model the proper value for the effective number of observations is still an open question (at least to this author). For right censored data the approach of \code{logLik.coxph} is the possible the most sensible, but for interval censored observations the result is unclear. The code currently does not add a \emph{nobs} attribute. } \value{an object of class \code{logLik}} \references{ Robert E. Kass and Adrian E. Raftery (1995). "Bayes Factors". J. American Statistical Assoc. 90 (430): 791. Raftery A.E. (1995), "Bayesian Model Selection in Social Research", Sociological methodology, 111-196. } \seealso{\code{\link{logLik}}} \author{Terry Therneau} \keyword{ survival} survival/man/rotterdam.Rd0000644000176200001440000000624114102646306015175 0ustar liggesusers\name{rotterdam} \alias{rotterdam} \docType{data} \title{Breast cancer data set used in Royston and Altman (2013)} \description{ The \code{rotterdam} data set includes 2982 primary breast cancers patients whose data whose records were included in the Rotterdam tumor bank. } \usage{rotterdam data(cancer, package="survival") } \format{ A data frame with 2982 observations on the following 15 variables. \describe{ \item{\code{pid}}{patient identifier} \item{\code{year}}{year of surgery} \item{\code{age}}{age at surgery} \item{\code{meno}}{menopausal status (0= premenopausal, 1= postmenopausal)} \item{\code{size}}{tumor size, a factor with levels \code{<=20} \code{20-50} \code{>50}} \item{\code{grade}}{differentiation grade} \item{\code{nodes}}{number of positive lymph nodes} \item{\code{pgr}}{progesterone receptors (fmol/l)} \item{\code{er}}{estrogen receptors (fmol/l)} \item{\code{hormon}}{hormonal treatment (0=no, 1=yes)} \item{\code{chemo}}{chemotherapy} \item{\code{rtime}}{days to relapse or last follow-up} \item{\code{recur}}{0= no relapse, 1= relapse} \item{\code{dtime}}{days to death or last follow-up} \item{\code{death}}{0= alive, 1= dead} } } \details{ These data sets are used in the paper by Royston and Altman that is referenced below. The Rotterdam data is used to create a fitted model, and the GBSG data for validation of the model. The paper gives references for the data source. There are 43 subjects who have died without recurrence, but whose death time is greater than the censoring time for recurrence. A common way that this happens is that a death date is updated in the health record sometime after the research study ended, and said value is then picked up when a study data set is created. But it raises serious questions about censoring. For instance subject 40 is censored for recurrence at 4.2 years and died at 6.6 years; when creating the endpoint of recurrence free survival (earlier of recurrence or death), treating them as a death at 6.6 years implicitly assumes that they were recurrence free just before death. For this to be true we would have to assume that if they had progressed in the 2.4 year interval before death (off study), that this information would also have been noted in their general medical record, and would also be captured in the study data set. However, that may be unlikely. Death information is often in a centralized location in electronic health records, easily accessed by a programmer and merged with the study data, while recurrence may require manual review. How best to address this is an open issue. } \seealso{ \code{\link{gbsg}} } \references{ Patrick Royston and Douglas Altman, External validation of a Cox prognostic model: principles and methods. BMC Medical Research Methodology 2013, 13:33 } \examples{ status <- pmax(rotterdam$recur, rotterdam$death) rfstime <- with(rotterdam, ifelse(recur==1, rtime, dtime)) fit1 <- coxph(Surv(rfstime, status) ~ pspline(age) + meno + size + pspline(nodes) + er, data=rotterdam, subset = (nodes > 0)) # Royston and Altman used fractional polynomials for the nonlinear terms } \keyword{datasets} \keyword{survival} survival/man/residuals.survfit.Rd0000644000176200001440000000641113774234736016705 0ustar liggesusers\name{residuals.survfit} \alias{residuals.survfit} \title{IJ residuals from a survfit object.} \description{ Return infinitesimal jackknife residuals from a survfit object, for the survival, cumulative hazard, or restricted mean time in state (RMTS). } \usage{ \method{residuals}{survfit}(object, times, type="pstate", collapse, weighted=FALSE, method=1, ...) } \arguments{ \item{object}{a \code{survfit} object} \item{times}{a vector of times at which the residuals are desired} \item{type}{the type of residual, see below} \item{collapse}{add the residuals for all subjects in a cluster} \item{weighted}{weight the residuals by each observation's weight} \item{method}{controls a choice of algorithm. Current an internal debugging option.} \item{...}{arguments for other methods} } \details{ This function is designed to efficiently compute the leverage residuals at a small number of time points; a primary use is the creation of pseudo-values. If the residuals at all time points are needed, e.g. to compute a robust pointwise confidence interval for the survival curve, then this can be done more efficiently using the \code{influence} argument of the underlying \code{survfit} function. But be aware that such matrices can get very large. The residuals are the impact of each observation or cluster on the resulting probability in state curves at the given time points, the cumulative hazard curv\code{surv} at those time points, or the expected sojourn time in each state up to the given time points. For a simple Kaplan-Meier the \code{survfit} object contains only the probability in the "initial" state, i.e., the survival fraction. For the KM case the sojourn time, the expected amount of time spent in the initial state, up to the specified endpoint, is more commonly known as the restricted mean survival time (RMST). For a multistate model this same quantity is also referred to as the restricted mean time in state (RMTS). It can be computed as the area under the respective probability in state curve. The program allows any of \code{pstate}, \code{surv}, \code{cumhaz}, \code{chaz}, \code{sojourn}, \code{rmst}, \code{rmts} or \code{auc} for the type argument, ignoring upper/lowercase, so users can choose whichever abbreviation they like best. When \code{collapse=TRUE} the result has the cluster identifier (which defaults to the \code{id} variable) as the dimname for the first dimension. If the \code{fit} object contains more than one curve, and the same identifier is reused in two different curves this approach does not work and the routine will stop with an error. In principle this is not necessary, e.g., the result could contain two rows with the same label, showing the separate effect on each curve, but this was deemed too confusing. } \value{A matrix or array with one row per observation or cluster, and one column for each value in \code{times}. For a multi-state model the three dimensions are observation, time and state. For cumulative hazard, the last dimension is the set of transitions. (A competing risks model for instance has 3 states and 2 transitions.) } \seealso{\code{\link{survfit}}, \code{\link{survfit.formula}} } \examples{ fit <- survfit(Surv(time, status) ~ x, aml) resid(fit, times=c(24, 48), type="RMTS") } % \keyword{ survival } survival/man/survfit.object.Rd0000644000176200001440000001341413775316525016157 0ustar liggesusers\name{survfit.object} \alias{survfit.object} \alias{survfitms.object} \title{ Survival Curve Object } \description{ This class of objects is returned by the \code{survfit} class of functions to represent a fitted survival curve. For a multi-state model the object has class \code{c('survfitms', 'survfit')}. Objects of this class have methods for the functions \code{print}, \code{summary}, \code{plot}, \code{points} and \code{lines}. The \code{\link{print.survfit}} method does more computation than is typical for a print method and is documented on a separate page. } \section{Structure}{ The following components must be included in a legitimate \code{survfit} or \code{survfitms} object. } \arguments{ \item{n}{ total number of subjects in each curve. } \item{time}{ the time points at which the curve has a step. } \item{n.risk}{ the number of subjects at risk at t. } \item{n.event}{ the number of events that occur at time t. } \item{n.enter}{ for counting process data only, the number of subjects that enter at time t. } \item{n.censor}{ for counting process data only, the number of subjects who exit the risk set, without an event, at time t. (For right censored data, this number can be computed from the successive values of the number at risk). } \item{surv}{ the estimate of survival at time t+0. This may be a vector or a matrix. The latter occurs when a set of survival curves is created from a single Cox model, in which case there is one column for each covariate set. } \item{pstate}{ a multi-state survival will have the \code{pstate} component instead of \code{surv}. It will be a matrix containing the estimated probability of each state at each time, one column per state. } \item{std.err}{ for a survival curve this contains standard error of the cumulative hazard or -log(survival), for a multi-state curve it contains the standard error of prev. This difference is a reflection of the fact that each is the natural calculation for that case. } \item{cumhaz hazard}{optional. Contains the cumulative hazard for each possible transtion. } \item{strata}{ if there are multiple curves, this component gives the number of elements of the \code{time} vector corresponding to the first curve, the second curve, and so on. The names of the elements are labels for the curves. } \item{upper}{optional upper confidence limit for the survival curve or pstate } \item{lower}{options lower confidence limit for the survival curve or pstate } \item{start.time}{optional, the starting time for the curve if other than 0} \item{p0, sp0}{for a multistate object, the distribution of starting states. If the curve has a strata dimension, this will be a matrix one row per stratum. The \code{sp0} element has the standard error of p0, if p0 was estimated. } \item{newdata}{for survival curves from a fitted model, this contains the covariate values for the curves } \item{n.all}{the total number of observations that were available For counting process data, and any time that the \code{start.time} argument was used, not all may have been used in creating the curve, in which case this value will be larger than \code{n} above. The \code{print} and \code{plot} routines in the package do no use this value, it is for information only. } \item{conf.type}{ the approximation used to compute the confidence limits. } \item{conf.int}{ the level of the confidence limits, e.g. 90 or 95\%. } \item{transitions}{for multi-state data, the total number of transitions of each type.} \item{na.action}{ the returned value from the na.action function, if any. It will be used in the printout of the curve, e.g., the number of observations deleted due to missing values. } \item{call}{ an image of the call that produced the object. } \item{type}{ type of survival censoring. } \item{influence.p, influence.c}{optional influence matrices for the \code{pstate} (or \code{surv}) and for the \code{cumhaz} estimates. A list with one element per stratum, each element of the list is an array indexed by subject, time, state. } \item{version}{the version of the object. Will be missing, 2, or 3} } \section{Subscripts}{ Survfit objects can be subscripted. This is often used to plot a subset of the curves, for instance. From the user's point of view the \code{survfit} object appears to be a vector, matrix, or array of curves. The first dimension is always the underlying number of curves or ``strata''; for multi-state models the state is always the last dimension. Predicted curves from a Cox model can have a second dimension which is the number of different covariate prediction vectors. } \section{Details}{ The \code{survfit} object has evolved over time: when first created there was no thought of multi-state models for instance. This evolution has almost entirely been accomplished by the addition of new elements. One change in survival version 3 is the addition of a \code{survfitconf} routine which will compute confidence intervals for a \code{survfit} object. This allows the computation of CI intervals to be deferred until later, if desired, rather than making them a permanent part of the object. Later iterations of the base routines may omit the confidence intervals. The survfit object starts at the first observation time, but survival curves are normally plotted from time 0. A helper routine \code{survfit0} can be used to add this first time point and align the data. } \seealso{ \code{\link{plot.survfit}}, \code{\link{summary.survfit}}, \code{\link{print.survfit}}, \code{\link{survfit}}, \code{\link{survfit0}} } \keyword{survival} survival/man/residuals.coxph.Rd0000644000176200001440000000645413761573633016331 0ustar liggesusers\name{residuals.coxph} \alias{residuals.coxph.penal} \alias{residuals.coxph.null} \alias{residuals.coxph} \alias{residuals.coxphms} \title{Calculate Residuals for a `coxph' Fit } \description{ Calculates martingale, deviance, score or Schoenfeld residuals for a Cox proportional hazards model. } \usage{ \method{residuals}{coxph}(object, type=c("martingale", "deviance", "score", "schoenfeld", "dfbeta", "dfbetas", "scaledsch","partial"), collapse=FALSE, weighted= (type \%in\% c("dfbeta", "dfbetas")), ...) \method{residuals}{coxphms}(object, type=c("martingale", "score", "schoenfeld", "dfbeta", "dfbetas", "scaledsch"), collapse=FALSE, weighted= FALSE, ...) \method{residuals}{coxph.null}(object, type=c("martingale", "deviance","score","schoenfeld"), collapse=FALSE, weighted= FALSE, ...) } \arguments{ \item{object}{ an object inheriting from class \code{coxph}, representing a fitted Cox regression model. Typically this is the output from the \code{coxph} function. } \item{type}{ character string indicating the type of residual desired. Possible values are \code{"martingale"}, \code{"deviance"}, \code{"score"}, \code{"schoenfeld"}, "dfbeta"', \code{"dfbetas"}, \code{"scaledsch"} and \code{"partial"}. Only enough of the string to determine a unique match is required. } \item{collapse}{ vector indicating which rows to collapse (sum) over. In time-dependent models more than one row data can pertain to a single individual. If there were 4 individuals represented by 3, 1, 2 and 4 rows of data respectively, then \code{collapse=c(1,1,1, 2, 3,3, 4,4,4,4)} could be used to obtain per subject rather than per observation residuals. } \item{weighted}{ if \code{TRUE} and the model was fit with case weights, then the weighted residuals are returned. }\item{...}{other unused arguments}} \value{ For martingale and deviance residuals, the returned object is a vector with one element for each subject (without \code{collapse}). For score residuals it is a matrix with one row per subject and one column per variable. The row order will match the input data for the original fit. For Schoenfeld residuals, the returned object is a matrix with one row for each event and one column per variable. The rows are ordered by time within strata, and an attribute \code{strata} is attached that contains the number of observations in each strata. The scaled Schoenfeld residuals are used in the \code{cox.zph} function. The score residuals are each individual's contribution to the score vector. Two transformations of this are often more useful: \code{dfbeta} is the approximate change in the coefficient vector if that observation were dropped, and \code{dfbetas} is the approximate change in the coefficients, scaled by the standard error for the coefficients. } \section{NOTE}{ For deviance residuals, the status variable may need to be reconstructed. For score and Schoenfeld residuals, the X matrix will need to be reconstructed. } \references{ T. Therneau, P. Grambsch, and T. Fleming. "Martingale based residuals for survival models", \emph{Biometrika}, March 1990. } \seealso{ \code{\link{coxph}}} \examples{ fit <- coxph(Surv(start, stop, event) ~ (age + surgery)* transplant, data=heart) mresid <- resid(fit, collapse=heart$id) } \keyword{survival} % Converted by Sd2Rd version 0.3-2. survival/man/concordance.Rd0000644000176200001440000001502414027425473015457 0ustar liggesusers\name{concordance} \alias{concordance} \alias{concordance.coxph} \alias{concordance.formula} \alias{concordance.lm} \alias{concordance.survreg} \title{Compute the concordance statistic for data or a model} \description{ The concordance statistic compute the agreement between an observed response and a predictor. It is closely related to Kendall's tau-a and tau-b, Goodman's gamma, and Somers' d, all of which can also be calculated from the results of this function. } \usage{ concordance(object, \ldots) \method{concordance}{formula}(object, data, weights, subset, na.action, cluster, ymin, ymax, timewt= c("n", "S", "S/G", "n/G", "n/G2", "I"), influence=0, ranks = FALSE, reverse=FALSE, timefix=TRUE, keepstrata=10, \ldots) \method{concordance}{lm}(object, \ldots, newdata, cluster, ymin, ymax, influence=0, ranks=FALSE, timefix=TRUE, keepstrata=10) \method{concordance}{coxph}(object, \ldots, newdata, cluster, ymin, ymax, timewt= c("n", "S", "S/G", "n/G", "n/G2", "I"), influence=0, ranks=FALSE, timefix=FALSE, keepstrata=10) \method{concordance}{survreg}(object, \ldots, newdata, cluster, ymin, ymax, timewt= c("n", "S", "S/G", "n/G", "n/G2", "I"), influence=0, ranks=FALSE, timefix=FALSE, keepstrata=10) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{object}{a fitted model or a formula. The formula should be of the form \code{y ~x} or \code{y ~ x + strata(z)} with a single numeric or survival response and a single predictor. Counts of concordant, discordant and tied pairs are computed separately per stratum, and then added. } \item{data}{ a data.frame in which to interpret the variables named in the \code{formula}, or in the \code{subset} and the \code{weights} argument. Only applicable if \code{object} is a formula. } \item{weights}{ optional vector of case weights. Only applicable if \code{object} is a formula. } \item{subset}{ expression indicating which subset of the rows of data should be used in the fit. Only applicable if \code{object} is a formula. } \item{na.action}{ a missing-data filter function. This is applied to the model.frame after any subset argument has been used. Default is \code{options()\$na.action}. Only applicable if \code{object} is a formula. } \item{\ldots}{multiple fitted models are allowed. Only applicable if \code{object} is a model object.} \item{newdata}{optional, a new data frame in which to evaluate (but not refit) the models} \item{cluster}{optional grouping vector for calculating the robust variance} \item{ymin, ymax}{compute the concordance over the restricted range ymin <= y <= ymax. (For survival data this is a time range.) } \item{timewt}{the weighting to be applied. The overall statistic is a weighted mean over event times. } \item{influence}{1= return the dfbeta vector, 2= return the full influence matrix, 3 = return both } \item{ranks}{if TRUE, return a data frame containing the individual ranks that make up the overall score. } \item{reverse}{if TRUE then assume that larger \code{x} values predict smaller response values \code{y}; a proportional hazards model is the common example of this. } \item{timefix}{if the response is a Surv object, correct for possible rounding error; otherwise this argument has no effect. See the vignette on tied times for more explanation. For the coxph and survreg methods this issue will have already been addressed in the parent routine, so should not be revisited. } \item{keepstrata}{either TRUE, FALSE, or an integer value. Computations are always done within stratum, then added. If the total number of strata greater than \code{keepstrata}, or \code{keepstrata=FALSE}, those subtotals are not kept in the output. } } \details{ At each event time, compute the rank of the subject who had the event as compared to all others with a longer survival, where the rank is value between 0 and 1. The concordance is a weighted mean of these values, determined by the \code{timewt} option. For uncensored data each unique response value is compared to all those which are larger. Using the default value for \code{timewt} gives the area under the receiver operating curve (AUC) for a binary response, and (d+1)/2 when y is continuous, where d is Somers' d. For a survival time, \code{timewt} of n gives Harrell's c-statistic, which is closely related to the Gehan-Wilcoxon test, S corresponds to the Peto-Wilcoxon, n/G2 is the weighted advocated by Umo, and S/G the weighting proposed by Schemper. When the number of strata is very large, such as in a conditional logistic regression for instance (\code{clogit} function), a much faster computation is available when the individual strata results are not retained. In the more general case the \code{keepstrata = 10} default simply keeps the printout managable. } \value{ An object of class \code{concordance} containing the following components: \item{concordance}{the estimated concordance value or values} \item{count}{a vector containing the number of concordant pairs, discordant, tied on x but not y, tied on y but not x, and tied on both x and y} \item{n}{the number of observations} \item{var}{a vector containing the estimated variance of the concordance based on the infinitesimal jackknife (IJ) method. If there are multiple models it contains the estimtated variance/covariance matrix.} \item{cvar}{a vector containing the estimated variance(s) of the concordance values, based on the variance formula for the associated score test from a proportional hazards model. (This was the primary variance used in the \code{survConcordance} function.)} \item{dfbeta}{optional, the vector of leverage estimates for the concordance} \item{influence}{optional, the matrix of leverage values for each of the counts, one row per observation} \item{ranks}{optional, a data frame containing the Somers' d rank at each event time, along with the time weight, case weight of the observation with an event, and variance (contribution to the proportional hazards model information matrix). A weighted mean of the ranks equals Somer's d.} } \author{Terry Therneau} \seealso{\code{\link{coxph}}} \examples{ fit1 <- coxph(Surv(ptime, pstat) ~ age + sex + mspike, mgus2) concordance(fit1, timewt="n") # logistic regression fit2 <- glm(pstat ~ age + sex + mspike, binomial, data= mgus2) concordance(fit2) # equal to the AUC } \keyword{ survival } survival/man/concordancefit.Rd0000644000176200001440000000303714073031214016146 0ustar liggesusers\name{concordancefit} \alias{concordancefit} \title{Compute the concordance} \description{ This is the working routine behind the \code{concordance} function. It is not meant to be called by users, but is available for other packages to use. Input arguments, for instance, are assumed to all be the correct length and type, and missing values are not allowed: the calling routine is responsible for these things. } \usage{ concordancefit(y, x, strata, weights, ymin = NULL, ymax = NULL, timewt = c("n", "S", "S/G", "n/G", "n/G2", "I"), cluster, influence =0, ranks = FALSE, reverse = FALSE, timefix = TRUE, keepstrata=10, robustse = TRUE) } \arguments{ \item{y}{the response. It can be numeric, factor, or a Surv object} \item{x}{the predictor, a numeric vector} \item{strata}{optional numeric vector that stratifies the data} \item{weights}{options vector of case weights} \item{ymin, ymax}{restrict the comparison to response values in this range} \item{timewt}{the time weighting to be used} \item{cluster, influence,ranks, reverse, timefix}{see the help for the \code{concordance} function} \item{keepstrata}{either TRUE, FALSE, or an integer value. Computations are always done within stratum, then added. If the total number of strata greater than \code{keepstrata}, or \code{keepstrata=FALSE}, those subtotals are not kept in the output. } \item{robustse}{comput the robust standard error} } \value{a list containing the results} \author{ Terry Therneau} \seealso{ \code{\link{concordance}}} \keyword{ survival } survival/man/transplant.Rd0000644000176200001440000000601014013474002015345 0ustar liggesusers\name{transplant} \alias{transplant} \docType{data} \title{Liver transplant waiting list} \description{ Subjects on a liver transplant waiting list from 1990-1999, and their disposition: received a transplant, died while waiting, withdrew from the list, or censored. } \usage{transplant data(transplant, package="survival") } \format{ A data frame with 815 (transplant) observations on the following 6 variables. \describe{ \item{\code{age}}{age at addition to the waiting list} \item{\code{sex}}{\code{m} or \code{f}} \item{\code{abo}}{blood type: \code{A}, \code{B}, \code{AB} or \code{O}} \item{\code{year}}{year in which they entered the waiting list} \item{\code{futime}}{time from entry to final disposition} \item{\code{event}}{final disposition: \code{censored}, \code{death}, \code{ltx} or \code{withdraw}} } } \details{ This represents the transplant experience in a particular region, over a time period in which liver transplant became much more widely recognized as a viable treatment modality. The number of liver transplants rises over the period, but the number of subjects added to the liver transplant waiting list grew much faster. Important questions addressed by the data are the change in waiting time, who waits, and whether there was an consequent increase in deaths while on the list. Blood type is an important consideration. Donor livers from subjects with blood type O can be used by patients with A, B, AB or 0 blood types, whereas an AB liver can only be used by an AB recipient. Thus type O subjects on the waiting list are at a disadvantage, since the pool of competitors is larger for type O donor livers. This data is of historical interest and provides a useful example of competing risks, but it has little relevance to current practice. Liver allocation policies have evolved and now depend directly on each individual patient's risk and need, assessments of which are regularly updated while a patient is on the waiting list. The overall organ shortage remains acute, however. The \code{transplant} data set was a version used early in the analysis, \code{transplant2} has several additions and corrections, and was the final data set and matches the paper. } \examples{ #since event is a factor, survfit creates competing risk curves pfit <- survfit(Surv(futime, event) ~ abo, transplant) pfit[,2] #time to liver transplant, by blood type plot(pfit[,2], mark.time=FALSE, col=1:4, lwd=2, xmax=735, xscale=30.5, xlab="Months", ylab="Fraction transplanted", xaxt = 'n') temp <- c(0, 6, 12, 18, 24) axis(1, temp*30.5, temp) legend(450, .35, levels(transplant$abo), lty=1, col=1:4, lwd=2) # competing risks for type O plot(pfit[4,], xscale=30.5, xmax=735, col=1:3, lwd=2) legend(450, .4, c("Death", "Transpant", "Withdrawal"), col=1:3, lwd=2) } \references{ Kim WR, Therneau TM, Benson JT, Kremers WK, Rosen CB, Gores GJ, Dickson ER. Deaths on the liver transplant waiting list: An analysis of competing risks. Hepatology 2006 Feb; 43(2):345-51. } \keyword{datasets} survival/man/ratetable.Rd0000644000176200001440000000674313537676563015171 0ustar liggesusers\name{ratetable} \alias{ratetable} \alias{[.ratetable} \alias{print.ratetable} \alias{summary.ratetable} \title{ Rate table structure } \description{ Description of the rate tables used by expected survival routines. } \details{ A rate table contains event rates per unit time for some particular endpoint. Death rates are the most common use, the \code{survexp.us} table, for instance, contains death rates for the United States by year of age, sex, and calendar year. A rate table is structured as a multi-way array with the following attributes: \describe{ \item{dim}{the dimensions of the array} \item{dimnames}{a named list of dimnames. The names are used to match user data to the dimensions, e.g., see the \code{rmap} argument in the \code{pyears} example. If a dimension is categorical, such as \code{sex} in \code{survexp.us}, then the dimname itself is matched against user's data values. The matching ignores case and allows abbreviations, e.g., "M", "Male", and "m" all successfully match the \code{survexp.us} dimname of \code{sex=c("male", "female")}.} \item{type}{a vector giving the type of each dimension, which will be 1= categorical, 2= continuous, 3= date, 4= calendar year of a US rate table. If \code{type} is 3 or 4, then the corresponding cutpoints must be one of the calendar date types: Date, POSIXt, date, or chron. This allows the code to properly match user data to the ratetable. (The published US decennial rate tables' definition is that a subject does not begin to experience a new years' death rate on Jan 1, but rather on their next birthday. The actual impact of this delay on any given subjects' calculation is neglible, but the code has always tried to be correct.) } \item{cutpoints}{a list with one elment per dimension. If \code{type=1} then the corresponding list element should be NULL, otherwise it should be a vector of length \code{dim[i]} containing the starting point of the interval to which the corresponding row/col of the array applies. Cutpoints must be in the same units as the underlying table, e.g., the \code{survexp.us} table contains death rates per day, so the \code{age} cutpoint vector contains age in days while \code{year} contains a vector of Dates. Cutpoints do not need to be evenly spaced: the \code{survexp.us} table, for instance, originally had age divided up as 0-1 days, 1-7 days, 7-28 days, 28 days - 1 year, 2, 3, \ldots 119 years. (Changes in the source of the tables made it difficult to continue splitting out the first year.)} \item{summary}{an optional summarization function. If present, it will be called with a numeric matrix that has one column per dimension and one row per observation. The function returns a character string giving a summary of the data. This is used by some routines to print an informative message, and provides one way to inform users of a data mistake, e.g., if the printout states that all subjects are between 0.14 and 0.23 years old it is likely that the user's age variable was in years when it should have been in days. } \item{dimid}{optional attribute containing the names of the dimnames. If the dimnames list itself has names, this attribute will be ignored.} }} \seealso{\code{\link{survexp}}, \code{\link{pyears}}, \code{\link{survexp.us}}} \keyword{survival} survival/man/statefig.Rd0000644000176200001440000001102113537676563015015 0ustar liggesusers\name{statefig} \alias{statefig} \title{Draw a state space figure.} \description{ For multi-state survival models it is useful to have a figure that shows the states and the possible transitions between them. This function creates a simple "box and arrows" figure. It's goal was simplicity. } \usage{ statefig(layout, connect, margin = 0.03, box = TRUE, cex = 1, col = 1, lwd=1, lty=1, bcol=col, acol=col, alwd=lwd, alty=lty, offset=0) } \arguments{ \item{layout}{describes the layout of the boxes on the page. See the detailed description below. } \item{connect}{a square matrix with one row for each state. If \code{connect[i,j] !=0} then an arrow is drawn from state i to state j. The row names of the matrix are used as the labels for the states. } \item{margin}{the fraction of white space between the label and the surrounding box, and between the box and the arrows, as a function of the plot region size. } \item{box}{should boxes be drawn? TRUE or FALSE. } \item{cex, col, lty, lwd}{default graphical parameters used for the text and boxes. The last 3 can be a vector of values. } \item{bcol}{color for the box, if it differs from that used for the text.} \item{acol, alwd, alty}{color, line type and line width for the arrows.} \item{offset}{used to slight offset the arrows between two boxes x and y if there is a transition in both directions. The default of 0 leads to a double headed arrow in this case -- to arrows are drawn but they coincide. A positive value causes each arrow to shift to the left, from the view of someone standing at the foot of a arrow and looking towards the arrowhead, a negative offset shifts to the right. A value of 1 corresponds to the size of the plotting region.} } \details{ The arguments for color, line type and line width can all be vectors, in which case they are recycled as needed. Boxes and text are drawn in the order of the rownames of \code{connect}, and arrows are drawn in the usual R matrix order. The \code{layout} argument is normally a vector of integers, e.g., the vector (1, 3, 2) describes a layout with 3 columns. The first has a single state, the second column has 3 states and the third has 2. The coordinates of the plotting region are 0 to 1 for both x and y. Within a column the centers of the boxes are evenly spaced, with 1/2 a space between the boxes and the margin, e.g., 4 boxes would be at 1/8, 3/8, 5/8 and 7/8. If \code{layout} were a 1 column matrix with values of (1, 3, 2) then the layout will have three rows with 1, 3, and 2 boxes per row, respectively. Alternatively, the user can supply a 2 column matrix that directly gives the centers. The values of the connect matrix should be 0 for pairs of states that do not have a transition and values between 0 and 2 for those that do. States are connected by an arc that passes through the centers of the two boxes and a third point that is between them. Specifically, consider a line segment joining the two centers and erect a second segment at right angles to the midpoint of length d times the distance from center to midpoint. The arc passes through this point. A value of d=0 gives a straight line, d=1 a right hand half circle centered on the midpoint and d= -1 a left hand half circle. The \code{connect} matrix contains values of d+1 with -1 < d < 1. The connecting arrow are drawn from (center of box 1 + offset) to (center of box 2 + offset), where the the amount of offset (white space) is determined by the \code{box} and \code{margin} parameters. If a pair of states are too close together this can result in an arrow that points the wrong way. } \value{a matrix containing the centers of the boxes, with the invisible attribute set.} \author{Terry Therneau} \note{ The goal of this function is to make ``good enough'' figures as simply as possible, and thereby to encourage users to draw them. The \code{layout} argument was inspired by the \code{diagram} package, which can draw more complex and well decorated figures, e.g., many different shapes, shading, multiple types of connecting lines, etc., but at the price of greater complexity. Because curved lines are drawn as a set of short line segments, line types have almost no effect for that case. } \examples{ # Draw a simple competing risks figure states <- c("Entry", "Complete response", "Relapse", "Death") connect <- matrix(0, 4, 4, dimnames=list(states, states)) connect[1, -1] <- c(1.1, 1, 0.9) statefig(c(1, 3), connect) } \keyword{survival} \keyword{hplot} survival/man/heart.Rd0000644000176200001440000000264214013462777014311 0ustar liggesusers\name{heart} \docType{data} \alias{jasa1} \alias{jasa} \alias{heart} \title{Stanford Heart Transplant data} \description{Survival of patients on the waiting list for the Stanford heart transplant program.} \usage{heart data(heart, package="survival")} \format{ jasa: original data \tabular{ll}{ birth.dt:\tab birth date \cr accept.dt:\tab acceptance into program \cr tx.date:\tab transplant date \cr fu.date:\tab end of followup \cr fustat:\tab dead or alive \cr surgery:\tab prior bypass surgery\cr age: \tab age (in years)\cr futime:\tab followup time\cr wait.time:\tab time before transplant\cr transplant:\tab transplant indicator\cr mismatch:\tab mismatch score\cr hla.a2:\tab particular type of mismatch\cr mscore:\tab another mismatch score\cr reject:\tab rejection occurred\cr } jasa1, heart: processed data \tabular{ll}{ start, stop, event: \tab Entry and exit time and status for this interval of time\cr age:\tab age-48 years\cr year:\tab year of acceptance (in years after 1 Nov 1967)\cr surgery:\tab prior bypass surgery 1=yes\cr transplant: \tab received transplant 1=yes\cr id:\tab patient id\cr } } \seealso{\code{\link{stanford2}}} \source{ J Crowley and M Hu (1977), Covariance analysis of heart transplant survival data. \emph{Journal of the American Statistical Association}, \bold{72}, 27--36. } \keyword{datasets} \keyword{survival} survival/man/neardate.Rd0000644000176200001440000000774113537676563015010 0ustar liggesusers\name{neardate} \alias{neardate} \title{ Find the index of the closest value in data set 2, for each entry in data set one. } \description{ A common task in medical work is to find the closest lab value to some index date, for each subject. } \usage{ neardate(id1, id2, y1, y2, best = c("after", "prior"), nomatch = NA_integer_) } \arguments{ \item{id1}{vector of subject identifiers for the index group} \item{id2}{vector of identifiers for the reference group} \item{y1}{normally a vector of dates for the index group, but any orderable data type is allowed} \item{y2}{reference set of dates} \item{best}{if \code{best='prior'} find the index of the first y2 value less than or equal to the target y1 value, for each subject. If \code{best='after'} find the first y2 value which is greater than or equal to the target y1 value, for each subject.} \item{nomatch}{the value to return for items without a match} } \details{ This routine is closely related to \code{match} and to \code{findInterval}, the first of which finds exact matches and the second closest matches. This finds the closest matching date within sets of exactly matching identifiers. Closest date matching is often needed in clinical studies. For example data set 1 might contain the subject identifier and the date of some procedure and data set set 2 has the dates and values for laboratory tests, and the query is to find the first test value after the intervention but no closer than 7 days. The \code{id1} and \code{id2} arguments are similar to \code{match} in that we are searching for instances of \code{id1} that will be found in \code{id2}, and the result is the same length as \code{id1}. However, instead of returning the first match with \code{id2} this routine returns the one that best matches with respect to \code{y1}. The \code{y1} and \code{y2} arguments need not be dates, the function works for any data type such that the expression \code{c(y1, y2)} gives a sensible, sortable result. Be careful about matching Date and DateTime values and the impact of time zones, however, see \code{\link{as.POSIXct}}. If \code{y1} and \code{y2} are not of the same class the user is on their own. Since there exist pairs of unmatched data types where the result could be sensible, the routine will in this case proceed under the assumption that "the user knows what they are doing". Caveat emptor. } \value{the index of the matching observations in the second data set, or the \code{nomatch} value for no successful match} \author{Terry Therneau} \examples{ data1 <- data.frame(id = 1:10, entry.dt = as.Date(paste("2011", 1:10, "5", sep='-'))) temp1 <- c(1,4,5,1,3,6,9, 2,7,8,12,4,6,7,10,12,3) data2 <- data.frame(id = c(1,1,1,2,2,4,4,5,5,5,6,8,8,9,10,10,12), lab.dt = as.Date(paste("2011", temp1, "1", sep='-')), chol = round(runif(17, 130, 280))) #first cholesterol on or after enrollment indx1 <- neardate(data1$id, data2$id, data1$entry.dt, data2$lab.dt) data2[indx1, "chol"] # Closest one, either before or after. # indx2 <- neardate(data1$id, data2$id, data1$entry.dt, data2$lab.dt, best="prior") ifelse(is.na(indx1), indx2, # none after, take before ifelse(is.na(indx2), indx1, #none before ifelse(abs(data2$lab.dt[indx2]- data1$entry.dt) < abs(data2$lab.dt[indx1]- data1$entry.dt), indx2, indx1))) # closest date before or after, but no more than 21 days prior to index indx2 <- ifelse((data1$entry.dt - data2$lab.dt[indx2]) >21, NA, indx2) ifelse(is.na(indx1), indx2, # none after, take before ifelse(is.na(indx2), indx1, #none before ifelse(abs(data2$lab.dt[indx2]- data1$entry.dt) < abs(data2$lab.dt[indx1]- data1$entry.dt), indx2, indx1))) } \seealso{\code{\link{match}}, \code{\link{findInterval}}} \keyword{ manip } \keyword{ utilities } survival/man/coxph.object.Rd0000644000176200001440000000716314012033721015555 0ustar liggesusers\name{coxph.object} \alias{coxph.object} \alias{extractAIC.coxph.penal} \alias{print.coxph} \title{ Proportional Hazards Regression Object } \description{ This class of objects is returned by the \code{coxph} class of functions to represent a fitted proportional hazards model. Objects of this class have methods for the functions \code{print}, \code{summary}, \code{residuals}, \code{predict} and \code{survfit}. } \section{Components}{ The following components must be included in a legitimate \code{coxph} object. } \arguments{ \item{coefficients}{ the vector of coefficients. If the model is over-determined there will be missing values in the vector corresponding to the redundant columns in the model matrix. } \item{var}{ the variance matrix of the coefficients. Rows and columns corresponding to any missing coefficients are set to zero. } \item{naive.var}{ this component will be present only if the \code{robust} option was true. If so, the \code{var} component will contain the robust estimate of variance, and this component will contain the ordinary estimate. } \item{loglik}{ a vector of length 2 containing the log-likelihood with the initial values and with the final values of the coefficients. } \item{score}{ value of the efficient score test, at the initial value of the coefficients. } \item{rscore}{ the robust log-rank statistic, if a robust variance was requested. } \item{wald.test}{ the Wald test of whether the final coefficients differ from the initial values. } \item{iter}{ number of iterations used. } \item{linear.predictors}{ the vector of linear predictors, one per subject. Note that this vector has been centered, see \code{predict.coxph} for more details. } \item{residuals}{ the martingale residuals. } \item{means}{ vector of column means of the X matrix. Subsequent survival curves are adjusted to this value. } \item{n}{ the number of observations used in the fit. } \item{nevent}{ the number of events (usually deaths) used in the fit. } \item{concordance}{a vector of length 6, containing the number of pairs that are concordant, discordant, tied on x, tied on y, and tied on both, followed by the standard error of the concordance statistic.} \item{first}{the first derivative vector at the solution.} \item{weights}{ the vector of case weights, if one was used. } \item{method}{ the method used for handling tied survival times. } \item{na.action}{ the na.action attribute, if any, that was returned by the \code{na.action} routine. } \item{timefix}{the value of the timefix option used in the fit} \item{cmap}{the coefficient map, present for multi-state coxph fits. There a column for each transition and a row for each coefficient, the value maps that transition/coefficient pair to a position in the coefficient vector. If a particular covariate is not used by the transition the matrix will contain a zero, if two transitions share a coefficient the matrix will contain repeats.} \item{stratum_map}{stratum mapping, present for multi-state coxph fits. The row labeled `(Baseline)' identifies transitions that do or do not share a baseline stratum. Further rows correspond to strata() terms in the model, each of which may apply to some transitions and not others. } \item{...}{ The object will also contain the following, for documentation see the \code{lm} object: \code{terms}, \code{assign}, \code{formula}, \code{call}, and, optionally, \code{x}, \code{y}, and/or \code{frame}. } } \seealso{ \code{\link{coxph}}, \code{\link{coxph.detail}}, \code{\link{cox.zph}}, \code{\link{residuals.coxph}}, \code{\link{survfit}}, \code{\link{survreg}}. } \keyword{survival} survival/man/survfit.coxph.Rd0000644000176200001440000002154114041423076016015 0ustar liggesusers\name{survfit.coxph} \alias{survfit.coxph} \title{ Compute a Survival Curve from a Cox model } \description{ Computes the predicted survivor function for a Cox proportional hazards model. } \usage{ \method{survfit}{coxph}(formula, newdata, se.fit=TRUE, conf.int=.95, individual=FALSE, stype=2, ctype, conf.type=c("log","log-log","plain","none", "logit", "arcsin"), censor=TRUE, start.time, id, influence=FALSE, na.action=na.pass, type, ...) } \arguments{ \item{formula}{ A \code{coxph} object. } \item{newdata}{ a data frame with the same variable names as those that appear in the \code{coxph} formula. It is also valid to use a vector, if the data frame would consist of a single row. The curve(s) produced will be representative of a cohort whose covariates correspond to the values in \code{newdata}. Default is the mean of the covariates used in the \code{coxph} fit. } \item{se.fit}{ a logical value indicating whether standard errors should be computed. Default is \code{TRUE}. } \item{conf.int}{ the level for a two-sided confidence interval on the survival curve(s). Default is 0.95. } \item{individual}{depricated argument, replaced by the general \code{id}} \item{stype}{computation of the survival curve, 1=direct, 2= exponenial of the cumulative hazard.} \item{ctype}{whether the cumulative hazard computation should have a correction for ties, 1=no, 2=yes.} \item{conf.type}{ One of \code{"none"}, \code{"plain"}, \code{"log"} (the default), \code{"log-log"} or \code{"logit"}. Only enough of the string to uniquely identify it is necessary. The first option causes confidence intervals not to be generated. The second causes the standard intervals \code{curve +- k *se(curve)}, where k is determined from \code{conf.int}. The log option calculates intervals based on the cumulative hazard or log(survival). The log-log option uses the log hazard or log(-log(survival)), and the logit log(survival/(1-survival)). } \item{censor}{if FALSE time points at which there are no events (only censoring) are not included in the result.} \item{id}{optional variable name of subject identifiers. If this is present, it will be search for in the \code{newdata} data frame. Each group of rows in \code{newdata} with the same subject id represents the covariate path through time of a single subject, and the result will contain one curve per subject. If the \code{coxph} fit had strata then that must also be specified in \code{newdata}. If \code{newid} is not present, then each individual row of \code{newdata} is presumed to represent a distinct subject.} \item{start.time}{optional starting time, a single numeric value. If present the returned curve contains survival after \code{start.time} conditional on surviving to \code{start.time}. } \item{influence}{option to return the influence values} \item{na.action}{the na.action to be used on the newdata argument} \item{type}{older argument that encompassed \code{stype} and \code{ctype}, now depricated} \item{\dots}{for future methods} } \value{ an object of class \code{"survfit"}. See \code{survfit.object} for details. Methods defined for survfit objects are \code{print}, \code{plot}, \code{lines}, and \code{points}. } \details{ This routine produces Pr(state) curves based on a \code{coxph} model fit. For single state models it produces the single curve for S(t) = Pr(remain in initial state at time t), known as the survival curve; for multi-state models a matrix giving probabilities for all states. The \code{stype} argument states the type of estimate, and defaults to the exponential of the cumulative hazard, better known as the Breslow estimate. For a multi-state Cox model this involves the exponential of a matrix. The argument \code{stype=1} uses a non-exponential or `direct' estimate. For a single endpoint coxph model the code evaluates the Kalbfleich-Prentice estimate, and for a multi-state model it uses an analog of the Aalen-Johansen estimator. The latter approach is the default in the \code{mstate} package. The \code{ctype} option affects the estimated cumulative hazard, and if \code{stype=2} the estimated P(state) curves as well. If not present it is chosen so as to be concordant with the \code{ties} option in the \code{coxph} call. (For multistate \code{coxphms} objects only \code{ctype=1} is currently implemented.) Likewise the choice between a model based and robust variance estimate for the curve will mirror the choice made in the \code{coxph} call, any clustering is also inherited from the parent model. If the \code{newdata} argument is missing, then a curve is produced for a single "pseudo" subject with covariate values equal to the means of the data set. The resulting curve(s) almost never make sense, but The default remains due to an unwarranted attachment to the option shown by some users and by other packages. Two particularly egregious examples are factor variables and interactions. Suppose one were studying interspecies transmission of a virus, and the data set has a factor variable with levels ("pig", "chicken") and about equal numbers of observations for each. The ``mean'' covariate level will be 0.5 -- is this a flying pig? As to interactions assume data with sex coded as 0/1, ages ranging from 50 to 80, and a model with age*sex. The ``mean'' value for the age:sex interaction term will be about 30, a value that does not occur in the data. Users are strongly advised to use the newdata argument. For these reasons predictions from a multistate coxph model require the newdata argument. When the original model contains time-dependent covariates, then the path of that covariate through time needs to be specified in order to obtain a predicted curve. This requires \code{newdata} to contain multiple lines for each hypothetical subject which gives the covariate values, time interval, and strata for each line (a subject can change strata), along with an \code{id} variable which demarks which rows belong to each subject. The time interval must have the same (start, stop, status) variables as the original model: although the status variable is not used and thus can be set to a dummy value of 0 or 1, it is necessary for the response to be recognized as a \code{Surv} object. Last, although predictions with a time-dependent covariate path can be useful, it is very easy to create a prediction that is senseless. Users are encouraged to seek out a text that discusses the issue in detail. When a model contains strata but no time-dependent covariates the user of this routine has a choice. If newdata argument does not contain strata variables then the returned object will be a matrix of survival curves with one row for each strata in the model and one column for each row in newdata. (This is the historical behavior of the routine.) If newdata does contain strata variables, then the result will contain one curve per row of newdata, based on the indicated stratum of the original model. In the rare case of a model with strata by covariate interactions the strata variable must be included in newdata, the routine does not allow it to be omitted (predictions become too confusing). (Note that the model Surv(time, status) ~ age*strata(sex) expands internally to strata(sex) + age:sex; the sex variable is needed for the second term of the model.) See \code{\link{survfit}} for more details about the counts (number of events, number at risk, etc.) } \section{Notes}{ If the following pair of lines is used inside of another function then the \code{model=TRUE} argument must be added to the coxph call: \code{fit <- coxph(...); survfit(fit)}. This is a consequence of the non-standard evaluation process used by the \code{model.frame} function when a formula is involved. } \section{References}{ Fleming, T. H. and Harrington, D. P. (1984). Nonparametric estimation of the survival distribution in censored data. \emph{Comm. in Statistics} \bold{13}, 2469-86. Kalbfleisch, J. D. and Prentice, R. L. (1980). \emph{The Statistical Analysis of Failure Time Data.} New York:Wiley. Link, C. L. (1984). Confidence intervals for the survival function using Cox's proportional hazards model with covariates. \emph{Biometrics} \bold{40}, 601-610. Therneau T and Grambsch P (2000), Modeling Survival Data: Extending the Cox Model, Springer-Verlag. Tsiatis, A. (1981). A large sample study of the estimate for the integrated hazard function in Cox's regression model for survival data. \emph{Annals of Statistics} \bold{9}, 93-108. } \seealso{ \code{\link{print.survfit}}, \code{\link{plot.survfit}}, \code{\link{lines.survfit}}, \code{\link{coxph}}, \code{\link{Surv}}, \code{\link{strata}}. } \keyword{survival} survival/man/basehaz.Rd0000644000176200001440000000257413537676563014641 0ustar liggesusers\name{basehaz} \alias{basehaz} \title{Alias for the survfit function} \description{ Compute the predicted survival curve for a Cox model. } \usage{ basehaz(fit, centered=TRUE) } \arguments{ \item{fit}{a coxph fit} \item{centered}{if TRUE return data from a predicted survival curve at the mean values of the covariates \code{fit$mean}, if FALSE return a prediction for all covariates equal to zero.} } \details{ This function is simply an alias for \code{survfit}, which does the actual work and has a richer set of options. The alias exists only because some users look for predicted survival estimates under this name. The function returns a data frame containing the \code{time}, \code{cumhaz} and optionally the strata (if the fitted Cox model used a strata statement), which are copied the \code{survfit} result. If there are factor variables in the model, then the default predictions at the "mean" are meaningless since they do not correspond to any possible subject; correct results require use of the \code{newdata} argument of survfit. Results for all covariates =0 are normally only of use as a building block for further calculations. } \value{ a data frame with variable names of \code{hazard}, \code{time} and optionally \code{strata}. The first is actually the cumulative hazard. } \seealso{\code{\link{survfit.coxph}}} \keyword{survival } survival/man/frailty.Rd0000644000176200001440000001172714027425473014661 0ustar liggesusers\name{frailty} \alias{frailty} \alias{frailty.gamma} \alias{frailty.gaussian} \alias{frailty.t} \title{ Random effects terms } \description{ The frailty function allows one to add a simple random effects term to a Cox model. } \usage{ frailty(x, distribution="gamma", ...) frailty.gamma(x, sparse = (nclass > 5), theta, df, eps = 1e-05, method = c("em","aic", "df", "fixed"), ...) frailty.gaussian(x, sparse = (nclass > 5), theta, df, method =c("reml","aic", "df", "fixed"), ...) frailty.t(x, sparse = (nclass > 5), theta, df, eps = 1e-05, tdf = 5, method = c("aic", "df", "fixed"), ...) } \arguments{ \item{x}{ the variable to be entered as a random effect. It is always treated as a factor. } \item{distribution}{ either the \code{gamma}, \code{gaussian} or \code{t} distribution may be specified. The routines \code{frailty.gamma}, \code{frailty.gaussian} and \code{frailty.t} do the actual work. } \item{\dots}{Arguments for specific distribution, including (but not limited to) } \item{sparse}{ cutoff for using a sparse coding of the data matrix. If the total number of levels of \code{x} is larger than this value, then a sparse matrix approximation is used. The correct cutoff is still a matter of exploration: if the number of levels is very large (thousands) then the non-sparse calculation may not be feasible in terms of both memory and compute time. Likewise, the accuracy of the sparse approximation appears to be related to the maximum proportion of subjects in any one class, being best when no one class has a large membership. } \item{theta}{ if specified, this fixes the variance of the random effect. If not, the variance is a parameter, and a best solution is sought. Specifying this implies \code{method='fixed'}. } \item{df}{ if specified, this fixes the degrees of freedom for the random effect. Specifying this implies \code{method='df'}. Only one of \code{theta} or \code{df} should be specified. } \item{method}{ the method used to select a solution for theta, the variance of the random effect. The \code{fixed} corresponds to a user-specified value, and no iteration is done. The \code{df} selects the variance such that the degrees of freedom for the random effect matches a user specified value. The \code{aic} method seeks to maximize Akaike's information criteria 2*(partial likelihood - df). The \code{em} and \code{reml} methods are specific to Cox models with gamma and gaussian random effects, respectively. Please see further discussion below. } \item{tdf}{ the degrees of freedom for the t-distribution. } \item{eps}{ convergence criteria for the iteration on theta. } } \value{ this function is used in the model statement of either \code{coxph} or \code{survreg}. It's results are used internally. } \details{ The \code{frailty} plugs into the general penalized modeling framework provided by the \code{coxph} and \code{survreg} routines. This framework deals with likelihood, penalties, and degrees of freedom; these aspects work well with either parent routine. Therneau, Grambsch, and Pankratz show how maximum likelihood estimation for the Cox model with a gamma frailty can be accomplished using a general penalized routine, and Ripatti and Palmgren work through a similar argument for the Cox model with a gaussian frailty. Both of these are specific to the Cox model. Use of gamma/ml or gaussian/reml with \code{survreg} does not lead to valid results. The extensible structure of the penalized methods is such that the penalty function, such as \code{frailty} or \code{pspine}, is completely separate from the modeling routine. The strength of this is that a user can plug in any penalization routine they choose. A weakness is that it is very difficult for the modeling routine to know whether a sensible penalty routine has been supplied. Note that use of a frailty term implies a mixed effects model and use of a cluster term implies a GEE approach; these cannot be mixed. The \code{coxme} package has superseded this method. It is faster, more stable, and more flexible. } \section{References}{ S Ripatti and J Palmgren, Estimation of multivariate frailty models using penalized partial likelihood, Biometrics, 56:1016-1022, 2000. T Therneau, P Grambsch and VS Pankratz, Penalized survival models and frailty, J Computational and Graphical Statistics, 12:156-175, 2003. } \seealso{ \link{coxph}, \link{survreg} } \examples{ # Random institutional effect coxph(Surv(time, status) ~ age + frailty(inst, df=4), lung) # Litter effects for the rats data rfit2a <- coxph(Surv(time, status) ~ rx + frailty.gaussian(litter, df=13, sparse=FALSE), rats, subset= (sex=='f')) rfit2b <- coxph(Surv(time, status) ~ rx + frailty.gaussian(litter, df=13, sparse=TRUE), rats, subset= (sex=='f')) } \keyword{survival} survival/man/retinopathy.Rd0000644000176200001440000000420514013472642015541 0ustar liggesusers\name{retinopathy} \alias{retinopathy} \docType{data} \title{Diabetic Retinopathy} \description{A trial of laser coagulation as a treatment to delay diabetic retinopathy. } \usage{retinopathy data(retinopathy, package="survival") } \format{ A data frame with 394 observations on the following 9 variables. \describe{ \item{\code{id}}{numeric subject id} \item{\code{laser}}{type of laser used: \code{xenon} \code{argon}} \item{\code{eye}}{which eye was treated: \code{right} \code{left}} \item{\code{age}}{age at diagnosis of diabetes} \item{\code{type}}{type of diabetes: \code{juvenile} \code{adult}, (diagnosis before age 20)} \item{\code{trt}}{0 = control eye, 1 = treated eye} \item{\code{futime}}{time to loss of vision or last follow-up} \item{\code{status}}{0 = censored, 1 = loss of vision in this eye} \item{\code{risk}}{a risk score for the eye. This high risk subset is defined as a score of 6 or greater in at least one eye.} } } \details{ The 197 patients in this dataset were a 50\% random sample of the patients with "high-risk" diabetic retinopathy as defined by the Diabetic Retinopathy Study (DRS). Each patient had one eye randomized to laser treatment and the other eye received no treatment, and has two observations in the data set. For each eye, the event of interest was the time from initiation of treatment to the time when visual acuity dropped below 5/200 two visits in a row. Thus there is a built-in lag time of approximately 6 months (visits were every 3 months). Survival times in this dataset are the actual time to vision loss in months, minus the minimum possible time to event (6.5 months). Censoring was caused by death, dropout, or end of the study. } \references{ W. J. Huster, R. Brookmeyer and S. G. Self (1989). Modelling paired survival data with covariates, Biometrics 45:145-156. A. L. Blair, D. R. Hadden, J. A. Weaver, D. B. Archer, P. B. Johnston and C. J. Maguire (1976). The 5-year prognosis for vision in diabetes, American Journal of Ophthalmology, 81:383-396. } \examples{ coxph(Surv(futime, status) ~ type + trt, cluster= id, retinopathy) } \keyword{datasets} survival/man/quantile.survfit.Rd0000644000176200001440000000722013745547716016536 0ustar liggesusers\name{quantile.survfit} \alias{quantile.survfit} \alias{quantile.survfitms} \title{Quantiles from a survfit object} \description{Retrieve quantiles and confidence intervals for them from a survfit object. } \usage{ \method{quantile}{survfit}(x, probs = c(0.25, 0.5, 0.75), conf.int = TRUE, scale, tolerance= sqrt(.Machine$double.eps), ...) \method{quantile}{survfitms}(x, probs = c(0.25, 0.5, 0.75), conf.int = TRUE, scale, tolerance= sqrt(.Machine$double.eps), ...) } \arguments{ \item{x}{a result of the survfit function} \item{probs}{numeric vector of probabilities with values in [0,1]} \item{conf.int}{should lower and upper confidence limits be returned?} \item{scale}{optional scale factor, e.g., \code{scale=365.25} would return results in years if the fit object were in days.} \item{tolerance}{tolerance for checking that the survival curve exactly equals one of the quantiles} \item{...}{optional arguments for other methods} } \details{ The kth quantile for a survival curve S(t) is the location at which a horizontal line at height p= 1-k intersects the plot of S(t). Since S(t) is a step function, it is possible for the curve to have a horizontal segment at exactly 1-k, in which case the midpoint of the horizontal segment is returned. This mirrors the standard behavior of the median when data is uncensored. If the survival curve does not fall to 1-k, then that quantile is undefined. In order to be consistent with other quantile functions, the argument \code{prob} of this function applies to the cumulative distribution function F(t) = 1-S(t). Confidence limits for the values are based on the intersection of the horizontal line at 1-k with the upper and lower limits for the survival curve. Hence confidence limits use the same p-value as was in effect when the curve was created, and will differ depending on the \code{conf.type} option of \code{survfit}. If the survival curves have no confidence bands, confidence limits for the quantiles are not available. When a horizontal segment of the survival curve exactly matches one of the requested quantiles the returned value will be the midpoint of the horizontal segment; this agrees with the usual definition of a median for uncensored data. Since the survival curve is computed as a series of products, however, there may be round off error. Assume for instance a sample of size 20 with no tied times and no censoring. The survival curve after the 10th death is (19/20)(18/19)(17/18) ... (10/11) = 10/20, but the computed result will not be exactly 0.5. Any horizontal segment whose absolute difference with a requested percentile is less than \code{tolerance} is considered to be an exact match. } \value{ The quantiles will be a vector if the \code{survfit} object contains only a single curve, otherwise it will be a matrix or array. In this case the last dimension will index the quantiles. If confidence limits are requested, then result will be a list with components \code{quantile}, \code{lower}, and \code{upper}, otherwise it is the vector or matrix of quantiles. } \author{Terry Therneau} \seealso{\code{\link{survfit}}, \code{\link{print.survfit}}, \code{\link{qsurvreg}} } \examples{ fit <- survfit(Surv(time, status) ~ ph.ecog, data=lung) quantile(fit) cfit <- coxph(Surv(time, status) ~ age + strata(ph.ecog), data=lung) csurv<- survfit(cfit, newdata=data.frame(age=c(40, 60, 80)), conf.type ="none") temp <- quantile(csurv, 1:5/10) temp[2,3,] # quantiles for second level of ph.ecog, age=80 quantile(csurv[2,3], 1:5/10) # quantiles of a single curve, same result } \keyword{ survival } survival/man/mgus.Rd0000644000176200001440000000704514013457336014156 0ustar liggesusers\name{mgus} \alias{mgus} \alias{mgus1} \docType{data} \title{Monoclonal gammopathy data} \description{ Natural history of 241 subjects with monoclonal gammopathy of undetermined significance (MGUS). } \usage{ mgus mgus1 data(cancer, package="survival") } \format{ mgus: A data frame with 241 observations on the following 12 variables. \tabular{ll}{ id:\tab subject id \cr age:\tab age in years at the detection of MGUS \cr sex:\tab \code{male} or \code{female} \cr dxyr:\tab year of diagnosis \cr pcdx:\tab for subjects who progress to a plasma cell malignancy \cr \tab the subtype of malignancy: multiple myeloma (MM) is the \cr \tab most common, followed by amyloidosis (AM), macroglobulinemia (MA),\cr \tab and other lymphprolifative disorders (LP) \cr pctime:\tab days from MGUS until diagnosis of a plasma cell malignancy \cr futime:\tab days from diagnosis to last follow-up \cr death:\tab 1= follow-up is until death \cr alb:\tab albumin level at MGUS diagnosis \cr creat:\tab creatinine at MGUS diagnosis \cr hgb:\tab hemoglobin at MGUS diagnosis \cr mspike:\tab size of the monoclonal protein spike at diagnosis \cr } mgus1: The same data set in start,stop format. Contains the id, age, sex, and laboratory variable described above along with \tabular{ll}{ start, stop:\tab sequential intervals of time for each subject \cr status:\tab =1 if the interval ends in an event \cr event:\tab a factor containing the event type: censor, death, or plasma cell malignancy \cr enum: \tab event number for each subject: 1 or 2 } } \details{ Plasma cells are responsible for manufacturing immunoglobulins, an important part of the immune defense. At any given time there are estimated to be about \eqn{10^6} different immunoglobulins in the circulation at any one time. When a patient has a plasma cell malignancy the distribution will become dominated by a single isotype, the product of the malignant clone, visible as a spike on a serum protein electrophoresis. Monoclonal gammopathy of undertermined significance (MGUS) is the presence of such a spike, but in a patient with no evidence of overt malignancy. This data set of 241 sequential subjects at Mayo Clinic was the groundbreaking study defining the natural history of such subjects. Due to the diligence of the principle investigator 0 subjects have been lost to follow-up. Three subjects had MGUS detected on the day of death. In data set \code{mgus1} these subjects have the time to MGUS coded as .5 day before the death in order to avoid tied times. These data sets were updated in Jan 2015 to correct some small errors. } \source{ Mayo Clinic data courtesy of Dr. Robert Kyle. } \examples{ # Create the competing risk curves for time to first of death or PCM sfit <- survfit(Surv(start, stop, event) ~ sex, mgus1, id=id, subset=(enum==1)) print(sfit) # the order of printout is the order in which they plot plot(sfit, xscale=365.25, lty=c(2,2,1,1), col=c(1,2,1,2), xlab="Years after MGUS detection", ylab="Proportion") legend(0, .8, c("Death/male", "Death/female", "PCM/male", "PCM/female"), lty=c(1,1,2,2), col=c(2,1,2,1), bty='n') title("Curves for the first of plasma cell malignancy or death") # The plot shows that males have a higher death rate than females (no # surprise) but their rates of conversion to PCM are essentially the same. } \references{ R Kyle, Benign monoclonal gammopathy -- after 20 to 35 years of follow-up, Mayo Clinic Proc 1993; 68:26-36. } \keyword{datasets} \keyword{survival} survival/man/summary.coxph.Rd0000644000176200001440000000404713537676563016036 0ustar liggesusers\name{summary.coxph} \alias{summary.coxph} \title{ Summary method for Cox models } \description{ Produces a summary of a fitted coxph model } \usage{ \method{summary}{coxph}(object, conf.int=0.95, scale=1,...) } \arguments{ \item{object}{ the result of a coxph fit } \item{conf.int}{ level for computation of the confidence intervals. If set to FALSE no confidence intervals are printed } \item{scale}{ vector of scale factors for the coefficients, defaults to 1. The printed coefficients, se, and confidence intervals will be associated with one scale unit. } \item{\dots}{for future methods} } \value{ An object of class \code{summary.coxph}, with components: \item{n, nevent}{number of observations and number of events, respectively, in the fit} \item{loglik}{the log partial likelihood at the initial and final values} \item{coefficients}{a matrix with one row for each coefficient, and columns containing the coefficient, the hazard ratio exp(coef), standard error, Wald statistic, and P value.} \item{conf.int}{a matrix with one row for each coefficient, containing the confidence limits for exp(coef)} \item{logtest, sctest, waldtest}{the overall likelihood ratio, score, and Wald test statistics for the model} \item{concordance}{the concordance statistic and its standard error} \item{used.robust}{whether an asymptotic or robust variance was used} \item{rsq}{an approximate R^2 based on Nagelkirke (Biometrika 1991).} \item{fail}{a message, if the underlying coxph call failed} \item{call}{a copy of the call} \item{na.action}{information on missing values} } \note{ The pseudo r-squared of Nagelkirke is attractive because it is simple, but further work has shown that it has poor properties and it is now depricated. The value is no longer printed by default, and will eventually be removed from the object. } \seealso{ \code{\link{coxph}}, \code{\link{print.coxph}} } \examples{ fit <- coxph(Surv(time, status) ~ age + sex, lung) summary(fit) } \keyword{survival} survival/man/nsk.Rd0000644000176200001440000001266513773503416014005 0ustar liggesusers\name{nsk} \alias{nsk} \title{ Natural splines with knot heights as the basis. } \description{ Create the design matrix for a natural spline, such that the coefficient of the resulting fit are the values of the function at the knots. } \usage{ nsk(x, df = NULL, knots = NULL, intercept = FALSE, b = 0.05, Boundary.knots = quantile(x, c(b, 1 - b), na.rm = TRUE)) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{the predictor variable. Missing values are allowed. } \item{df}{ degrees of freedom. One can supply df rather than knots; ns() then chooses df - 1 - intercept knots at suitably chosen quantiles of x (which will ignore missing values). The default, df = NULL, sets the number of inner knots as length(knots). } \item{knots}{ breakpoints that define the spline. The default is no knots; together with the natural boundary conditions this results in a basis for linear regression on x. Typical values are the mean or median for one knot, quantiles for more knots. See also Boundary.knots. } \item{intercept}{ if TRUE, an intercept is included in the basis; default is FALSE } \item{b}{default placement of the boundary knots. A value of \code{bs=0} will replicate the default behavior of \code{ns}. } \item{Boundary.knots}{ boundary points at which to impose the natural boundary conditions and anchor the B-spline basis. Beyond these points the function is assumed to be linear. If both knots and Boundary.knots are supplied, the basis parameters do not depend on x. Data can extend beyond Boundary.knots } } \details{ The \code{nsk} function behaves identically to the \code{ns} function, with two exceptions. The primary one is that the returned basis is such that coefficients correspond to the value of the fitted function at the knot points. If \code{intercept = FALSE}, there will be k-1 coefficients corresponding to the k knots, and they will be the difference in predicted value between knots 2-k and knot 1. The primary advantage to the basis is that the coefficients are directly interpretable. A second is that tests for the linear and non-linear components are simple contrasts. The second differnce with \code{ns} is one of opinion with respect to the default position for the boundary knots. The default here is closer to that found in the \code{rms::rcs} function. } \value{ A matrix of dimension length(x) * df where either df was supplied or, if knots were supplied, df = length(knots) + 1 + intercept. Attributes are returned that correspond to the arguments to kns, and explicitly give the knots, Boundary.knots etc for use by predict.kns(). } \note{ A thin flexible metal or wooden strip is called a spline, and is the traditional method for laying out a smooth curve, e.g., for a ship's hull or an airplane wing. Pins are put into a board and the strip is passed through them, each pin is a 'knot'. A mathematical spline is a piecewise function between each knot. A linear spline will be a set of connected line segments, a quadratic spline is a set of connected local quadratic functions, constrained to have a continuous first derivative, a cubic spline is cubic between each knot, constrained to have continuous first and second derivatives, and etc. Mathematical splines are not an exact representation of natural splines: being a physical object the wood or metal strip will have continuous derivatives of all orders. Cubic splines are commonly used because they are sufficiently smooth to look natural to the human eye. If the mathematical spline is further constrained to be linear beyond the end knots, this is often called a 'natural spline', due to the fact that a wooden or metal spline will also be linear beyond the last knots. Another name for the same object is a 'restricted cubic spline', since it is achieved in code by adding a further constraint. Given a vector of data points and a set of knots, it is possible to create a basis matrix X with one column per knot, such that ordinary regression of X on y will fit the cubic spline function, hence these are also called 'regression splines'. One label is no better than another. Given a basis matrix $X$, the matrix Z= XT for any k by k nonsingular matrix T is is also a basis matrix, and will result in identical predicted values, but a new set of coefficients gamma = (T-inverse) beta in place of beta. One can choose the basis function so that X is easy to construct, to make the regression numerically stable, to make tests easier, or based on other considerations. It seems as though every spline function returns a different basis set, which unfortunately makes fits difficult to compare; and this is yet one more, chosen to make the coefficients more interpretable. } \seealso{ \code{\link[splines]{ns}} } \examples{ # make some dummy data tdata <- data.frame(x= lung$age, y = 10*log(lung$age-35) + rnorm(228, 0, 2)) fit1 <- lm(y ~ -1 + nsk(x, df=4, intercept=TRUE) , data=tdata) fit2 <- lm(y ~ nsk(x, df=3), data=tdata) # the knots (same for both fits) knots <- unlist(attributes(fit1$model[[2]])[c('Boundary.knots', 'knots')]) knots unname(coef(fit1)) # predictions at the knot points unname(coef(fit1)[-1] - coef(fit1)[1]) # differences: yhat[2:4] - yhat[1] unname(coef(fit2)) \dontrun{ plot(y ~ x, data=tdata) points(sort(knots), coef(fit1), col=2, pch=19) coef(fit)[1] + c(0, coef(fit)[-1]) } } \keyword{ smooth } survival/man/cch.Rd0000644000176200001440000001204313722530320013721 0ustar liggesusers\alias{cch} \name{cch} \title{Fits proportional hazards regression model to case-cohort data} \description{ Returns estimates and standard errors from relative risk regression fit to data from case-cohort studies. A choice is available among the Prentice, Self-Prentice and Lin-Ying methods for unstratified data. For stratified data the choice is between Borgan I, a generalization of the Self-Prentice estimator for unstratified case-cohort data, and Borgan II, a generalization of the Lin-Ying estimator. } \usage{ cch(formula, data, subcoh, id, stratum=NULL, cohort.size, method =c("Prentice","SelfPrentice","LinYing","I.Borgan","II.Borgan"), robust=FALSE) } \arguments{ \item{formula}{ A formula object that must have a \code{\link{Surv}} object as the response. The Surv object must be of type \code{"right"}, or of type \code{"counting"}. } \item{subcoh}{ Vector of indicators for subjects sampled as part of the sub-cohort. Code \code{1} or \code{TRUE} for members of the sub-cohort, \code{0} or \code{FALSE} for others. If \code{data} is a data frame then \code{subcoh} may be a one-sided formula. } \item{id}{ Vector of unique identifiers, or formula specifying such a vector. } \item{stratum}{A vector of stratum indicators or a formula specifying such a vector} \item{cohort.size}{ Vector with size of each stratum original cohort from which subcohort was sampled } \item{data}{ An optional data frame in which to interpret the variables occurring in the formula. } \item{method}{ Three procedures are available. The default method is "Prentice", with options for "SelfPrentice" or "LinYing". } \item{robust}{For \code{"LinYing"} only, if \code{robust=TRUE}, use design-based standard errors even for phase I} } \value{ An object of class "cch" incorporating a list of estimated regression coefficients and two estimates of their asymptotic variance-covariance matrix. \item{coef}{ regression coefficients. } \item{naive.var}{ Self-Prentice model based variance-covariance matrix. } \item{var}{ Lin-Ying empirical variance-covariance matrix. }} \details{ Implements methods for case-cohort data analysis described by Therneau and Li (1999). The three methods differ in the choice of "risk sets" used to compare the covariate values of the failure with those of others at risk at the time of failure. "Prentice" uses the sub-cohort members "at risk" plus the failure if that occurs outside the sub-cohort and is score unbiased. "SelfPren" (Self-Prentice) uses just the sub-cohort members "at risk". These two have the same asymptotic variance-covariance matrix. "LinYing" (Lin-Ying) uses the all members of the sub-cohort and all failures outside the sub-cohort who are "at risk". The methods also differ in the weights given to different score contributions. The \code{data} argument must not have missing values for any variables in the model. There must not be any censored observations outside the subcohort. } \author{Norman Breslow, modified by Thomas Lumley} \references{ Prentice, RL (1986). A case-cohort design for epidemiologic cohort studies and disease prevention trials. Biometrika 73: 1--11. Self, S and Prentice, RL (1988). Asymptotic distribution theory and efficiency results for case-cohort studies. Annals of Statistics 16: 64--81. Lin, DY and Ying, Z (1993). Cox regression with incomplete covariate measurements. Journal of the American Statistical Association 88: 1341--1349. Barlow, WE (1994). Robust variance estimation for the case-cohort design. Biometrics 50: 1064--1072 Therneau, TM and Li, H (1999). Computing the Cox model for case-cohort designs. Lifetime Data Analysis 5: 99--112. Borgan, \eqn{O}{O}, Langholz, B, Samuelsen, SO, Goldstein, L and Pogoda, J (2000) Exposure stratified case-cohort designs. Lifetime Data Analysis 6, 39-58. } \seealso{ \code{twophase} and \code{svycoxph} in the "survey" package for more general two-phase designs. \url{http://faculty.washington.edu/tlumley/survey/} } \examples{ ## The complete Wilms Tumor Data ## (Breslow and Chatterjee, Applied Statistics, 1999) ## subcohort selected by simple random sampling. ## subcoh <- nwtco$in.subcohort selccoh <- with(nwtco, rel==1|subcoh==1) ccoh.data <- nwtco[selccoh,] ccoh.data$subcohort <- subcoh[selccoh] ## central-lab histology ccoh.data$histol <- factor(ccoh.data$histol,labels=c("FH","UH")) ## tumour stage ccoh.data$stage <- factor(ccoh.data$stage,labels=c("I","II","III","IV")) ccoh.data$age <- ccoh.data$age/12 # Age in years ## ## Standard case-cohort analysis: simple random subcohort ## fit.ccP <- cch(Surv(edrel, rel) ~ stage + histol + age, data =ccoh.data, subcoh = ~subcohort, id=~seqno, cohort.size=4028) fit.ccP fit.ccSP <- cch(Surv(edrel, rel) ~ stage + histol + age, data =ccoh.data, subcoh = ~subcohort, id=~seqno, cohort.size=4028, method="SelfPren") summary(fit.ccSP) ## ## (post-)stratified on instit ## stratsizes<-table(nwtco$instit) fit.BI<- cch(Surv(edrel, rel) ~ stage + histol + age, data =ccoh.data, subcoh = ~subcohort, id=~seqno, stratum=~instit, cohort.size=stratsizes, method="I.Borgan") summary(fit.BI) } \keyword{survival} survival/man/survfit.matrix.Rd0000644000176200001440000000713413537676563016226 0ustar liggesusers\name{survfit.matrix} \alias{survfit.matrix} \title{Create Aalen-Johansen estimates of multi-state survival from a matrix of hazards.} \description{ This allows one to create the Aalen-Johansen estimate of P, a matrix with one column per state and one row per time, starting with the individual hazard estimates. Each row of P will sum to 1. Note that this routine has been superseded by the use of multi-state Cox models, and will eventually be removed. } \usage{ \method{survfit}{matrix}(formula, p0, method = c("discrete", "matexp"), start.time, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{a matrix of lists, each element of which is either NULL or a survival curve object. } \item{p0}{the initial state vector. The names of this vector are used as the names of the states in the output object. If there are multiple curves then \code{p0} can be a matrix with one row per curve. } \item{method}{ use a product of discrete hazards, or a product of matrix exponentials. See details below. } \item{start.time}{optional; start the calculations at a given starting point} \item{...}{further arguments used by other survfit methods} } \details{ On input the matrix should contain a set of predicted curves for each possible transition, and NULL in other positions. Each of the predictions will have been obtained from the relevant Cox model. This approach for multistate curves is easy to use but has some caveats. First, the input curves must be consistent. The routine checks as best it can, but can easy be fooled. For instance, if one were to fit two Cox models, obtain predictions for males and females from one, and for treatment A and B from the other, this routine will create two curves but they are not meaningful. A second issue is that standard errors are not produced. The names of the resulting states are taken from the names of the vector of initial state probabilities. If they are missing, then the dimnames of the input matrix are used, and lacking that the labels '1', '2', etc. are used. For the usual Aalen-Johansen estimator the multiplier at each event time is the matrix of hazards H (also written as I + dA). When using predicted survival curves from a Cox model, however, it is possible to get predicted hazards that are greater than 1, which leads to probabilities less than 0. If the \code{method} argument is not supplied and the input curves are derived from a Cox model this routine instead uses the approximation expm(H-I) as the multiplier, which always gives valid probabilities. (This is also the standard approach for ordinary survival curves from a Cox model.) } \value{a survfitms object} \author{Terry Therneau} \note{The R syntax for creating a matrix of lists is very fussy.} \seealso{\code{\link{survfit}}} \examples{ etime <- with(mgus2, ifelse(pstat==0, futime, ptime)) event <- with(mgus2, ifelse(pstat==0, 2*death, 1)) event <- factor(event, 0:2, labels=c("censor", "pcm", "death")) cfit1 <- coxph(Surv(etime, event=="pcm") ~ age + sex, mgus2) cfit2 <- coxph(Surv(etime, event=="death") ~ age + sex, mgus2) # predicted competing risk curves for a 72 year old with mspike of 1.2 # (median values), male and female. # The survfit call is a bit faster without standard errors. newdata <- expand.grid(sex=c("F", "M"), age=72, mspike=1.2) AJmat <- matrix(list(), 3,3) AJmat[1,2] <- list(survfit(cfit1, newdata, std.err=FALSE)) AJmat[1,3] <- list(survfit(cfit2, newdata, std.err=FALSE)) csurv <- survfit(AJmat, p0 =c(entry=1, PCM=0, death=0)) } \keyword{survival } survival/man/pseudo.Rd0000644000176200001440000000615514016245357014504 0ustar liggesusers\name{pseudo} \alias{pseudo} \title{ Pseudo values for survival. } \description{ Produce pseudo values from a survival curve. } \usage{ pseudo(fit, times, type, addNA=TRUE, data.frame=FALSE, minus1=FALSE, ...) } \arguments{ \item{fit}{a \code{survfit} object, or one that inherits that class. } \item{times}{ a vector of time points, at which to evaluate the pseudo values. } \item{type}{ the type of value, either the probabilty in state \code{pstate}, the cumulative hazard \code{cumhaz} or the expected sojourn time in the state \code{sojourn}. } \item{addNA}{If any observations were removed due to missing values in the \code{fit} object, add those rows (as NA) into the return. This causes the result of pseudo to match the original dataframe. } \item{data.frame}{if TRUE, return the data in "long" form as a data.frame with id, time, and pseudo as variables.} \item{minus1}{use n-1 as the multiplier rather than n}. \item{\dots}{ other arguments to the \code{residuals.survfit} function, which does the majority of the work, e.g., \code{collapse} and \code{weighted}. } } \details{ This function computes pseudo values based on a first order Taylor series, also known as the "infinitesimal jackknife" (IJ) or "dfbeta" residuals. To be completely correct these results could perhaps be called `IJ pseudo values' or even pseudo psuedo-values. For moderate to large data, however, the resulting values will be almost identical, numerically, to the ordinary jackknife. A primary advantage of this approach is computational speed. Other features, neither good nor bad, are that they will agree with robust standard errors of other survival package estimates, which are based on the IJ, and that the mean of the estimates, over subjects, is exactly the underlying survival estimate. For the \code{type} variable, \code{surv} is an acceptable synonym for \code{pstate}, and \code{rmst, rmts} are equivalent to \code{sojourn}. All of these are case insensitive. } \value{ A vector, matrix, or array. The first dimension is always the number of observations in \code{fit} object, in the same order as the original data set (less any missing values that were removed when creating the survfit object); the second, if applicable, corresponds to \code{fit$states}, e.g., multi-state survival, and the last dimension to the selected time points. For the data.frame option, a data frame containing values for id, time, and pseudo. If the original \code{survfit} call contained an \code{id} statement, then the values in the \code{id} column will be taken from that variable. If the \code{id} statement has a simple form, e.g., \code{id = patno}, then the name of the id column will be `patno', otherwise it will be named `(id)'. } \references{ PK Andersen and M Pohar-Perme, Pseudo-observations in surivival analysis, Stat Methods Medical Res, 2010; 19:71-99 } \seealso{ \code{\link{residuals.survfit}} } \examples{ fit1 <- survfit(Surv(time, status) ~ 1, data=lung) yhat <- pseudo(fit1, times=c(365, 730)) dim(yhat) lfit <- lm(yhat[,1] ~ ph.ecog + age + sex, data=lung) } \keyword{ survival } survival/man/survfit.formula.Rd0000644000176200001440000003041313716107436016346 0ustar liggesusers\name{survfit.formula} \alias{survfit.formula} \alias{[.survfit} \title{ Compute a Survival Curve for Censored Data } \description{ Computes an estimate of a survival curve for censored data using the Aalen-Johansen estimator. For ordinary (single event) survival this reduces to the Kaplan-Meier estimate. } \usage{ \method{survfit}{formula}(formula, data, weights, subset, na.action, stype=1, ctype=1, id, cluster, robust, istate, timefix=TRUE, etype, error, ...) } \arguments{ \item{formula}{ a formula object, which must have a \code{Surv} object as the response on the left of the \code{~} operator and, if desired, terms separated by + operators on the right. One of the terms may be a \code{strata} object. For a single survival curve the right hand side should be \code{~ 1}. } \item{data}{ a data frame in which to interpret the variables named in the formula, \code{subset} and \code{weights} arguments. } \item{weights}{ The weights must be nonnegative and it is strongly recommended that they be strictly positive, since zero weights are ambiguous, compared to use of the \code{subset} argument. } \item{subset}{ expression saying that only a subset of the rows of the data should be used in the fit. } \item{na.action}{ a missing-data filter function, applied to the model frame, after any \code{subset} argument has been used. Default is \code{options()$na.action}. } \item{stype}{the method to be used estimation of the survival curve: 1 = direct, 2 = exp(cumulative hazard). } \item{ctype}{the method to be used for estimation of the cumulative hazard: 1 = Nelson-Aalen formula, 2 = Fleming-Harrington correction for tied events.} \item{id}{ identifies individual subjects, when a given person can have multiple lines of data. } \item{cluster}{used to group observations for the infinitesmal jackknife variance estimate, defaults to the value of id.} \item{robust}{logical, should the function compute a robust variance. For multi-state survival curves this is true by default. For single state data see details, below.} \item{istate}{for multi-state models, identifies the initial state of each subject or observation} \item{timefix}{process times through the \code{aeqSurv} function to eliminate potential roundoff issues.} \item{etype}{ a variable giving the type of event. This has been superseded by multi-state Surv objects and is depricated; see example below. } \item{error}{this argument is no longer used} \item{\dots}{ The following additional arguments are passed to internal functions called by \code{survfit}. \describe{ \item{se.fit}{logical value, default is TRUE. If FALSE then standard error computations are omitted. } \item{conf.type}{ One of \code{"none"}, \code{"plain"}, \code{"log"} (the default), \code{"log-log"}, \code{"logit"} or \code{"arcsin"}. Only enough of the string to uniquely identify it is necessary. The first option causes confidence intervals not to be generated. The second causes the standard intervals \code{curve +- k *se(curve)}, where k is determined from \code{conf.int}. The log option calculates intervals based on the cumulative hazard or log(survival). The log-log option bases the intervals on the log hazard or log(-log(survival)), the logit option on log(survival/(1-survival)) and arcsin on arcsin(survival). } \item{conf.lower}{ a character string to specify modified lower limits to the curve, the upper limit remains unchanged. Possible values are \code{"usual"} (unmodified), \code{"peto"}, and \code{"modified"}. The modified lower limit is based on an "effective n" argument. The confidence bands will agree with the usual calculation at each death time, but unlike the usual bands the confidence interval becomes wider at each censored observation. The extra width is obtained by multiplying the usual variance by a factor m/n, where n is the number currently at risk and m is the number at risk at the last death time. (The bands thus agree with the un-modified bands at each death time.) This is especially useful for survival curves with a long flat tail. The Peto lower limit is based on the same "effective n" argument as the modified limit, but also replaces the usual Greenwood variance term with a simple approximation. It is known to be conservative. } \item{start.time}{ numeric value specifying a time to start calculating survival information. The resulting curve is the survival conditional on surviving to \code{start.time}. } \item{conf.int}{ the level for a two-sided confidence interval on the survival curve(s). Default is 0.95. } \item{se.fit}{ a logical value indicating whether standard errors should be computed. Default is \code{TRUE}. } \item{influence}{a logical value indicating whether to return the infinitesimal jackknife (influence) values for each subject. These contain the values of the derivative of each value with respect to the case weights of each subject i: \eqn{\partial p/\partial w_i}{dp/dw[i]}, evaluated at the vector of weights. The resulting object will contain \code{influence.surv} and \code{influence.chaz} components. Alternatively, options of \code{influence=1} or \code{influence=2} will return values for only the survival or hazard curves, respectively. } \item{p0}{this applies only to multi-state curves. An optional vector giving the initial probability across the states. If this is missing, then p0 is estimated using the frequency of the starting states of all observations at risk at \code{start.time}, or if that is not specified, at the time of the first event.} \item{type}{an older argument that combined \code{stype} and \code{ctype}, now depricated. Legal values were "kaplan-meier" which is equivalent to \code{stype=1, ctype=1}, "fleming-harrington" which is equivalent to \code{stype=2, ctype=1}, and "fh2" which is equivalent to \code{stype=2, ctype=2.} } } } } \value{ an object of class \code{"survfit"}. See \code{survfit.object} for details. Methods defined for survfit objects are \code{print}, \code{plot}, \code{lines}, and \code{points}. } \details{ If there is a \code{data} argument, then variables in the \code{formula}, code{weights}, \code{subset}, \code{id}, \code{cluster} and \code{istate} arguments will be searched for in that data set. The routine returns both an estimated probability in state and an estimated cumulative hazard estimate. The cumulative hazard estimate is the Nelson-Aalen (NA) estimate or the Fleming-Harrington (FH) estimate, the latter includes a correction for tied event times. The estimated probability in state can estimated either using the exponential of the cumulative hazard, or as a direct estimate using the Aalen-Johansen approach. For single state data the AJ estimate reduces to the Kaplan-Meier and the probability in state to the survival curve; for competing risks data the AJ reduces to the cumulative incidence (CI) estimator. For backward compatability the \code{type} argument can be used instead. When the data set includes left censored or interval censored data (or both), then the EM approach of Turnbull is used to compute the overall curve. Currently this algorithm is very slow, only a survival curve is produced, and it does not support a robust variance. Robust variance: If a \code{robust} is TRUE, or for multi-state curves, then the standard errors of the results will be based on an infinitesimal jackknife (IJ) estimate, otherwise the standard model based estimate will be used. For single state curves, the default for \code{robust} will be TRUE if one of: there is a \code{cluster} argument, there are non-integer weights, or there is a \code{id} statement and at least one of the id values has multiple events, and FALSE otherwise. The default represents our best guess about when one would most often desire a robust variance. When there are non-integer case weights and (time1, time2) survival data the routine is at an impasse: a robust variance likely is called for, but requires either \code{id} or \code{cluster} information to be done correctly; it will default to robust=FALSE. With the IJ estimate, the leverage values themselves can be returned as arrays with dimensions: number of subjects, number of unique times, and for a multi-state model, the number of unique states. Be forwarned that these arrays can be huge. If there is a \code{cluster} argument this first dimension will be the number of clusters and the variance will be a grouped IJ estimate; this can be an important tool for reducing the size. A numeric value for the \code{influence} argument allows finer control: 0= return neither (same as FALSE), 1= return the influence array for probability in state, 2= return the influence array for the cumulative hazard, 3= both (same as TRUE). } \section{References}{ Dorey, F. J. and Korn, E. L. (1987). Effective sample sizes for confidence intervals for survival probabilities. \emph{Statistics in Medicine} \bold{6}, 679-87. Fleming, T. H. and Harrington, D. P. (1984). Nonparametric estimation of the survival distribution in censored data. \emph{Comm. in Statistics} \bold{13}, 2469-86. Kalbfleisch, J. D. and Prentice, R. L. (1980). \emph{The Statistical Analysis of Failure Time Data.} New York:Wiley. Kyle, R. A. (1997). Moncolonal gammopathy of undetermined significance and solitary plasmacytoma. Implications for progression to overt multiple myeloma\}, \emph{Hematology/Oncology Clinics N. Amer.} \bold{11}, 71-87. Link, C. L. (1984). Confidence intervals for the survival function using Cox's proportional hazards model with covariates. \emph{Biometrics} \bold{40}, 601-610. Turnbull, B. W. (1974). Nonparametric estimation of a survivorship function with doubly censored data. \emph{J Am Stat Assoc}, \bold{69}, 169-173. } \seealso{ \code{\link{survfit.coxph}} for survival curves from Cox models, \code{\link{survfit.object}} for a description of the components of a survfit object, \code{\link{print.survfit}}, \code{\link{plot.survfit}}, \code{\link{lines.survfit}}, \code{\link{coxph}}, \code{\link{Surv}}. } \examples{ #fit a Kaplan-Meier and plot it fit <- survfit(Surv(time, status) ~ x, data = aml) plot(fit, lty = 2:3) legend(100, .8, c("Maintained", "Nonmaintained"), lty = 2:3) #fit a Cox proportional hazards model and plot the #predicted survival for a 60 year old fit <- coxph(Surv(futime, fustat) ~ age, data = ovarian) plot(survfit(fit, newdata=data.frame(age=60)), xscale=365.25, xlab = "Years", ylab="Survival") # Here is the data set from Turnbull # There are no interval censored subjects, only left-censored (status=3), # right-censored (status 0) and observed events (status 1) # # Time # 1 2 3 4 # Type of observation # death 12 6 2 3 # losses 3 2 0 3 # late entry 2 4 2 5 # tdata <- data.frame(time =c(1,1,1,2,2,2,3,3,3,4,4,4), status=rep(c(1,0,2),4), n =c(12,3,2,6,2,4,2,0,2,3,3,5)) fit <- survfit(Surv(time, time, status, type='interval') ~1, data=tdata, weight=n) # # Three curves for patients with monoclonal gammopathy. # 1. KM of time to PCM, ignoring death (statistically incorrect) # 2. Competing risk curves (also known as "cumulative incidence") # 3. Multi-state, showing Pr(in each state, at time t) # fitKM <- survfit(Surv(stop, event=='pcm') ~1, data=mgus1, subset=(start==0)) fitCR <- survfit(Surv(stop, event) ~1, data=mgus1, subset=(start==0)) fitMS <- survfit(Surv(start, stop, event) ~ 1, id=id, data=mgus1) \dontrun{ # CR curves show the competing risks plot(fitCR, xscale=365.25, xmax=7300, mark.time=FALSE, col=2:3, xlab="Years post diagnosis of MGUS", ylab="P(state)") lines(fitKM, fun='event', xmax=7300, mark.time=FALSE, conf.int=FALSE) text(3652, .4, "Competing risk: death", col=3) text(5840, .15,"Competing risk: progression", col=2) text(5480, .30,"KM:prog") } } \keyword{survival} survival/man/survregDtest.Rd0000644000176200001440000000301213537676563015711 0ustar liggesusers\name{survregDtest} \alias{survregDtest} \title{Verify a survreg distribution} \description{ This routine is called by \code{survreg} to verify that a distribution object is valid. } \usage{ survregDtest(dlist, verbose = F) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{dlist}{the list describing a survival distribution} \item{verbose}{return a simple TRUE/FALSE from the test for validity (the default), or a verbose description of any flaws.} } \details{ If the \code{survreg} function rejects your user-supplied distribution as invalid, this routine will tell you why it did so. } \value{ TRUE if the distribution object passes the tests, and either FALSE or a vector of character strings if not. } \author{Terry Therneau} \seealso{\code{\link{survreg.distributions}}, \code{\link{survreg}}} \examples{ # An invalid distribution (it should have "init =" on line 2) # surveg would give an error message mycauchy <- list(name='Cauchy', init<- function(x, weights, ...) c(median(x), mad(x)), density= function(x, parms) { temp <- 1/(1 + x^2) cbind(.5 + atan(temp)/pi, .5+ atan(-temp)/pi, temp/pi, -2 *x*temp, 2*temp^2*(4*x^2*temp -1)) }, quantile= function(p, parms) tan((p-.5)*pi), deviance= function(...) stop('deviance residuals not defined') ) survregDtest(mycauchy, TRUE) } \keyword{survival} survival/man/xtfrm.Surv.Rd0000644000176200001440000000227413537676563015317 0ustar liggesusers\name{xtfrm.Surv} \alias{xtfrm.Surv} \alias{sort.Surv} \alias{order.Surv} \title{Sorting order for Surv objects} \description{ Sort survival objects into a partial order, which is the same one used internally for many of the calculations. } \usage{ \method{xtfrm}{Surv}(x) } \arguments{ \item{x}{a \code{Surv} object} } \details{ This creates a partial ordering of survival objects. The result is sorted in time order, for tied pairs of times right censored events come after observed events (censor after death), and left censored events are sorted before observed events. For counting process data \code{(tstart, tstop, status)} the ordering is by stop time, status, and start time, again with censoring last. Interval censored data is sorted using the midpoint of each interval. The \code{xtfrm} routine is used internally by \code{order} and \code{sort}, so these results carry over to those routines. } \value{a vector of integers which will have the same sort order as \code{x}. } \author{Terry Therneau} \seealso{\code{\link{sort}}, \code{\link{order}}} \examples{ test <- c(Surv(c(10, 9,9, 8,8,8,7,5,5,4), rep(1:0, 5)), Surv(6.2, NA)) test sort(test) } \keyword{survival} survival/man/ratetableDate.Rd0000644000176200001440000000152013537676563015753 0ustar liggesusers\name{ratetableDate} \alias{ratetableDate} \title{Convert date objects to ratetable form} \description{ This method converts dates from various forms into the internal form used in \code{ratetable} objects. } \usage{ ratetableDate(x) } \arguments{ \item{x}{a date. The function currently has methods for Date, date, POSIXt, timeDate, and chron objects. } } \details{ This function is useful for those who create new ratetables, but is normally invisible to users. It is used internally by the \code{survexp} and \code{pyears} functions to map the various date formats; if a new method is added then those routines will automatically be adapted to the new date type. } \value{a numeric vector, the number of days since 1/1/1960.} \author{Terry Therneau} \seealso{\code{\link{pyears}}, \code{\link{survexp}}} \keyword{survival} survival/man/tobin.Rd0000644000176200001440000000141014013462370014276 0ustar liggesusers\name{tobin} \alias{tobin} \docType{data} \title{Tobin's Tobit data} \description{ Economists fit a parametric censored data model called the \sQuote{tobit}. These data are from Tobin's original paper. } \usage{tobin data(tobin, package="survival") } \format{ A data frame with 20 observations on the following 3 variables. \describe{ \item{durable}{Durable goods purchase} \item{age}{Age in years} \item{quant}{Liquidity ratio (x 1000)} } } \source{ J Tobin (1958), Estimation of relationships for limited dependent variables. \emph{Econometrica} \bold{26}, 24--36. } \examples{ tfit <- survreg(Surv(durable, durable>0, type='left') ~age + quant, data=tobin, dist='gaussian') predict(tfit,type="response") } \keyword{datasets} survival/man/pyears.Rd0000644000176200001440000001715613537676563014531 0ustar liggesusers\name{pyears} \alias{pyears} \title{ Person Years } \description{ This function computes the person-years of follow-up time contributed by a cohort of subjects, stratified into subgroups. It also computes the number of subjects who contribute to each cell of the output table, and optionally the number of events and/or expected number of events in each cell. } \usage{ pyears(formula, data, weights, subset, na.action, rmap, ratetable, scale=365.25, expect=c('event', 'pyears'), model=FALSE, x=FALSE, y=FALSE, data.frame=FALSE) } \arguments{ \item{formula}{ a formula object. The response variable will be a vector of follow-up times for each subject, or a \code{Surv} object containing the survival time and an event indicator. The predictors consist of optional grouping variables separated by + operators (exactly as in \code{survfit}), time-dependent grouping variables such as age (specified with \code{tcut}), and optionally a \code{ratetable} term. This latter matches each subject to his/her expected cohort. } \item{data}{ a data frame in which to interpret the variables named in the \code{formula}, or in the \code{subset} and the \code{weights} argument. } \item{weights}{ case weights. } \item{subset}{ expression saying that only a subset of the rows of the data should be used in the fit. } \item{na.action}{ a missing-data filter function, applied to the model.frame, after any \code{subset} argument has been used. Default is \code{options()$na.action}. } \item{rmap}{ an optional list that maps data set names to the ratetable names. See the details section below. } \item{ratetable}{ a table of event rates, such as \code{survexp.uswhite}. } \item{scale}{ a scaling for the results. As most rate tables are in units/day, the default value of 365.25 causes the output to be reported in years. } \item{expect}{ should the output table include the expected number of events, or the expected number of person-years of observation. This is only valid with a rate table. } \item{data.frame}{ return a data frame rather than a set of arrays.} \item{model, x, y}{ If any of these is true, then the model frame, the model matrix, and/or the vector of response times will be returned as components of the final result. } } \value{ a list with components: \item{pyears}{ an array containing the person-years of exposure. (Or other units, depending on the rate table and the scale). The dimension and dimnames of the array correspond to the variables on the right hand side of the model equation. } \item{n}{ an array containing the number of subjects who contribute time to each cell of the \code{pyears} array. } \item{event}{ an array containing the observed number of events. This will be present only if the response variable is a \code{Surv} object. } \item{expected}{ an array containing the expected number of events (or person years if \code{expect ="pyears"}). This will be present only if there was a \code{ratetable} term. } \item{data}{ if the \code{data.frame} option was set, a data frame containing the variables \code{n}, \code{event}, \code{pyears} and \code{event} that supplants the four arrays listed above, along with variables corresponding to each dimension. There will be one row for each cell in the arrays.} \item{offtable}{ the number of person-years of exposure in the cohort that was not part of any cell in the \code{pyears} array. This is often useful as an error check; if there is a mismatch of units between two variables, nearly all the person years may be off table. } \item{tcut}{whether the call included any time-dependent cutpoints.} \item{summary}{ a summary of the rate-table matching. This is also useful as an error check. } \item{call}{ an image of the call to the function. } \item{observations}{the number of observations in the input data set, after any missings were removed.} \item{na.action}{ the \code{na.action} attribute contributed by an \code{na.action} routine, if any. } } \details{ Because \code{pyears} may have several time variables, it is necessary that all of them be in the same units. For instance, in the call \preformatted{ py <- pyears(futime ~ rx, rmap=list(age=age, sex=sex, year=entry.dt), ratetable=survexp.us) } the natural unit of the ratetable is hazard per day, it is important that \code{futime}, \code{age} and \code{entry.dt} all be in days. Given the wide range of possible inputs, it is difficult for the routine to do sanity checks of this aspect. The ratetable being used may have different variable names than the user's data set, this is dealt with by the \code{rmap} argument. The rate table for the above calculation was \code{survexp.us}, a call to \code{summary{survexp.us}} reveals that it expects to have variables \code{age} = age in days, \code{sex}, and \code{year} = the date of study entry, we create them in the \code{rmap} line. The sex variable is not mapped, therefore the code assumes that it exists in \code{mydata} in the correct format. (Note: for factors such as sex, the program will match on any unique abbreviation, ignoring case.) A special function \code{tcut} is needed to specify time-dependent cutpoints. For instance, assume that age is in years, and that the desired final arrays have as one of their margins the age groups 0-2, 2-10, 10-25, and 25+. A subject who enters the study at age 4 and remains under observation for 10 years will contribute follow-up time to both the 2-10 and 10-25 subsets. If \code{cut(age, c(0,2,10,25,100))} were used in the formula, the subject would be classified according to his starting age only. The \code{tcut} function has the same arguments as \code{cut}, but produces a different output object which allows the \code{pyears} function to correctly track the subject. The results of \code{pyears} are normally used as input to further calculations. The \code{print} routine, therefore, is designed to give only a summary of the table. } \seealso{ \code{\link{ratetable}}, \code{\link{survexp}}, \code{\link{Surv}}. } \examples{ # Look at progression rates jointly by calendar date and age # temp.yr <- tcut(mgus$dxyr, 55:92, labels=as.character(55:91)) temp.age <- tcut(mgus$age, 34:101, labels=as.character(34:100)) ptime <- ifelse(is.na(mgus$pctime), mgus$futime, mgus$pctime) pstat <- ifelse(is.na(mgus$pctime), 0, 1) pfit <- pyears(Surv(ptime/365.25, pstat) ~ temp.yr + temp.age + sex, mgus, data.frame=TRUE) # Turn the factor back into numerics for regression tdata <- pfit$data tdata$age <- as.numeric(as.character(tdata$temp.age)) tdata$year<- as.numeric(as.character(tdata$temp.yr)) fit1 <- glm(event ~ year + age+ sex +offset(log(pyears)), data=tdata, family=poisson) \dontrun{ # fit a gam model gfit.m <- gam(y ~ s(age) + s(year) + offset(log(time)), family = poisson, data = tdata) } # Example #2 Create the hearta data frame: hearta <- by(heart, heart$id, function(x)x[x$stop == max(x$stop),]) hearta <- do.call("rbind", hearta) # Produce pyears table of death rates on the surgical arm # The first is by age at randomization, the second by current age fit1 <- pyears(Surv(stop/365.25, event) ~ cut(age + 48, c(0,50,60,70,100)) + surgery, data = hearta, scale = 1) fit2 <- pyears(Surv(stop/365.25, event) ~ tcut(age + 48, c(0,50,60,70,100)) + surgery, data = hearta, scale = 1) fit1$event/fit1$pyears #death rates on the surgery and non-surg arm fit2$event/fit2$pyears #death rates on the surgery and non-surg arm } \keyword{survival} survival/man/tmerge.Rd0000644000176200001440000001544014110720327014453 0ustar liggesusers\name{tmerge} \alias{tmerge} \title{Time based merge for survival data} \description{ A common task in survival analysis is the creation of start,stop data sets which have multiple intervals for each subject, along with the covariate values that apply over that interval. This function aids in the creation of such data sets. } \usage{ tmerge(data1, data2, id,\dots, tstart, tstop, options) } \arguments{ \item{data1}{the primary data set, to which new variables and/or observation will be added} \item{data2}{second data set in which all the other arguments will be found} \item{id}{subject identifier} \item{\dots}{operations that add new variables or intervals, see below} \item{tstart}{optional variable to define the valid time range for each subject, only used on an initial call} \item{tstop}{optional variable to define the valid time range for each subject, only used on an initial call} \item{options}{a list of options. Valid ones are idname, tstartname, tstopname, delay, na.rm, and tdcstart. See the explanation below.} } \details{ The program is often run in multiple passes, the first of which defines the basic structure, and subsequent ones that add new variables to that structure. For a more complete explanation of how this routine works refer to the vignette on time-dependent variables. There are 4 types of operational arguments: a time dependent covariate (tdc), cumulative count (cumtdc), event (event) or cumulative event (cumevent). Time dependent covariates change their values before an event, events are outcomes. \itemize{ \item{newname = tdc(y, x, init)}{ A new time dependent covariate variable will created. The argument \code{y} is assumed to be on the scale of the start and end time, and each instance describes the occurrence of a "condition" at that time. The second argument \code{x} is optional. In the case where \code{x} is missing the count variable starts at 0 for each subject and becomes 1 at the time of the event. If \code{x} is present the value of the time dependent covariate is initialized to value of \code{init}, if present, or the \code{tdcstart} option otherwise, and is updated to the value of \code{x} at each observation. If the option \code{na.rm=TRUE} missing values of \code{x} are first removed, i.e., the update will not create missing values. \item{newname = cumtdc(y,x, init)}{ Similar to tdc, except that the event count is accumulated over time for each subject. The variable \code{x} must be numeric. } \item{newname = event(y,x)}{ Mark an event at time y. In the usual case that \code{x} is missing the new 0/1 variable will be similar to the 0/1 status variable of a survival time. } \item{newname = cumevent(y,x)}{ Cumulative events}. } } The function adds three new variables to the output data set: \code{tstart}, \code{tstop}, and \code{id}. The \code{options} argument can be used to change these names. If, in the first call, the \code{id} argument is a simple name, that variable name will be used as the default for the \code{idname} option. If \code{data1} contains the \code{tstart} variable then that is used as the starting point for the created time intervals, otherwise the initial interval for each id will begin at 0 by default. This will lead to an invalid interval and subsequent error if say a death time were <= 0. The \code{na.rm} option affects creation of time-dependent covariates. Should a data row in \code{data2} that has a missing value for the variable be ignored or should it generate an observation with a value of NA? The default of TRUE causes the last non-missing value to be carried forward. The \code{delay} option causes a time-dependent covariate's new value to be delayed, see the vignette for an example. } \value{a data frame with two extra attributes \code{tm.retain} and \code{tcount}. The first contains the names of the key variables, and which names correspond to tdc or event variables. The tcount variable contains counts of the match types. New time values that occur before the first interval for a subject are "early", those after the last interval for a subject are "late", and those that fall into a gap are of type "gap". All these are are considered to be outside the specified time frame for the given subject. An event of this type will be discarded. An observation in \code{data2} whose identifier matches no rows in \code{data1} is of type "missid" and is also discarded. A time-dependent covariate value will be applied to later intervals but will not generate a new time point in the output. The most common type will usually be "within", corresponding to those new times that fall inside an existing interval and cause it to be split into two. Observations that fall exactly on the edge of an interval but within the (min, max] time for a subject are counted as being on a "leading" edge, "trailing" edge or "boundary". The first corresponds for instance to an occurrence at 17 for someone with an intervals of (0,15] and (17, 35]. A \code{tdc} at time 17 will affect this interval but an \code{event} at 17 would be ignored. An \code{event} occurrence at 15 would count in the (0,15] interval. The last case is where the main data set has touching intervals for a subject, e.g. (17, 28] and (28,35] and a new occurrence lands at the join. Events will go to the earlier interval and counts to the latter one. A last column shows the number of additions where the id and time point were identical. When this occurs, the \code{tdc} and \code{event} operators will use the final value in the data (last edit wins), but ignoring missing, while \code{cumtdc} and \code{cumevent} operators add up the values. These extra attributes are ephemeral and will be discarded if the dataframe is modified. This is intentional, since they will become invalid if for instance a subset were selected. } \author{Terry Therneau} \seealso{\code{\link{neardate}}} \examples{ # The pbc data set contains baseline data and follow-up status # for a set of subjects with primary biliary cirrhosis, while the # pbcseq data set contains repeated laboratory values for those # subjects. # The first data set contains data on 312 subjects in a clinical trial plus # 106 that agreed to be followed off protocol, the second data set has data # only on the trial subjects. temp <- subset(pbc, id <= 312, select=c(id:sex, stage)) # baseline data pbc2 <- tmerge(temp, temp, id=id, endpt = event(time, status)) pbc2 <- tmerge(pbc2, pbcseq, id=id, ascites = tdc(day, ascites), bili = tdc(day, bili), albumin = tdc(day, albumin), protime = tdc(day, protime), alk.phos = tdc(day, alk.phos)) fit <- coxph(Surv(tstart, tstop, endpt==2) ~ protime + log(bili), data=pbc2) } \keyword{ survival } survival/man/survreg.distributions.Rd0000644000176200001440000001032013537676563017606 0ustar liggesusers\name{survreg.distributions} \alias{survreg.distributions} \title{Parametric Survival Distributions} \usage{ survreg.distributions } \description{ List of distributions for accelerated failure models. These are location-scale families for some transformation of time. The entry describes the cdf \eqn{F} and density \eqn{f} of a canonical member of the family. } \format{ There are two basic formats, the first defines a distribution de novo, the second defines a new distribution in terms of an old one. \tabular{ll}{ name:\tab name of distribution\cr variance:\tab function(parms) returning the variance (currently unused)\cr init(x,weights,...):\tab Function returning an initial\cr \tab estimate of the mean and variance \cr \tab (used for initial values in the iteration)\cr density(x,parms):\tab Function returning a matrix with columns \eqn{F}, \eqn{1-F}, \eqn{f}, \eqn{f'/f}, and \eqn{f''/f}\cr quantile(p,parms):\tab Quantile function\cr scale:\tab Optional fixed value for the scale parameter\cr parms:\tab Vector of default values and names for any additional parameters\cr deviance(y,scale,parms):\tab Function returning the deviance for a\cr \tab saturated model; used only for deviance residuals. } and to define one distribution in terms of another \tabular{ll}{ name:\tab name of distribution\cr dist:\tab name of parent distribution\cr trans:\tab transformation (eg log)\cr dtrans:\tab derivative of transformation\cr itrans:\tab inverse of transformation\cr scale:\tab Optional fixed value for scale parameter\cr } } \details{ There are four basic distributions:\code{extreme}, \code{gaussian}, \code{logistic} and \code{t}. The last three are parametrised in the same way as the distributions already present in \R. The extreme value cdf is \deqn{F=1-e^{-e^t}.} When the logarithm of survival time has one of the first three distributions we obtain respectively \code{weibull}, \code{lognormal}, and \code{loglogistic}. The location-scale parameterization of a Weibull distribution found in \code{survreg} is not the same as the parameterization of \code{\link{rweibull}}. The other predefined distributions are defined in terms of these. The \code{exponential} and \code{rayleigh} distributions are Weibull distributions with fixed \code{scale} of 1 and 0.5 respectively, and \code{loggaussian} is a synonym for \code{lognormal}. For speed parts of the three most commonly used distributions are hardcoded in C; for this reason the elements of \code{survreg.distributions} with names of "Extreme value", "Logistic" and "Gaussian" should not be modified. (The order of these in the list is not important, recognition is by name.) As an alternative to modifying \code{survreg.distributions} a new distribution can be specified as a separate list. This is the preferred method of addition and is illustrated below. } \seealso{\code{\link{survreg}}, \code{\link{pweibull}}, \code{\link{pnorm}},\code{\link{plogis}}, \code{\link{pt}}, \code{\link{survregDtest}} } \examples{ # time transformation survreg(Surv(time, status) ~ ph.ecog + sex, dist='weibull', data=lung) # change the transformation to work in years # intercept changes by log(365), everything else stays the same my.weibull <- survreg.distributions$weibull my.weibull$trans <- function(y) log(y/365) my.weibull$itrans <- function(y) 365*exp(y) survreg(Surv(time, status) ~ ph.ecog + sex, lung, dist=my.weibull) # Weibull parametrisation y<-rweibull(1000, shape=2, scale=5) survreg(Surv(y)~1, dist="weibull") # survreg scale parameter maps to 1/shape, linear predictor to log(scale) # Cauchy fit mycauchy <- list(name='Cauchy', init= function(x, weights, ...) c(median(x), mad(x)), density= function(x, parms) { temp <- 1/(1 + x^2) cbind(.5 + atan(x)/pi, .5+ atan(-x)/pi, temp/pi, -2 *x*temp, 2*temp*(4*x^2*temp -1)) }, quantile= function(p, parms) tan((p-.5)*pi), deviance= function(...) stop('deviance residuals not defined') ) survreg(Surv(log(time), status) ~ ph.ecog + sex, lung, dist=mycauchy) } \keyword{survival} survival/man/stanford2.Rd0000644000176200001440000000132013537676563015112 0ustar liggesusers\name{stanford2} \alias{stanford2} \docType{data} \title{More Stanford Heart Transplant data} \description{ This contains the Stanford Heart Transplant data in a different format. The main data set is in \code{\link{heart}}. } \usage{stanford2} \format{ \tabular{ll}{ id: \tab ID number\cr time:\tab survival or censoring time\cr status:\tab censoring status\cr age: \tab in years\cr t5: \tab T5 mismatch score\cr } } \seealso{ \code{\link{predict.survreg}}, \code{\link{heart}} } \source{ LA Escobar and WQ Meeker Jr (1992), Assessing influence in regression analysis with censored data. \emph{Biometrics} \bold{48}, 507--528. Page 519. } \keyword{datasets} \keyword{survival} survival/man/mgus2.Rd0000644000176200001440000000316214013460342014223 0ustar liggesusers\name{mgus2} \alias{mgus2} \docType{data} \title{Monoclonal gammopathy data} \description{Natural history of 1341 sequential patients with monoclonal gammopathy of undetermined significance (MGUS). This is a superset of the \code{mgus} data, at a later point in the accrual process } \usage{mgus2 data(cancer, package="survival") } \format{ A data frame with 1384 observations on the following 10 variables. \describe{ \item{\code{id}}{subject identifier} \item{\code{age}}{age at diagnosis, in years} \item{\code{sex}}{a factor with levels \code{F} \code{M}} \item{\code{dxyr}}{year of diagnosis} \item{\code{hgb}}{hemoglobin} \item{\code{creat}}{creatinine} \item{\code{mspike}}{size of the monoclonal serum splike} \item{\code{ptime}}{time until progression to a plasma cell malignancy (PCM) or last contact, in months} \item{\code{pstat}}{occurrence of PCM: 0=no, 1=yes } \item{\code{futime}}{time until death or last contact, in months} \item{\code{death}}{occurrence of death: 0=no, 1=yes} } } \details{ This is an extension of the study found in the \code{mgus} data set, containing enrollment through 1994 and follow-up through 1999. } \source{Mayo Clinic data courtesy of Dr. Robert Kyle. All patient identifiers have been removed, age rounded to the nearest year, and follow-up times rounded to the nearest month.} \references{ R. Kyle, T. Therneau, V. Rajkumar, J. Offord, D. Larson, M. Plevak, and L. J. Melton III, A long-terms study of prognosis in monoclonal gammopathy of undertermined significance. New Engl J Med, 346:564-569 (2002). } \keyword{datasets} survival/man/untangle.specials.Rd0000644000176200001440000000232114110720327016601 0ustar liggesusers\name{untangle.specials} \alias{untangle.specials} \title{ Help Process the `specials' Argument of the `terms' Function. } \description{ Given a \code{terms} structure and a desired special name, this returns an index appropriate for subscripting the \code{terms} structure and another appropriate for the data frame. } \usage{ untangle.specials(tt, special, order=1) } \arguments{ \item{tt}{ a \code{terms} object. } \item{special}{ the name of a special function, presumably used in the terms object. } \item{order}{ the order of the desired terms. If set to 2, interactions with the special function will be included. }} \value{ a list with two components: \item{vars}{ a vector of variable names, as would be found in the data frame, of the specials. } \item{terms}{ a numeric vector, suitable for subscripting the terms structure, that indexes the terms in the expanded model formula which involve the special. }} \examples{ formula <- Surv(tt,ss) ~ x + z*strata(id) tms <- terms(formula, specials="strata") ## the specials attribute attr(tms, "specials") ## main effects untangle.specials(tms, "strata") ## and interactions untangle.specials(tms, "strata", order=1:2) } \keyword{survival} % Converted by Sd2Rd version 0.3-2. survival/man/cgd.Rd0000644000176200001440000000307414013461216013726 0ustar liggesusers\name{cgd} \docType{data} \alias{cgd} \alias{cgd.raw} \title{Chronic Granulotamous Disease data} \description{Data are from a placebo controlled trial of gamma interferon in chronic granulotomous disease (CGD). Contains the data on time to serious infections observed through end of study for each patient. } \usage{cgd data(cgd) } \format{ \describe{ \item{id}{subject identification number} \item{center}{enrolling center } \item{random}{date of randomization } \item{treatment}{placebo or gamma interferon } \item{sex}{sex} \item{age}{age in years, at study entry } \item{height}{height in cm at study entry} \item{weight}{weight in kg at study entry} \item{inherit}{pattern of inheritance } \item{steroids}{use of steroids at study entry,1=yes} \item{propylac}{use of prophylactic antibiotics at study entry} \item{hos.cat}{a categorization of the centers into 4 groups} \item{tstart, tstop}{start and end of each time interval } \item{status}{1=the interval ends with an infection } \item{enum}{observation number within subject} } } \details{ The \code{cgd0} data set is in the form found in the references, with one line per patient and no recoding of the variables. The \code{cgd} data set (this one) has been cast into (start, stop] format with one line per event, and covariates such as center recoded as factors to include meaningful labels. } \source{ Fleming and Harrington, Counting Processes and Survival Analysis, appendix D.2. } \seealso{\code{link{cgd0}}} \keyword{datasets} \keyword{survival} survival/man/kidney.Rd0000644000176200001440000000271214013460244014452 0ustar liggesusers\name{kidney} \alias{kidney} \title{Kidney catheter data} \description{ Data on the recurrence times to infection, at the point of insertion of the catheter, for kidney patients using portable dialysis equipment. Catheters may be removed for reasons other than infection, in which case the observation is censored. Each patient has exactly 2 observations. This data has often been used to illustrate the use of random effects (frailty) in a survival model. However, one of the males (id 21) is a large outlier, with much longer survival than his peers. If this observation is removed no evidence remains for a random subject effect. } \usage{kidney data(cancer, package="survival") } \format{ \tabular{ll}{ patient:\tab id\cr time:\tab time\cr status:\tab event status\cr age:\tab in years\cr sex:\tab 1=male, 2=female\cr disease:\tab disease type (0=GN, 1=AN, 2=PKD, 3=Other)\cr frail:\tab frailty estimate from original paper\cr }} \section{Note}{ The original paper ignored the issue of tied times and so is not exactly reproduced by the survival package. } \examples{ kfit <- coxph(Surv(time, status)~ age + sex + disease + frailty(id), kidney) kfit0 <- coxph(Surv(time, status)~ age + sex + disease, kidney) kfitm1 <- coxph(Surv(time,status) ~ age + sex + disease + frailty(id, dist='gauss'), kidney) } \source{ CA McGilchrist, CW Aisbett (1991), Regression with frailty in survival analysis. \emph{Biometrics} \bold{47}, 461--66. } \keyword{survival} survival/man/logan.Rd0000644000176200001440000000167014013461413014270 0ustar liggesusers\name{logan} \docType{data} \alias{logan} \title{Data from the 1972-78 GSS data used by Logan} \usage{logan data(logan, package="survival") } \description{ Intergenerational occupational mobility data with covariates. } \format{ A data frame with 838 observations on the following 4 variables. \describe{ \item{occupation}{subject's occupation, a factor with levels \code{farm}, \code{operatives}, \code{craftsmen}, \code{sales}, and \code{professional}} \item{focc}{father's occupation} \item{education}{total years of schooling, 0 to 20} \item{race}{levels of \code{non-black} and \code{black}} } } \source{ General Social Survey data, see the web site for detailed information on the variables. \url{https://gss.norc.org/}. } \references{ Logan, John A. (1983). A Multivariate Model for Mobility Tables. \cite{American Journal of Sociology} 89: 324-349.} \keyword{datasets} survival/man/cluster.Rd0000644000176200001440000000220013640437051014645 0ustar liggesusers\name{cluster} \alias{cluster} \title{ Identify clusters. } \description{ This is a special function used in the context of survival models. It identifies correlated groups of observations, and is used on the right hand side of a formula. This style is now discouraged, use the \code{cluster} option instead. } \usage{ cluster(x) } \arguments{ \item{x}{ A character, factor, or numeric variable. } } \value{ \code{x} } \details{ The function's only action is semantic, to mark a variable as the cluster indicator. The resulting variance is what is known as the ``working independence'' variance in a GEE model. Note that one cannot use both a frailty term and a cluster term in the same model, the first is a mixed-effects approach to correlation and the second a GEE approach, and these don't mix. } \seealso{ \code{\link{coxph}}, \code{\link{survreg}} } \examples{ marginal.model <- coxph(Surv(time, status) ~ rx, data= rats, cluster=litter, subset=(sex=='f')) frailty.model <- coxph(Surv(time, status) ~ rx + frailty(litter), rats, subset=(sex=='f')) } \keyword{survival} survival/man/survexp.Rd0000644000176200001440000002030713537676563014732 0ustar liggesusers\name{survexp} \alias{survexp} \alias{print.survexp} \title{ Compute Expected Survival } \description{ Returns either the expected survival of a cohort of subjects, or the individual expected survival for each subject. } \usage{ survexp(formula, data, weights, subset, na.action, rmap, times, method=c("ederer", "hakulinen", "conditional", "individual.h", "individual.s"), cohort=TRUE, conditional=FALSE, ratetable=survival::survexp.us, scale=1, se.fit, model=FALSE, x=FALSE, y=FALSE) } \arguments{ \item{formula}{ formula object. The response variable is a vector of follow-up times and is optional. The predictors consist of optional grouping variables separated by the \code{+} operator (as in \code{survfit}), and is often \code{~1}, i.e., expected survival for the entire group. } \item{data}{ data frame in which to interpret the variables named in the \code{formula}, \code{subset} and \code{weights} arguments. } \item{weights}{ case weights. This is most useful when conditional survival for a known population is desired, e.g., the data set would contain all unique age/sex combinations and the weights would be the proportion of each. } \item{subset}{ expression indicating a subset of the rows of \code{data} to be used in the fit. } \item{na.action}{ function to filter missing data. This is applied to the model frame after \code{subset} has been applied. Default is \code{options()$na.action}. } \item{rmap}{ an optional list that maps data set names to the ratetable names. See the details section below. } \item{times}{ vector of follow-up times at which the resulting survival curve is evaluated. If absent, the result will be reported for each unique value of the vector of times supplied in the response value of the \code{formula}. } \item{method}{computational method for the creating the survival curves. The \code{individual} option does not create a curve, rather it retrieves the predicted survival \code{individual.s} or cumulative hazard \code{individual.h} for each subject. The default is to use \code{method='ederer'} if the formula has no response, and \code{method='hakulinen'} otherwise.} \item{cohort}{logical value. This argument has been superseded by the \code{method} argument. To maintain backwards compatability, if is present and FALSE, it implies \code{method='individual.s'}.} \item{conditional}{logical value. This argument has been superseded by the \code{method} argument. To maintain backwards compatability, if it is present and TRUE it implies \code{method='conditional'}.} \item{ratetable}{ a table of event rates, such as \code{survexp.mn}, or a fitted Cox model. Note the \code{survival::} prefix in the default argument is present to avoid the (rare) case of a user who expects the default table but just happens to have an object named "survexp.us" in their own directory.} \item{scale}{ numeric value to scale the results. If \code{ratetable} is in units/day, \code{scale = 365.25} causes the output to be reported in years. } \item{se.fit}{ compute the standard error of the predicted survival. This argument is currently ignored. Standard errors are not a defined concept for population rate tables (they are treated as coming from a complete census), and for Cox models the calculation is hard. Despite good intentions standard errors for this latter case have not been coded and validated. } \item{model,x,y}{ flags to control what is returned. If any of these is true, then the model frame, the model matrix, and/or the vector of response times will be returned as components of the final result, with the same names as the flag arguments. }} \value{ if \code{cohort=TRUE} an object of class \code{survexp}, otherwise a vector of per-subject expected survival values. The former contains the number of subjects at risk and the expected survival for the cohort at each requested time. The cohort survival is the hypothetical survival for a cohort of subjects enrolled from the population at large, but matching the data set on the factors found in the rate table. } \details{ Individual expected survival is usually used in models or testing, to `correct' for the age and sex composition of a group of subjects. For instance, assume that birth date, entry date into the study, sex and actual survival time are all known for a group of subjects. The \code{survexp.us} population tables contain expected death rates based on calendar year, sex and age. Then \preformatted{ haz <- survexp(fu.time ~ 1, data=mydata, rmap = list(year=entry.dt, age=(birth.dt-entry.dt)), method='individual.h')) } gives for each subject the total hazard experienced up to their observed death time or last follow-up time (variable fu.time) This probability can be used as a rescaled time value in models: \preformatted{ glm(status ~ 1 + offset(log(haz)), family=poisson) glm(status ~ x + offset(log(haz)), family=poisson) } In the first model, a test for intercept=0 is the one sample log-rank test of whether the observed group of subjects has equivalent survival to the baseline population. The second model tests for an effect of variable \code{x} after adjustment for age and sex. The ratetable being used may have different variable names than the user's data set, this is dealt with by the \code{rmap} argument. The rate table for the above calculation was \code{survexp.us}, a call to \code{summary{survexp.us}} reveals that it expects to have variables \code{age} = age in days, \code{sex}, and \code{year} = the date of study entry, we create them in the \code{rmap} line. The sex variable was not mapped, therefore the function assumes that it exists in \code{mydata} in the correct format. (Note: for factors such as sex, the program will match on any unique abbreviation, ignoring case.) Cohort survival is used to produce an overall survival curve. This is then added to the Kaplan-Meier plot of the study group for visual comparison between these subjects and the population at large. There are three common methods of computing cohort survival. In the "exact method" of Ederer the cohort is not censored, for this case no response variable is required in the formula. Hakulinen recommends censoring the cohort at the anticipated censoring time of each patient, and Verheul recommends censoring the cohort at the actual observation time of each patient. The last of these is the conditional method. These are obtained by using the respective time values as the follow-up time or response in the formula. } \references{ Berry, G. (1983). The analysis of mortality by the subject-years method. \emph{Biometrics}, 39:173-84. Ederer, F., Axtell, L. and Cutler, S. (1961). The relative survival rate: a statistical methodology. \emph{Natl Cancer Inst Monogr}, 6:101-21. Hakulinen, T. (1982). Cancer survival corrected for heterogeneity in patient withdrawal. \emph{Biometrics}, 38:933-942. Therneau, T. and Grambsch, P. (2000). Modeling survival data: Extending the Cox model. Springer. Chapter 10. Verheul, H., Dekker, E., Bossuyt, P., Moulijn, A. and Dunning, A. (1993). Background mortality in clinical survival studies. \emph{Lancet}, 341: 872-875. } \seealso{ \code{\link{survfit}}, \code{\link{pyears}}, \code{\link{survexp.us}}, \code{\link{ratetable}}, \code{\link{survexp.fit}}. } \examples{ # # Stanford heart transplant data # We don't have sex in the data set, but know it to be nearly all males. # Estimate of conditional survival fit1 <- survexp(futime ~ 1, rmap=list(sex="male", year=accept.dt, age=(accept.dt-birth.dt)), method='conditional', data=jasa) summary(fit1, times=1:10*182.5, scale=365) #expected survival by 1/2 years # Estimate of expected survival stratified by prior surgery survexp(~ surgery, rmap= list(sex="male", year=accept.dt, age=(accept.dt-birth.dt)), method='ederer', data=jasa, times=1:10 * 182.5) ## Compare the survival curves for the Mayo PBC data to Cox model fit ## pfit <-coxph(Surv(time,status>0) ~ trt + log(bili) + log(protime) + age + platelet, data=pbc) plot(survfit(Surv(time, status>0) ~ trt, data=pbc), mark.time=FALSE) lines(survexp( ~ trt, ratetable=pfit, data=pbc), col='purple') } \keyword{survival} survival/man/nwtco.Rd0000644000176200001440000000231414013461504014317 0ustar liggesusers\name{nwtco} \alias{nwtco} \docType{data} \title{Data from the National Wilm's Tumor Study} \description{ Measurement error example. Tumor histology predicts survival, but prediction is stronger with central lab histology than with the local institution determination. } \usage{nwtco data(nwtco, package="survival") } \format{ A data frame with 4028 observations on the following 9 variables. \describe{ \item{\code{seqno}}{id number} \item{\code{instit}}{Histology from local institution} \item{\code{histol}}{Histology from central lab} \item{\code{stage}}{Disease stage} \item{\code{study}}{study} \item{\code{rel}}{indicator for relapse} \item{\code{edrel}}{time to relapse} \item{\code{age}}{age in months} \item{\code{in.subcohort}}{Included in the subcohort for the example in the paper} } } \references{ NE Breslow and N Chatterjee (1999), Design and analysis of two-phase studies with binary outcome applied to Wilms tumour prognosis. \emph{Applied Statistics} \bold{48}, 457--68. } \examples{ with(nwtco, table(instit,histol)) anova(coxph(Surv(edrel,rel)~histol+instit,data=nwtco)) anova(coxph(Surv(edrel,rel)~instit+histol,data=nwtco)) } \keyword{datasets} survival/man/residuals.survreg.Rd0000644000176200001440000000651413537676563016711 0ustar liggesusers\name{residuals.survreg} \alias{residuals.survreg} \alias{residuals.survreg.penal} \title{Compute Residuals for `survreg' Objects} \description{ This is a method for the function \code{\link{residuals}} for objects inheriting from class \code{survreg}. } \usage{ \method{residuals}{survreg}(object, type=c("response", "deviance","dfbeta","dfbetas", "working","ldcase","ldresp","ldshape", "matrix"), rsigma=TRUE, collapse=FALSE, weighted=FALSE, ...) } \arguments{ \item{object}{ an object inheriting from class \code{survreg}. } \item{type}{ type of residuals, with choices of \code{"response"}, \code{"deviance"}, \code{"dfbeta"}, \code{"dfbetas"}, \code{"working"}, \code{"ldcase"}, \code{"lsresp"}, \code{"ldshape"}, and \code{"matrix"}. } \item{rsigma}{ include the scale parameters in the variance matrix, when doing computations. (I can think of no good reason not to). } \item{collapse}{ optional vector of subject groups. If given, this must be of the same length as the residuals, and causes the result to be per group residuals. } \item{weighted}{ give weighted residuals? Normally residuals are unweighted. }\item{...}{other unused arguments}} \value{ A vector or matrix of residuals is returned. Response residuals are on the scale of the original data, working residuals are on the scale of the linear predictor, and deviance residuals are on log-likelihood scale. The dfbeta residuals are a matrix, where the ith row gives the approximate change in the coefficients due to the addition of subject i. The dfbetas matrix contains the dfbeta residuals, with each column scaled by the standard deviation of that coefficient. The matrix type produces a matrix based on derivatives of the log-likelihood function. Let \eqn{L} be the log-likelihood, \eqn{p} be the linear predictor \eqn{X\beta}{X \%*\% coef}, and \eqn{s} be \eqn{\log(\sigma)}. Then the 6 columns of the matrix are \eqn{L}, \eqn{dL/dp},\eqn{\partial^2L/\partial p^2}{ddL/(dp dp)}, \eqn{dL/ds}, \eqn{\partial^2L/\partial s^2}{ddL/(ds ds)} and \eqn{\partial^2L/\partial p\partial s}{ddL/(dp ds)}. Diagnostics based on these quantities are discussed in the book and article by Escobar and Meeker. The main ones are the likelihood displacement residuals for perturbation of a case weight (\code{ldcase}), the response value (\code{ldresp}), and the \code{shape}. For a transformed distribution such as the log-normal or Weibull, matrix residuals are based on the log-likelihood of the transformed data log(y). For a monotone function f the density of f(X) is the density of X divided by the derivative of f (the Jacobian), so subtract log(derivative) from each uncensored observation's loglik value in order to match the \code{loglik} component of the result. The other colums of the matrix residual are unchanged by the transformation. } \references{ Escobar, L. A. and Meeker, W. Q. (1992). Assessing influence in regression analysis with censored data. \emph{Biometrics} \bold{48}, 507-528. Escobar, L. A. and Meeker, W. Q. (1998). Statistical Methods for Reliablilty Data. Wiley. } \seealso{\code{\link{predict.survreg}}} \examples{ fit <- survreg(Surv(futime, death) ~ age + sex, mgus2) summary(fit) # age and sex are both important rr <- residuals(fit, type='matrix') sum(rr[,1]) - with(mgus2, sum(log(futime[death==1]))) # loglik plot(mgus2$age, rr[,2], col= (1+mgus2$death)) # ldresp } \keyword{survival} survival/man/survreg.control.Rd0000644000176200001440000000161713537676563016375 0ustar liggesusers\name{survreg.control} \alias{survreg.control} %- Also NEED an `\alias' for EACH other topic documented here. \title{Package options for survreg and coxph} \description{ This functions checks and packages the fitting options for \code{\link{survreg}} } \usage{ survreg.control(maxiter=30, rel.tolerance=1e-09, toler.chol=1e-10, iter.max, debug=0, outer.max=10) } %- maybe also `usage' for other objects documented here. \arguments{ \item{maxiter}{maximum number of iterations } \item{rel.tolerance}{relative tolerance to declare convergence } \item{toler.chol}{Tolerance to declare Cholesky decomposition singular} \item{iter.max}{same as \code{maxiter}} \item{debug}{print debugging information} \item{outer.max}{maximum number of outer iterations for choosing penalty parameters} } \value{ A list with the same elements as the input } \seealso{ \code{\link{survreg}}} \keyword{survival} survival/man/survobrien.Rd0000644000176200001440000000567213537676563015424 0ustar liggesusers\name{survobrien} \alias{survobrien} \title{ O'Brien's Test for Association of a Single Variable with Survival } \description{ Peter O'Brien's test for association of a single variable with survival This test is proposed in Biometrics, June 1978. } \usage{ survobrien(formula, data, subset, na.action, transform) } \arguments{ \item{formula}{ a valid formula for a cox model. } \item{data}{ a data.frame in which to interpret the variables named in the \code{formula}, or in the \code{subset} and the \code{weights} argument. } \item{subset}{ expression indicating which subset of the rows of data should be used in the fit. All observations are included by default. } \item{na.action}{ a missing-data filter function. This is applied to the model.frame after any subset argument has been used. Default is \code{options()\$na.action}. } \item{transform}{the transformation function to be applied at each time point. The default is O'Brien's suggestion logit(tr) where tr = (rank(x)- 1/2)/ length(x) is the rank shifted to the range 0-1 and logit(x) = log(x/(1-x)) is the logit transform. }} \value{ a new data frame. The response variables will be column names returned by the \code{Surv} function, i.e., "time" and "status" for simple survival data, or "start", "stop", "status" for counting process data. Each individual event time is identified by the value of the variable \code{.strata.}. Other variables retain their original names. If a predictor variable is a factor or is protected with \code{I()}, it is retained as is. Other predictor variables have been replaced with time-dependent logit scores. The new data frame will have many more rows that the original data, approximately the original number of rows * number of deaths/2. } \section{Method}{ A time-dependent cox model can now be fit to the new data. The univariate statistic, as originally proposed, is equivalent to single variable score tests from the time-dependent model. This equivalence is the rationale for using the time dependent model as a multivariate extension of the original paper. In O'Brien's method, the x variables are re-ranked at each death time. A simpler method, proposed by Prentice, ranks the data only once at the start. The results are usually similar. } \references{ O'Brien, Peter, "A Nonparametric Test for Association with Censored Data", \emph{Biometrics} 34: 243-250, 1978. } \note{ A prior version of the routine returned new time variables rather than a strata. Unfortunately, that strategy does not work if the original formula has a strata statement. This new data set will be the same size, but the \code{coxph} routine will process it slightly faster. } \seealso{ \code{\link{survdiff}} } \keyword{survival} \examples{ xx <- survobrien(Surv(futime, fustat) ~ age + factor(rx) + I(ecog.ps), data=ovarian) coxph(Surv(time, status) ~ age + strata(.strata.), data=xx) } survival/man/Surv2data.Rd0000644000176200001440000000304513646411264015053 0ustar liggesusers\name{Surv2data} \alias{Surv2data} \title{Convert data from timecourse to (time1,time2) style } \description{ The multi-state survival functions \code{coxph} and \code{survfit} allow for two forms of input data. This routine converts between them. The function is normally called behind the scenes when \code{Surv2} is as the response. } \usage{ Surv2data(formula, data, subset, id) } \arguments{ \item{formula}{a model formula} \item{data}{a data frame} \item{subset}{optional, selects rows of the data to be retained} \item{id}{a variable that identified multiple rows for the same subject, normally found in the referenced data set} } \value{ a list with elements \item{mf}{an updated model frame (fewer rows, unchanged columns)} \item{S2.y}{the constructed response variable} \item{S2.state}{the current state for each of the rows} } \details{ For timeline style data, each row is uniquely identified by an (identifier, time) pair. The time could be a date, time from entry to a study, age, etc, (there may often be more than one time variable). The identifier and time cannot be missing. The remaining covariates represent values that were observed at that time point. Often, a given covariate is observed at only a subset of times and is missing at others. At the time of death, in particular, often only the identifier, time, and status indicator are known. In the resulting data set missing covariates are replaced by their last known value, and the response y will be a Surv(time1, time2, endpoint) object. } \keyword{survival} survival/man/anova.coxph.Rd0000644000176200001440000000425513741315222015421 0ustar liggesusers\name{anova.coxph} \alias{anova.coxph} \alias{anova.coxphlist} \title{Analysis of Deviance for a Cox model.} \usage{ \method{anova}{coxph}(object, \dots, test = 'Chisq') } \description{ Compute an analysis of deviance table for one or more Cox model fits, based on the log partial likelihood. } \arguments{ \item{object}{An object of class \code{coxph}} \item{\dots}{Further \code{coxph} objects} \item{test}{a character string. The appropriate test is a chisquare, all other choices result in no test being done.} } \details{ Specifying a single object gives a sequential analysis of deviance table for that fit. That is, the reductions in the model Cox log-partial-likelihood as each term of the formula is added in turn are given in as the rows of a table, plus the log-likelihoods themselves. A robust variance estimate is normally used in situations where the model may be mis-specified, e.g., multiple events per subject. In this case a comparison of likelihood values does not make sense (differences no longer have a chi-square distribution), and \code{anova} will refuse to print results. If more than one object is specified, the table has a row for the degrees of freedom and loglikelihood for each model. For all but the first model, the change in degrees of freedom and loglik is also given. (This only make statistical sense if the models are nested.) It is conventional to list the models from smallest to largest, but this is up to the user. The table will optionally contain test statistics (and P values) comparing the reduction in loglik for each row. } \value{ An object of class \code{"anova"} inheriting from class \code{"data.frame"}. } \section{Warning}{ The comparison between two or more models by \code{anova} will only be valid if they are fitted to the same dataset. This may be a problem if there are missing values.} \seealso{ \code{\link{coxph}}, \code{\link{anova}}. } \examples{ fit <- coxph(Surv(futime, fustat) ~ resid.ds *rx + ecog.ps, data = ovarian) anova(fit) fit2 <- coxph(Surv(futime, fustat) ~ resid.ds +rx + ecog.ps, data=ovarian) anova(fit2,fit) } \keyword{models} \keyword{regression} \keyword{survival} survival/man/survfit.Rd0000644000176200001440000000457514041423076014705 0ustar liggesusers\name{survfit} \alias{survfit} \title{Create survival curves} \description{ This function creates survival curves from either a formula (e.g. the Kaplan-Meier), a previously fitted Cox model, or a previously fitted accelerated failure time model. } \usage{ survfit(formula, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{either a formula or a previously fitted model} \item{\dots}{other arguments to the specific method} } \details{ A survival curve is based on a tabulation of the number at risk and number of events at each unique death time. When time is a floating point number the definition of "unique" is subject to interpretation. The code uses factor() to define the set. For further details see the documentation for the appropriate method, i.e., \code{?survfit.formula} or \code{?survfit.coxph}. A survfit object may contain a single curve, a set of curves, or a matrix curves. Predicted curves from a \code{coxph} model have one row for each stratum in the Cox model fit and one column for each specified covariate set. Curves from a multi-state model have one row for each stratum and a column for each state, the strata correspond to predictors on the right hand side of the equation. The default printing and plotting order for curves is by column, as with other matrices. Curves can be subscripted using either a single or double subscript. If the set of curves is a matrix, as in the above, and one of the dimensions is 1 then the code allows a single subscript to be used. (That is, it is not quite as general as using a single subscript for a numeric matrix.) } \value{ An object of class \code{survfit} containing one or more survival curves. } \author{Terry Therneau} \note{Older releases of the code also allowed the specification for a single curve to omit the right hand of the formula, i.e., \code{survfit(Surv(time, status))}, in which case the formula argument is not actually a formula. Handling this case required some non-standard and fairly fragile manipulations, and this case is no longer supported. } \seealso{\code{\link{survfit.formula}}, \code{\link{survfit.coxph}}, \code{\link{survfit.object}}, \code{\link{print.survfit}}, \code{\link{plot.survfit}}, \code{\link{quantile.survfit}}, \code{\link{residuals.survfit}}, \code{\link{summary.survfit}} } \keyword{ survival} survival/man/survival-internal.Rd0000644000176200001440000000507714012574772016676 0ustar liggesusers\name{survival-internal} \alias{survival-internal} \alias{agexact.fit} \alias{as.matrix.ratetable} \alias{coxpenal.df} \alias{coxpenal.fit} \alias{is.na.coxph.penalty} \alias{match.ratetable} \alias{survfitCI} \alias{survfitKM} \alias{survreg.fit} \alias{survpenal.fit} \alias{survdiff.fit} \alias{[.coxph.penalty} \title{Internal survival functions} \description{Internal survival functions} \usage{ survreg.fit(x, y, weights, offset, init, controlvals, dist, scale = 0, nstrat = 1, strata, parms = NULL,assign) survpenal.fit(x, y, weights, offset, init, controlvals, dist, scale = 0, nstrat = 1, strata, pcols, pattr, assign, parms = NULL) survdiff.fit(y, x, strat, rho = 0) match.ratetable(R, ratetable) \method{as.matrix}{ratetable}(x, ...) \method{is.na}{coxph.penalty}(x) coxpenal.df(hmat, hinv, fdiag, assign.list, ptype, nvar, pen1, pen2, sparse) coxpenal.fit(x, y, strata, offset, init, control, weights, method, rownames, pcols, pattr, assign, nocenter) coxph.wtest(var, b, toler.chol = 1e-09) agexact.fit(x, y, strata, offset, init, control, weights, method, rownames, resid=TRUE, nocenter=NULL) survfitCI(X, Y, weights, id, cluster, robust, istate, stype=1, ctype=1, se.fit=TRUE, conf.int= .95, conf.type=c('log', 'log-log', 'plain', 'none', 'logit', 'arcsin'), conf.lower=c('usual', 'peto', 'modified'), influence=FALSE, start.time, p0, type) survfitKM(x, y, weights=rep(1,length(x)), stype=1, ctype=1, se.fit=TRUE, conf.int= .95, conf.type=c('log', 'log-log', 'plain', 'none', 'logit', 'arcsin'), conf.lower=c('usual', 'peto', 'modified'), start.time, id, cluster, robust, influence=FALSE, type) survfitTurnbull(x, y, weights, type=c('kaplan-meier', 'fleming-harrington', 'fh2'), error=c('greenwood', "tsiatis"), se.fit=TRUE, conf.int= .95, conf.type=c('log', 'log-log', 'plain', 'none', 'logit', 'arcsin'), conf.lower=c('usual', 'peto', 'modified'), start.time) } \details{The arguments to these routines are not guaranteed to stay the same from release to release -- call them at your own risk!} \keyword{survival} \keyword{internal} survival/man/coxph.Rd0000644000176200001440000003272514012555342014322 0ustar liggesusers\name{coxph} \alias{coxph} \alias{print.coxph.null} \alias{print.coxph.penal} \alias{coxph.penalty} \alias{coxph.getdata} \alias{summary.coxph.penal} \title{ Fit Proportional Hazards Regression Model } \description{ Fits a Cox proportional hazards regression model. Time dependent variables, time dependent strata, multiple events per subject, and other extensions are incorporated using the counting process formulation of Andersen and Gill. } \usage{ coxph(formula, data=, weights, subset, na.action, init, control, ties=c("efron","breslow","exact"), singular.ok=TRUE, robust, model=FALSE, x=FALSE, y=TRUE, tt, method=ties, id, cluster, istate, statedata, nocenter=c(-1, 0, 1), ...) } \arguments{ \item{formula}{ a formula object, with the response on the left of a \code{~} operator, and the terms on the right. The response must be a survival object as returned by the \code{Surv} function. } \item{data}{ a data.frame in which to interpret the variables named in the \code{formula}, or in the \code{subset} and the \code{weights} argument. } \item{weights}{ vector of case weights, see the note below. For a thorough discussion of these see the book by Therneau and Grambsch. } \item{subset}{ expression indicating which subset of the rows of data should be used in the fit. All observations are included by default. } \item{na.action}{ a missing-data filter function. This is applied to the model.frame after any subset argument has been used. Default is \code{options()\$na.action}. } \item{init}{ vector of initial values of the iteration. Default initial value is zero for all variables. } \item{control}{ Object of class \code{\link{coxph.control}} specifying iteration limit and other control options. Default is \code{coxph.control(...)}. } \item{ties}{ a character string specifying the method for tie handling. If there are no tied death times all the methods are equivalent. Nearly all Cox regression programs use the Breslow method by default, but not this one. The Efron approximation is used as the default here, it is more accurate when dealing with tied death times, and is as efficient computationally. The ``exact partial likelihood'' is equivalent to a conditional logistic model, and is appropriate when the times are a small set of discrete values. See further below. } \item{singular.ok}{ logical value indicating how to handle collinearity in the model matrix. If \code{TRUE}, the program will automatically skip over columns of the X matrix that are linear combinations of earlier columns. In this case the coefficients for such columns will be NA, and the variance matrix will contain zeros. For ancillary calculations, such as the linear predictor, the missing coefficients are treated as zeros. } \item{robust}{should a robust variance be computed. The default is TRUE if: there is a \code{cluster} argument, there are case weights that are not 0 or 1, or there are \code{id} values with more than one event. } \item{id}{optional variable name that identifies subjects. Only necessary when a subject can have multiple rows in the data, and there is more than one event type. This variable will normally be found in \code{data}.} \item{cluster}{optional variable which clusters the observations, for the purposes of a robust variance. If present, it implies \code{robust}. This variable will normally be found in \code{data}.} \item{istate}{optional variable giving the current state at the start each interval. This variable will normally be found in \code{data}.} \item{statedata}{optional data set used to describe multistate models.} \item{model}{ logical value: if \code{TRUE}, the model frame is returned in component \code{model}. } \item{x}{ logical value: if \code{TRUE}, the x matrix is returned in component \code{x}. } \item{y}{ logical value: if \code{TRUE}, the response vector is returned in component \code{y}. } \item{tt}{optional list of time-transform functions.} \item{method}{alternate name for the \code{ties} argument.} \item{nocenter}{columns of the X matrix whose values lie strictly within this set are not recentered} \item{...}{Other arguments will be passed to \code{\link{coxph.control}} } } \value{ an object of class \code{coxph} representing the fit. See \code{coxph.object} for details. } \section{Side Effects}{ Depending on the call, the \code{predict}, \code{residuals}, and \code{survfit} routines may need to reconstruct the x matrix created by \code{coxph}. It is possible for this to fail, as in the example below in which the predict function is unable to find \code{tform}. \preformatted{ tfun <- function(tform) coxph(tform, data=lung) fit <- tfun(Surv(time, status) ~ age) predict(fit)} In such a case add the \code{model=TRUE} option to the \code{coxph} call to obviate the need for reconstruction, at the expense of a larger \code{fit} object. } \details{ The proportional hazards model is usually expressed in terms of a single survival time value for each person, with possible censoring. Andersen and Gill reformulated the same problem as a counting process; as time marches onward we observe the events for a subject, rather like watching a Geiger counter. The data for a subject is presented as multiple rows or "observations", each of which applies to an interval of observation (start, stop]. The routine internally scales and centers data to avoid overflow in the argument to the exponential function. These actions do not change the result, but lead to more numerical stability. Any column of the X matrix whose values lie within \code{nocenter} list are not recentered. The practical consequence of the default is to not recenter dummy variables corresponding to factors. However, arguments to offset are not scaled since there are situations where a large offset value is a purposefully used. In general, however, users should not avoid very large numeric values for an offset due to possible loss of precision in the estimates. } \section{Case weights}{ Case weights are treated as replication weights, i.e., a case weight of 2 is equivalent to having 2 copies of that subject's observation. When computers were much smaller grouping like subjects together was a common trick to used to conserve memory. Setting all weights to 2 for instance will give the same coefficient estimate but halve the variance. When the Efron approximation for ties (default) is employed replication of the data will not give exactly the same coefficients as the weights option, and in this case the weighted fit is arguably the correct one. When the model includes a \code{cluster} term or the \code{robust=TRUE} option the computed variance treats any weights as sampling weights; setting all weights to 2 will in this case give the same variance as weights of 1. } \section{Special terms}{ There are three special terms that may be used in the model equation. A \code{strata} term identifies a stratified Cox model; separate baseline hazard functions are fit for each strata. The \code{cluster} term is used to compute a robust variance for the model. The term \code{+ cluster(id)} where each value of \code{id} is unique is equivalent to specifying the \code{robust=TRUE} argument. If the \code{id} variable is not unique, it is assumed that it identifies clusters of correlated observations. The robust estimate arises from many different arguments and thus has had many labels. It is variously known as the Huber sandwich estimator, White's estimate (linear models/econometrics), the Horvitz-Thompson estimate (survey sampling), the working independence variance (generalized estimating equations), the infinitesimal jackknife, and the Wei, Lin, Weissfeld (WLW) estimate. A time-transform term allows variables to vary dynamically in time. In this case the \code{tt} argument will be a function or a list of functions (if there are more than one tt() term in the model) giving the appropriate transform. See the examples below. One user mistake that has recently arisen is to slavishly follow the advice of some coding guides and prepend \code{survival::} onto everthing, including the special terms, e.g., \code{survival::coxph(survival:Surv(time, status) ~ age + survival::cluster(inst), data=lung)} First, this is unnecessary: arguments within the \code{coxph} call will be evaluated within the survival namespace, so another package's Surv or cluster function would not be noticed. (Full qualification of the coxph call itself may be protective, however.) Second, and more importantly, the call just above will not give the correct answer. The specials are recognized by their name, and \code{survival::cluster} is not the same as \code{cluster}; the above model would treat \code{inst} as an ordinary variable. A similar issue arises from using \code{stats::offset} as a term, in either survival or glm models. } \section{Convergence}{ In certain data cases the actual MLE estimate of a coefficient is infinity, e.g., a dichotomous variable where one of the groups has no events. When this happens the associated coefficient grows at a steady pace and a race condition will exist in the fitting routine: either the log likelihood converges, the information matrix becomes effectively singular, an argument to exp becomes too large for the computer hardware, or the maximum number of interactions is exceeded. (Most often number 1 is the first to occur.) The routine attempts to detect when this has happened, not always successfully. The primary consequence for the user is that the Wald statistic = coefficient/se(coefficient) is not valid in this case and should be ignored; the likelihood ratio and score tests remain valid however. } \section{Ties}{ There are three possible choices for handling tied event times. The Breslow approximation is the easiest to program and hence became the first option coded for almost all computer routines. It then ended up as the default option when other options were added in order to "maintain backwards compatability". The Efron option is more accurate if there are a large number of ties, and it is the default option here. In practice the number of ties is usually small, in which case all the methods are statistically indistinguishable. Using the "exact partial likelihood" approach the Cox partial likelihood is equivalent to that for matched logistic regression. (The \code{clogit} function uses the \code{coxph} code to do the fit.) It is technically appropriate when the time scale is discrete and has only a few unique values, and some packages refer to this as the "discrete" option. There is also an "exact marginal likelihood" due to Prentice which is not implemented here. The calculation of the exact partial likelihood is numerically intense. Say for instance 180 subjects are at risk on day 7 of which 15 had an event; then the code needs to compute sums over all 180-choose-15 > 10^43 different possible subsets of size 15. There is an efficient recursive algorithm for this task, but even with this the computation can be insufferably long. With (start, stop) data it is much worse since the recursion needs to start anew for each unique start time. A separate issue is that of artificial ties due to floating-point imprecision. See the vignette on this topic for a full explanation or the \code{timefix} option in \code{coxph.control}. Users may need to add \code{timefix=FALSE} for simulated data sets. } \section{Penalized regression}{ \code{coxph} can maximise a penalised partial likelihood with arbitrary user-defined penalty. Supplied penalty functions include ridge regression (\link{ridge}), smoothing splines (\link{pspline}), and frailty models (\link{frailty}). } \references{ Andersen, P. and Gill, R. (1982). Cox's regression model for counting processes, a large sample study. \emph{Annals of Statistics} \bold{10}, 1100-1120. Therneau, T., Grambsch, P., Modeling Survival Data: Extending the Cox Model. Springer-Verlag, 2000. } \seealso{ \code{\link{coxph.object}}, \code{\link{coxph.control}}, \code{\link{cluster}}, \code{\link{strata}}, \code{\link{Surv}}, \code{\link{survfit}}, \code{\link{pspline}}, \code{\link{ridge}}. } \examples{ # Create the simplest test data set test1 <- list(time=c(4,3,1,1,2,2,3), status=c(1,1,1,0,1,1,0), x=c(0,2,1,1,1,0,0), sex=c(0,0,0,0,1,1,1)) # Fit a stratified model coxph(Surv(time, status) ~ x + strata(sex), test1) # Create a simple data set for a time-dependent model test2 <- list(start=c(1,2,5,2,1,7,3,4,8,8), stop=c(2,3,6,7,8,9,9,9,14,17), event=c(1,1,1,1,1,1,1,0,0,0), x=c(1,0,0,1,0,1,1,1,0,0)) summary(coxph(Surv(start, stop, event) ~ x, test2)) # # Create a simple data set for a time-dependent model # test2 <- list(start=c(1, 2, 5, 2, 1, 7, 3, 4, 8, 8), stop =c(2, 3, 6, 7, 8, 9, 9, 9,14,17), event=c(1, 1, 1, 1, 1, 1, 1, 0, 0, 0), x =c(1, 0, 0, 1, 0, 1, 1, 1, 0, 0) ) summary( coxph( Surv(start, stop, event) ~ x, test2)) # Fit a stratified model, clustered on patients bladder1 <- bladder[bladder$enum < 5, ] coxph(Surv(stop, event) ~ (rx + size + number) * strata(enum), cluster = id, bladder1) # Fit a time transform model using current age coxph(Surv(time, status) ~ ph.ecog + tt(age), data=lung, tt=function(x,t,...) pspline(x + t/365.25)) } \keyword{survival} survival/man/summary.pyears.Rd0000644000176200001440000000664213537676563016223 0ustar liggesusers\name{summary.pyears} \alias{summary.pyears} \title{Summary function for pyears objecs} \description{Create a printable table of a person-years result.} \usage{ \method{summary}{pyears}(object, header = TRUE, call = header, n = TRUE, event = TRUE, pyears = TRUE, expected = TRUE, rate = FALSE, rr =expected, ci.r = FALSE, ci.rr = FALSE, totals=FALSE, legend = TRUE, vline = FALSE, vertical= TRUE, nastring=".", conf.level = 0.95, scale = 1, ...) } \arguments{ \item{object}{a pyears object} \item{header}{print out a header giving the total number of observations, events, person-years, and total time (if any) omitted from the table} \item{call}{print out a copy of the call} \item{n, event, pyears, expected}{logical arguments: should these elements be printed in the table?} \item{rate, ci.r}{logical arguments: should the incidence rate and/or its confidence interval be given in the table?} \item{rr, ci.rr}{logical arguments: should the hazard ratio and/or its confidence interval be given in the table?} \item{totals}{should row and column totals be added?} \item{legend}{should a legend be included in the printout?} \item{vline}{should vertical lines be included in the printed tables?} \item{vertical}{when there is only a single predictor, should the table be printed with the predictor on the left (vertical=TRUE) or across the top (vertical=FALSE)?} \item{nastring}{what to use for missing values in the table. Some of these are structural, e.g., risk ratios for a cell with no follow-up time.} \item{conf.level}{confidence level for any confidence intervals} \item{scale}{a scaling factor for printed rates} \item{\dots}{optional arguments which will be passed to the \code{format} function; common choices would be digits=2 or nsmall=1.} } \details{ The \code{pyears} function is often used to create initial descriptions of a survival or time-to-event variable; the type of material that is often found in ``table 1'' of a paper. The summary routine prints this information out using one of pandoc table styles. A primary reason for choosing this style is that Rstudio is then able to automatically render the results in multiple formats: html, rtf, latex, etc. If the \code{pyears} call has only a single covariate then the table will have that covariate as one margin and the statistics of interest as the other. If the \code{pyears} call has two predictors then those two predictors are used as margins of the table, while each cell of the table contains the statistics of interest as multiple rows within the cell. If there are more than two predictors then multiple tables are produced, in the same order as the standard R printout for an array. The "N" entry of a pyears object is the number of observations which contributed to a particular cell. When the original call includes \code{tcut} objects then a single observation may contribute to multiple cells. } \section{Notes}{ The pandoc system has four table types: with or without vertical bars, and with single or multiple rows of data in each cell. This routine produces all 4 styles depending on options, but currently not all of them are recognized by the Rstudio-pandoc pipeline. (And we don't yet see why.) } \value{a copy of the object} \author{Terry Therneau and Elizabeth Atkinson} \seealso{\code{\link{cipoisson}}, \code{\link{pyears}}, \code{\link{format}}} \keyword{ survival } survival/man/aggregate.survfit.Rd0000644000176200001440000000251413773225616016634 0ustar liggesusers\name{aggregate.survfit} \alias{aggregate.survfit} \title{Average survival curves} \description{ For a survfit object containing multiple curves, create average curves over a grouping. } \usage{ \method{aggregate}{survfit}(x, by = NULL, FUN = mean, ...) } \arguments{ \item{x}{a \code{survfit} object which has a data dimension.} \item{by}{an optional list or vector of grouping elements, each as long as \code{dim(x)['data']}. } \item{FUN}{a function to compute the summary statistic of interest. } \item{\dots}{optional further arguments to FUN.} } \details{ The primary use of this is to take an average over multiple survival curves that were created from a modeling function. That is, a marginal estimate of the survival. It is primarily used to average over multiple predicted curves from a Cox model. } \value{a \code{survfit} object of lower dimension.} \seealso{\code{\link{survfit}}} \examples{ cfit <- coxph(Surv(futime, death) ~ sex + age*hgb, data=mgus2) # marginal effect of sex, after adjusting for the others dummy <- rbind(mgus2, mgus2) dummy$sex <- rep(c("F", "M"), each=nrow(mgus2)) # population data set dummy <- na.omit(dummy) # don't count missing hgb in our "population csurv <- survfit(cfit, newdata=dummy) dim(csurv) # 2 * 1384 survival curves csurv2 <- aggregate(csurv, dummy$sex) } \keyword{ survival } survival/man/survreg.object.Rd0000644000176200001440000000445413537676563016165 0ustar liggesusers\name{survreg.object} \alias{survreg.object} \alias{print.survreg} \alias{summary.survreg} \title{ Parametric Survival Model Object } \description{ This class of objects is returned by the \code{survreg} function to represent a fitted parametric survival model. Objects of this class have methods for the functions \code{print}, \code{summary}, \code{predict}, and \code{residuals}. } \section{COMPONENTS}{ The following components must be included in a legitimate \code{survreg} object. \describe{ \item{coefficients}{ the coefficients of the \code{linear.predictors}, which multiply the columns of the model matrix. It does not include the estimate of error (sigma). The names of the coefficients are the names of the single-degree-of-freedom effects (the columns of the model matrix). If the model is over-determined there will be missing values in the coefficients corresponding to non-estimable coefficients. } \item{icoef}{ coefficients of the baseline model, which will contain the intercept and log(scale), or multiple scale factors for a stratified model. } \item{var}{ the variance-covariance matrix for the parameters, including the log(scale) parameter(s). } \item{loglik}{ a vector of length 2, containing the log-likelihood for the baseline and full models. } \item{iter}{ the number of iterations required } \item{linear.predictors}{ the linear predictor for each subject. } \item{df}{ the degrees of freedom for the final model. For a penalized model this will be a vector with one element per term. } \item{scale}{ the scale factor(s), with length equal to the number of strata. } \item{idf}{ degrees of freedom for the initial model. } \item{means}{ a vector of the column means of the coefficient matrix. } \item{dist}{ the distribution used in the fit.} \item{weights}{included for a weighted fit.} } The object will also have the following components found in other model results (some are optional): \code{linear predictors}, \code{weights}, \code{x}, \code{y}, \code{model}, \code{call}, \code{terms} and \code{formula}. See \code{lm}. } \seealso{ \code{\link{survreg}}, \code{\link{lm}} } \keyword{regression} \keyword{survival} % Converted by Sd2Rd version 0.3-2. survival/man/bladder.Rd0000644000176200001440000000652714013456741014603 0ustar liggesusers\name{bladder} \docType{data} \alias{bladder} \alias{bladder1} \alias{bladder2} \title{Bladder Cancer Recurrences} \usage{ bladder1 bladder bladder2 data(cancer, package="survival") } \description{Data on recurrences of bladder cancer, used by many people to demonstrate methodology for recurrent event modelling. Bladder1 is the full data set from the study. It contains all three treatment arms and all recurrences for 118 subjects; the maximum observed number of recurrences is 9. Bladder is the data set that appears most commonly in the literature. It uses only the 85 subjects with nonzero follow-up who were assigned to either thiotepa or placebo, and only the first four recurrences for any patient. The status variable is 1 for recurrence and 0 for everything else (including death for any reason). The data set is laid out in the competing risks format of the paper by Wei, Lin, and Weissfeld. Bladder2 uses the same subset of subjects as bladder, but formatted in the (start, stop] or Anderson-Gill style. Note that in transforming from the WLW to the AG style data set there is a quite common programming mistake that leads to extra follow-up time for 12 subjects: all those with follow-up beyond their 4th recurrence. This "follow-up" is a side effect of throwing away all events after the fourth while retaining the last follow-up time variable from the original data. The bladder2 data set found here does not make this mistake, but some analyses in the literature have done so; it results in the addition of a small amount of immortal time bias and shrinks the fitted coefficients towards zero. } \format{ bladder1 \tabular{ll}{ id:\tab Patient id\cr treatment:\tab Placebo, pyridoxine (vitamin B6), or thiotepa\cr number:\tab Initial number of tumours (8=8 or more)\cr size:\tab Size (cm) of largest initial tumour\cr recur:\tab Number of recurrences \cr start,stop:\tab The start and end time of each time interval\cr status:\tab End of interval code, 0=censored, 1=recurrence, \cr \tab 2=death from bladder disease, 3=death other/unknown cause\cr rtumor:\tab Number of tumors found at the time of a recurrence\cr rsize:\tab Size of largest tumor at a recurrence\cr enum:\tab Event number (observation number within patient)\cr } bladder \tabular{ll}{ id:\tab Patient id\cr rx:\tab Treatment 1=placebo 2=thiotepa\cr number:\tab Initial number of tumours (8=8 or more)\cr size:\tab size (cm) of largest initial tumour\cr stop:\tab recurrence or censoring time\cr enum:\tab which recurrence (up to 4)\cr } bladder2 \tabular{ll}{ id:\tab Patient id\cr rx:\tab Treatment 1=placebo 2=thiotepa\cr number:\tab Initial number of tumours (8=8 or more)\cr size:\tab size (cm) of largest initial tumour\cr start:\tab start of interval (0 or previous recurrence time)\cr stop:\tab recurrence or censoring time\cr enum:\tab which recurrence (up to 4)\cr } } \source{ Andrews DF, Hertzberg AM (1985), DATA: A Collection of Problems from Many Fields for the Student and Research Worker, New York: Springer-Verlag. LJ Wei, DY Lin, L Weissfeld (1989), Regression analysis of multivariate incomplete failure time data by modeling marginal distributions. \emph{Journal of the American Statistical Association}, \bold{84}. } \keyword{datasets} \keyword{survival} survival/man/pbc.Rd0000644000176200001440000000536114013462774013750 0ustar liggesusers\name{pbc} \alias{pbc} \docType{data} \title{Mayo Clinic Primary Biliary Cholangitis Data} \description{ Primary sclerosing cholangitis is an autoimmune disease leading to destruction of the small bile ducts in the liver. Progression is slow but inexhortable, eventually leading to cirrhosis and liver decompensation. The condition has been recognised since at least 1851 and was named "primary biliary cirrhosis" in 1949. Because cirrhosis is a feature only of advanced disease, a change of its name to "primary biliary cholangitis" was proposed by patient advocacy groups in 2014. This data is from the Mayo Clinic trial in PBC conducted between 1974 and 1984. A total of 424 PBC patients, referred to Mayo Clinic during that ten-year interval, met eligibility criteria for the randomized placebo controlled trial of the drug D-penicillamine. The first 312 cases in the data set participated in the randomized trial and contain largely complete data. The additional 112 cases did not participate in the clinical trial, but consented to have basic measurements recorded and to be followed for survival. Six of those cases were lost to follow-up shortly after diagnosis, so the data here are on an additional 106 cases as well as the 312 randomized participants. A nearly identical data set found in appendix D of Fleming and Harrington; this version has fewer missing values. } \usage{pbc data(pbc, package="survival") } \format{ \tabular{ll}{ age:\tab in years\cr albumin:\tab serum albumin (g/dl)\cr alk.phos:\tab alkaline phosphotase (U/liter)\cr ascites:\tab presence of ascites \cr ast:\tab aspartate aminotransferase, once called SGOT (U/ml)\cr bili:\tab serum bilirunbin (mg/dl)\cr chol:\tab serum cholesterol (mg/dl)\cr copper:\tab urine copper (ug/day)\cr edema:\tab 0 no edema, 0.5 untreated or successfully treated\cr \tab 1 edema despite diuretic therapy\cr hepato:\tab presence of hepatomegaly or enlarged liver\cr id:\tab case number\cr platelet:\tab platelet count\cr protime:\tab standardised blood clotting time\cr sex:\tab m/f\cr spiders:\tab blood vessel malformations in the skin\cr stage:\tab histologic stage of disease (needs biopsy)\cr status:\tab status at endpoint, 0/1/2 for censored, transplant, dead\cr time: \tab number of days between registration and the earlier of death,\cr \tab transplantion, or study analysis in July, 1986\cr trt:\tab 1/2/NA for D-penicillmain, placebo, not randomised\cr trig:\tab triglycerides (mg/dl)\cr } } \source{ T Therneau and P Grambsch (2000), \emph{Modeling Survival Data: Extending the Cox Model}, Springer-Verlag, New York. ISBN: 0-387-98784-3. } \seealso{\code{\link{pbcseq}}} \keyword{datasets} survival/man/Survmethods.Rd0000644000176200001440000000767513537676563015556 0ustar liggesusers\name{Surv-methods} \alias{Math.Surv} \alias{Ops.Surv} \alias{Summary.Surv} \alias{anyDuplicated.Surv} \alias{as.character.Surv} \alias{as.data.frame.Surv} \alias{as.integer.Surv} \alias{as.matrix.Surv} \alias{as.numeric.Surv} \alias{c.Surv} \alias{duplicated.Surv} \alias{format.Surv} \alias{head.Surv} \alias{is.na.Surv} \alias{length.Surv} \alias{mean.Surv} \alias{median.Surv} \alias{names.Surv} \alias{names<-.Surv} \alias{quantile.Surv} \alias{plot.Surv} \alias{rep.Surv} \alias{rep.int.Surv} \alias{rep_len.Surv} \alias{rev.Surv} \alias{t.Surv} \alias{tail.Surv} \alias{unique.Surv} \title{Methods for Surv objects} \description{The list of methods that apply to \code{Surv} objects} \usage{ \method{anyDuplicated}{Surv}(x, ...) \method{as.character}{Surv}(x, ...) \method{as.data.frame}{Surv}(x, ...) \method{as.integer}{Surv}(x, ...) \method{as.matrix}{Surv}(x, ...) \method{as.numeric}{Surv}(x, ...) \method{c}{Surv}(...) \method{duplicated}{Surv}(x, ...) \method{format}{Surv}(x, ...) \method{head}{Surv}(x, ...) \method{is.na}{Surv}(x) \method{length}{Surv}(x) \method{mean}{Surv}(x, ...) \method{median}{Surv}(x, ...) \method{names}{Surv}(x) \method{names}{Surv}(x) <- value \method{quantile}{Surv}(x, probs, na.rm=FALSE, ...) \method{plot}{Surv}(x, ...) \method{rep}{Surv}(x, ...) \method{rep.int}{Surv}(x, ...) \method{rep_len}{Surv}(x, ...) \method{rev}{Surv}(x) \method{t}{Surv}(x) \method{tail}{Surv}(x, ...) \method{unique}{Surv}(x, ...) } \arguments{ \item{x}{a \code{Surv} object} \item{probs}{a vector of probabilities} \item{na.rm}{remove missing values from the calculation} \item{value}{a character vector of up to the same length as \code{x}, or \code{NULL}} \item{\ldots}{other arguments to the method} } \details{ These functions extend the standard methods to \code{Surv} objects. The arguments and results from these are mostly as expected, with the following further details: \itemize{ \item The \code{as.character} function uses "5+" for right censored at time 5, "5-" for left censored at time 5, "[2,7]" for an observation that was interval censored between 2 and 7, "(1,6]" for a counting process data denoting an observation which was at risk from time 1 to 6, with an event at time 6, and "(1,6+]" for an observation over the same interval but not ending with and event. For a multi-state survival object the type of event is appended to the event time using ":type". \item The \code{print} and \code{format} methods make use of \code{as.character}. \item The \code{as.numeric} and \code{as.integer} methods perform these actions on the survival times, but do not affect the censoring indicator. \item The \code{as.matrix} and \code{t} methods return a matrix \item The \code{length} of a \code{Surv} object is the number of survival times it contains, not the number of items required to encode it, e.g., \code{x <- Surv(1:4, 5:9, c(1,0,1,0)); length(x)} has a value of 4. Likewise \code{names(x)} will be NULL or a vector of length 4. (For technical reasons, any names are actually stored in the \code{rownames} attribute of the object.) \item For a multi-state survival object \code{levels} returns the names of the endpoints, otherwise it is NULL. \item The \code{median}, \code{quantile} and \code{plot} methods first construct a survival curve using \code{survfit}, then apply the appropriate method to that curve. \item The concatonation method \code{c()} is asymmetric, its first argument determines the exection path. For instance \code{c(Surv(1:4), Surv(5:6))} will concatonate the two objects, \code{c(Surv(1:4), 5:6)} will give an error, and \code{c(5:6, Surv(1:4))} is equivalent to \code{c(5:6, as.vector(Surv(1:4)))}. } } \seealso{ \code{\link{Surv}}} \keyword{survival} survival/man/is.ratetable.Rd0000644000176200001440000000210314013473047015543 0ustar liggesusers\name{is.ratetable} \alias{is.ratetable} \alias{Math.ratetable} \alias{Ops.ratetable} \title{ Verify that an object is of class ratetable. } \description{ The function verifies not only the \code{class} attribute, but the structure of the object. } \usage{ is.ratetable(x, verbose=FALSE) } \arguments{ \item{x}{ the object to be verified. } \item{verbose}{ if \code{TRUE} and the object is not a ratetable, then return a character string describing the way(s) in which \code{x} fails to be a proper ratetable object. } } \value{ returns \code{TRUE} if \code{x} is a ratetable, and \code{FALSE} or a description if it is not. } \details{ Rate tables are used by the \code{pyears} and \code{survexp} functions, and normally contain death rates for some population, categorized by age, sex, or other variables. They have a fairly rigid structure, and the \code{verbose} option can help in creating a new rate table. } \seealso{ \code{\link{pyears}}, \code{\link{survexp}}. } \examples{ is.ratetable(survexp.us) # True is.ratetable(lung) # False } \keyword{survival} survival/man/colon.Rd0000644000176200001440000000560614013460113014301 0ustar liggesusers\name{colon} \alias{colon} \title{Chemotherapy for Stage B/C colon cancer} \usage{colon data(cancer, package="survival") } \description{These are data from one of the first successful trials of adjuvant chemotherapy for colon cancer. Levamisole is a low-toxicity compound previously used to treat worm infestations in animals; 5-FU is a moderately toxic (as these things go) chemotherapy agent. There are two records per person, one for recurrence and one for death} \format{ \tabular{ll}{ id:\tab id\cr study:\tab 1 for all patients\cr rx:\tab Treatment - Obs(ervation), Lev(amisole), Lev(amisole)+5-FU\cr sex:\tab 1=male\cr age:\tab in years\cr obstruct:\tab obstruction of colon by tumour\cr perfor:\tab perforation of colon\cr adhere:\tab adherence to nearby organs\cr nodes:\tab number of lymph nodes with detectable cancer\cr time:\tab days until event or censoring\cr status:\tab censoring status\cr differ:\tab differentiation of tumour (1=well, 2=moderate, 3=poor)\cr extent:\tab Extent of local spread (1=submucosa, 2=muscle, 3=serosa, 4=contiguous structures)\cr surg:\tab time from surgery to registration (0=short, 1=long)\cr node4:\tab more than 4 positive lymph nodes\cr etype:\tab event type: 1=recurrence,2=death\cr }} \note{The study is originally described in Laurie (1989). The main report is found in Moertel (1990). This data set is closest to that of the final report in Moertel (1991). A version of the data with less follow-up time was used in the paper by Lin (1994). Peter Higgins has pointed out a data inconsistency, revealed by \code{table(colon$nodes, colon$node4)}. We don't know which of the two variables is actually correct so have elected not to 'fix' it. (Real data has warts, why not have some in the example data too?) } \references{ JA Laurie, CG Moertel, TR Fleming, HS Wieand, JE Leigh, J Rubin, GW McCormack, JB Gerstner, JE Krook and J Malliard. Surgical adjuvant therapy of large-bowel carcinoma: An evaluation of levamisole and the combination of levamisole and fluorouracil: The North Central Cancer Treatment Group and the Mayo Clinic. J Clinical Oncology, 7:1447-1456, 1989. DY Lin. Cox regression analysis of multivariate failure time data: the marginal approach. Statistics in Medicine, 13:2233-2247, 1994. CG Moertel, TR Fleming, JS MacDonald, DG Haller, JA Laurie, PJ Goodman, JS Ungerleider, WA Emerson, DC Tormey, JH Glick, MH Veeder and JA Maillard. Levamisole and fluorouracil for adjuvant therapy of resected colon carcinoma. New England J of Medicine, 332:352-358, 1990. CG Moertel, TR Fleming, JS MacDonald, DG Haller, JA Laurie, CM Tangen, JS Ungerleider, WA Emerson, DC Tormey, JH Glick, MH Veeder and JA Maillard, Fluorouracil plus Levamisole as an effective adjuvant therapy after resection of stage II colon carcinoma: a final report. Annals of Internal Med, 122:321-326, 1991. } \keyword{survival} survival/man/survexp.fit.Rd0000644000176200001440000000420613537676563015513 0ustar liggesusers\name{survexp.fit} \alias{survexp.fit} \title{ Compute Expected Survival } \description{ Compute expected survival times. } \usage{ survexp.fit(group, x, y, times, death, ratetable) } \arguments{ \item{group}{if there are multiple survival curves this identifies the group, otherwise it is a constant. Must be an integer.} \item{x}{A matrix whose columns match the dimensions of the \code{ratetable}, in the correct order. } \item{y}{ the follow up time for each subject. } \item{times}{ the vector of times at which a result will be computed. } \item{death}{ a logical value, if \code{TRUE} the conditional survival is computed, if \code{FALSE} the cohort survival is computed. See \code{\link{survexp}} for more details. } \item{ratetable}{ a rate table, such as \code{survexp.uswhite}. } } \value{ A list containing the number of subjects and the expected survival(s) at each time point. If there are multiple groups, these will be matrices with one column per group. } \details{ For conditional survival \code{y} must be the time of last follow-up or death for each subject. For cohort survival it must be the potential censoring time for each subject, ignoring death. For an exact estimate \code{times} should be a superset of \code{y}, so that each subject at risk is at risk for the entire sub-interval of time. For a large data set, however, this can use an inordinate amount of storage and/or compute time. If the \code{times} spacing is more coarse than this, an actuarial approximation is used which should, however, be extremely accurate as long as all of the returned values are > .99. For a subgroup of size 1 and \code{times} > \code{y}, the conditional method reduces to exp(-h) where h is the expected cumulative hazard for the subject over his/her observation time. This is used to compute individual expected survival. } \section{Warning}{ Most users will call the higher level routine \code{survexp}. Consequently, this function has very few error checks on its input arguments. } \seealso{ \code{\link{survexp}}, \code{\link{survexp.us}}. } \keyword{survival } % docclass is function % Converted by Sd2Rd version 37351. survival/man/survfitcoxph.fit.Rd0000644000176200001440000000567313537676563016554 0ustar liggesusers\name{survfitcoxph.fit} \alias{survfitcoxph.fit} \title{ A direct interface to the `computational engine' of survfit.coxph } \description{ This program is mainly supplied to allow other packages to invoke the survfit.coxph function at a `data' level rather than a `user' level. It does no checks on the input data that is provided, which can lead to unexpected errors if that data is wrong. } \usage{ survfitcoxph.fit(y, x, wt, x2, risk, newrisk, strata, se.fit, survtype, vartype, varmat, id, y2, strata2, unlist=TRUE) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{y}{the response variable used in the Cox model. (Missing values removed of course.) } \item{x}{covariate matrix used in the Cox model } \item{wt}{weight vector for the Cox model. If the model was unweighted use a vector of 1s. } \item{x2}{matrix describing the hypothetical subjects for which a curve is desired. Must have the same number of columns as \code{x}. } \item{risk}{the risk score exp(X beta) from the fitted Cox model. If the model had an offset, include it in the argument to exp. } \item{newrisk}{risk scores for the hypothetical subjects } \item{strata}{strata variable used in the Cox model. This will be a factor. } \item{se.fit}{if \code{TRUE} the standard errors of the curve(s) are returned } \item{survtype}{1=Kalbfleisch-Prentice, 2=Nelson-Aalen, 3=Efron. It is usual to match this to the approximation for ties used in the \code{coxph} model: KP for `exact', N-A for `breslow' and Efron for `efron'. } \item{vartype}{1=Greenwood, 2=Aalen, 3=Efron } \item{varmat}{the variance matrix of the coefficients } \item{id}{optional; if present and not NULL this should be a vector of identifiers of length \code{nrow(x2)}. A mon-null value signifies that \code{x2} contains time dependent covariates, in which case this identifies which rows of \code{x2} go with each subject. } \item{y2}{survival times, for time dependent prediction. It gives the time range (time1,time2] for each row of \code{x2}. Note: this must be a Surv object and thus contains a status indicator, which is never used in the routine, however. } \item{strata2}{vector of strata indicators for \code{x2}. This must be a factor. } \item{unlist}{if \code{FALSE} the result will be a list with one element for each strata. Otherwise the strata are ``unpacked'' into the form found in a \code{survfit} object.} } \value{a list containing nearly all the components of a \code{survfit} object. All that is missing is to add the confidence intervals, the type of the original model's response (as in a coxph object), and the class. } \note{The source code for for both this function and \code{survfit.coxph} is written using noweb. 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Contains the data on time to serious infections observed through end of study for each patient. } \usage{cgd0} \format{ \describe{ \item{id}{subject identification number} \item{center}{enrolling center } \item{random}{date of randomization } \item{treatment}{placebo or gamma interferon } \item{sex}{sex} \item{age}{age in years, at study entry } \item{height}{height in cm at study entry} \item{weight}{weight in kg at study entry} \item{inherit}{pattern of inheritance } \item{steroids}{use of steroids at study entry,1=yes} \item{propylac}{use of prophylactic antibiotics at study entry} \item{hos.cat}{a categorization of the centers into 4 groups} \item{futime}{days to last follow-up} \item{etime1-etime7}{up to 7 infection times for the subject} } } \details{ The \code{cgdraw} data set (this one) is in the form found in the references, with one line per patient and no recoding of the variables. The \code{cgd} data set has been further processed so as to have one line per event, with covariates such as center recoded as factors to include meaningful labels. } \source{ Fleming and Harrington, Counting Processes and Survival Analysis, appendix D.2. } \seealso{\code{\link{cgd}}} \keyword{datasets} \keyword{survival} survival/man/solder.Rd0000644000176200001440000000332714013462260014462 0ustar liggesusers\name{solder} \alias{solder} \docType{data} \title{Data from a soldering experiment} \description{ In 1988 an experiment was designed and implemented at one of AT&T's factories to investigate alternatives in the "wave soldering" procedure for mounting electronic componentes to printed circuit boards. The experiment varied a number of factors relevant to the process. The response, measured by eye, is the number of visible solder skips. } \usage{solder data(solder, package="survival") } \format{ A data frame with 900 observations on the following 6 variables. \describe{ \item{\code{Opening}}{the amount of clearance around the mounting pad (3 levels)} \item{\code{Solder}}{the amount of solder (Thick or Thin)} \item{\code{Mask}}{type and thickness of the material used for the solder mask (A1.5, A3, A6, B3, B6)} \item{\code{PadType}}{the geometry and size of the mounting pad (10 levels)} \item{\code{Panel}}{each board was divided into 3 panels} \item{\code{skips}}{the number of skips} } } \details{ This data set is used as a detailed example in chapter 1 of Chambers and Hastie. Observations 1-360 and 541-900 form a balanced design of 3*2*10*3= 180 observations for four of the pad types (A1.5, A3, B3, B6), while rows 361-540 match 3 of the 6 Solder*Opening combinations with pad type A6 and the other 3 with pad type A3. } \references{ J Chambers and T Hastie, Statistical models in S. Chapman and Hall, 1993. } \examples{ # The balanced subset used by Chambers and Hastie # contains the first 180 of each mask and deletes mask A6. index <- 1 + (1:nrow(solder)) - match(solder$Mask, solder$Mask) solder.balance <- droplevels(subset(solder, Mask != "A6" & index <= 180)) } \keyword{datasets} survival/man/print.summary.coxph.Rd0000644000176200001440000000123213646411264017144 0ustar liggesusers\name{print.summary.coxph} \alias{print.summary.coxph} \title{ Print method for summary.coxph objects } \description{ Produces a printed summary of a fitted coxph model } \usage{ \method{print}{summary.coxph}(x, digits=max(getOption("digits") - 3, 3), signif.stars = getOption("show.signif.stars"), expand=FALSE, ...) } \arguments{ \item{x}{ the result of a call to \code{summary.coxph} } \item{digits}{significant digits to print} \item{signif.stars}{ Show stars to highlight small p-values } \item{expand}{if the summary is for a multi-state coxph fit, print the results in an expanded format.} \item{\dots}{For future methods} } survival/man/survexp.us.Rd0000644000176200001440000000345214013462771015342 0ustar liggesusers\name{ratetables} \alias{survexp.us} \alias{survexp.usr} \alias{survexp.mn} \title{ Census Data Sets for the Expected Survival and Person Years Functions } \description{ Census data sets for the expected survival and person years functions. } \usage{ data(survexp, package="survival") } \details{ \describe{ \item{survexp.us}{ total United States population, by age and sex, 1940 to 2012. } \item{survexp.usr}{ United States population, by age, sex and race, 1940 to 2014. Race is white, nonwhite, or black. For 1960 and 1970 the black population values were not reported separately, so the nonwhite values were used. } \item{survexp.mn}{ total Minnesota population, by age and sex, 1970 to 2013. } } Each of these tables contains the daily hazard rate for a matched subject from the population, defined as \eqn{-\log(1-q)/365.25} where \eqn{q} is the 1 year probability of death as reported in the original tables from the US Census. For age 25 in 1970, for instance, \eqn{p = 1-q} is is the probability that a subject who becomes 25 years of age in 1970 will achieve his/her 26th birthday. The tables are recast in terms of hazard per day for computational convenience. Each table is stored as an array, with additional attributes, and can be subset and manipulated as standard R arrays. See the help page for \code{ratetable} for details. All numeric dimensions of a rate table must be in the same units. The \code{survexp.us} rate table contains daily hazard rates, the age cutpoints are in days, and the calendar year cutpoints are a Date. } \seealso{\code{\link{ratetable}}, \code{\link{survexp}}, \code{\link{pyears}} } \examples{ survexp.uswhite <- survexp.usr[,,"white",] } \keyword{survival} \keyword{datasets} survival/man/tcut.Rd0000644000176200001440000000225113537676563014173 0ustar liggesusers\name{tcut} \alias{tcut} \alias{[.tcut} \alias{levels.tcut} \title{Factors for person-year calculations} \description{ Attaches categories for person-year calculations to a variable without losing the underlying continuous representation } \usage{ tcut(x, breaks, labels, scale=1) \method{levels}{tcut}(x) } %- maybe also `usage' for other objects documented here. \arguments{ \item{x}{numeric/date variable } \item{breaks}{breaks between categories, which are right-continuous } \item{labels}{labels for categories } \item{scale}{Multiply \code{x} and \code{breaks} by this.} } \value{ An object of class \code{tcut} } \seealso{ \code{\link{cut}}, \code{\link{pyears}} } \examples{ mdy.date <- function(m,d,y) as.Date(paste(ifelse(y<100, y+1900, y), m, d, sep='/')) temp1 <- mdy.date(6,6,36) temp2 <- mdy.date(6,6,55)# Now compare the results from person-years # temp.age <- tcut(temp2-temp1, floor(c(-1, (18:31 * 365.24))), labels=c('0-18', paste(18:30, 19:31, sep='-'))) temp.yr <- tcut(temp2, mdy.date(1,1,1954:1965), labels=1954:1964) temp.time <- 3700 #total days of fu py1 <- pyears(temp.time ~ temp.age + temp.yr, scale=1) #output in days py1 } \keyword{survival} survival/man/gbsg.Rd0000644000176200001440000000311614041423076014113 0ustar liggesusers\name{gbsg} \alias{gbsg} \docType{data} \title{Breast cancer data sets used in Royston and Altman (2013)} \description{ The \code{gbsg} data set contains patient records from a 1984-1989 trial conducted by the German Breast Cancer Study Group (GBSG) of 720 patients with node positive breast cancer; it retains the 686 patients with complete data for the prognostic variables. } \usage{gbsg data(cancer, package="survival") } \format{ A data set with 686 observations and 11 variables. \describe{ \item{\code{pid}}{patient identifier} \item{\code{age}}{age, years} \item{\code{meno}}{menopausal status (0= premenopausal, 1= postmenopausal)} \item{\code{size}}{tumor size, mm} \item{\code{grade}}{tumor grade} \item{\code{nodes}}{number of positive lymph nodes} \item{\code{pgr}}{progesterone receptors (fmol/l)} \item{\code{er}}{estrogen receptors (fmol/l)} \item{\code{hormon}}{hormonal therapy, 0= no, 1= yes} \item{\code{rfstime}}{recurrence free survival time; days to first of reccurence, death or last follow-up} \item{\code{status}}{0= alive without recurrence, 1= recurrence or death} }} \details{ These data sets are used in the paper by Royston and Altman. The Rotterdam data is used to create a fitted model, and the GBSG data for validation of the model. The paper gives references for the data source. } \seealso{ \code{\link{rotterdam}} } \references{ Patrick Royston and Douglas Altman, External validation of a Cox prognostic model: principles and methods. BMC Medical Research Methodology 2013, 13:33 } \keyword{datasets} \keyword{survival} survival/man/print.aareg.Rd0000644000176200001440000000276213537676563015435 0ustar liggesusers\name{print.aareg} \alias{print.aareg} \title{ Print an aareg object } \description{ Print out a fit of Aalen's additive regression model } \usage{ \method{print}{aareg}(x, maxtime, test=c("aalen", "nrisk"),scale=1,...) } \arguments{ \item{x}{ the result of a call to the \code{aareg} function } \item{maxtime}{ the upper time point to be used in the test for non-zero slope } \item{test}{ the weighting to be used in the test for non-zero slope. The default weights are based on the variance of each coefficient, as a function of time. The alternative weight is proportional to the number of subjects still at risk at each time point. } \item{scale}{scales the coefficients. For some data sets, the coefficients of the Aalen model will be very small (10-4); this simply multiplies the printed values by a constant, say 1e6, to make the printout easier to read.} \item{\dots}{for future methods} } \value{ the calling argument is returned. } \section{Side Effects}{ the results of the fit are displayed. } \details{ The estimated increments in the coefficient estimates can become quite unstable near the end of follow-up, due to the small number of observations still at risk in a data set. Thus, the test for slope will sometimes be more powerful if this last `tail' is excluded. } \section{References}{ Aalen, O.O. (1989). A linear regression model for the analysis of life times. Statistics in Medicine, 8:907-925. } \seealso{ aareg } \keyword{survival} % docclass is function % Converted by Sd2Rd version 37351. survival/man/rttright.Rd0000644000176200001440000000610314001422427015032 0ustar liggesusers\name{rttright} \alias{rttright} \title{Compute redistribute-to-the-right weights} \description{ For many survival estimands, one approach is to redistribute each censored observation's weight to those other observations with a longer survival time (think of distributing an estate to the heirs). Then compute on the remaining, uncensored data. } \usage{ rttright(formula, data, weights, subset, na.action, times, id, timefix = TRUE) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{ a formula object, which must have a \code{Surv} object as the response on the left of the \code{~} operator and, if desired, terms separated by + operators on the right. Each unique combination of predictors will define a separate strata. } \item{data}{ a data frame in which to interpret the variables named in the formula, \code{subset} and \code{weights} arguments. } \item{weights}{ The weights must be nonnegative and it is strongly recommended that they be strictly positive, since zero weights are ambiguous, compared to use of the \code{subset} argument. } \item{subset}{ expression saying that only a subset of the rows of the data should be used in the fit. } \item{na.action}{ a missing-data filter function, applied to the model frame, after any \code{subset} argument has been used. Default is \code{options()$na.action}. } \item{times}{a vector of time points, for which to return updated weights. If missing, a time after the largest time in the data is assumed.} \item{id}{optional: if the data set has multiple rows per subject, a a variable containing the subect identifier of each row.} \item{timefix}{correct for possible round-off error} } \details{ The \code{formula} argument is treated exactly the same as in the \code{survfit} function. Redistribution is recursive: redistribute the weight of the smallest censored observation to all those with longer time, which may include other censored observations. Then redistribute the next smallest and etc. up to the specified \code{time} value. After re-distributing the weight for a censored observation to other observations that are not censored, ordinary non-censored methods can often be applied. For example, redistribution of the weights, followed by computation of the weighted cumulative distribution function, reprises the Kaplan-Meier estimator. A primary use of this routine is illustration of methods or exploration of new methods. Methods that use RTTR directly, such as the Brier score, will normally do these compuations internally. } \value{a vector or matrix of weights, with one column for each requested time} \seealso{ \code{\link{survfit}} } \examples{ afit <- survfit(Surv(time, status) ~1, data=aml) rwt <- rttright(Surv(time, status) ~1, data=aml) index <- order(aml$time) cdf <- cumsum(rwt[index]) # weighted CDF cdf <- cdf[!duplicated(aml$time[index], fromLast=TRUE)] # remove duplicates cbind(time=afit$time, KM= afit$surv, RTTR= 1-cdf) } \keyword{ survival } survival/man/ovarian.Rd0000644000176200001440000000162314013460475014634 0ustar liggesusers\name{ovarian} \alias{ovarian} \docType{data} \title{Ovarian Cancer Survival Data} \usage{ovarian data(cancer, package="survival") } \description{Survival in a randomised trial comparing two treatments for ovarian cancer} \format{ \tabular{ll}{ futime:\tab survival or censoring time\cr fustat:\tab censoring status\cr age: \tab in years\cr resid.ds:\tab residual disease present (1=no,2=yes)\cr rx:\tab treatment group\cr ecog.ps:\tab ECOG performance status (1 is better, see reference)\cr } } \source{Terry Therneau} \references{ Edmunson, J.H., Fleming, T.R., Decker, D.G., Malkasian, G.D., Jefferies, J.A., Webb, M.J., and Kvols, L.K., Different Chemotherapeutic Sensitivities and Host Factors Affecting Prognosis in Advanced Ovarian Carcinoma vs. Minimal Residual Disease. Cancer Treatment Reports, 63:241-47, 1979. } \keyword{datasets} \keyword{survival} survival/man/plot.survfit.Rd0000644000176200001440000002266414006757654015676 0ustar liggesusers\name{plot.survfit} \alias{plot.survfit} \title{ Plot method for \code{survfit} objects } \usage{ \method{plot}{survfit}(x, conf.int=, mark.time=FALSE, pch=3, col=1, lty=1, lwd=1, cex=1, log=FALSE, xscale=1, yscale=1, xlim, ylim, xmax, fun, xlab="", ylab="", xaxs="r", conf.times, conf.cap=.005, conf.offset=.012, conf.type = c("log", "log-log", "plain", "logit", "arcsin"), mark, noplot="(s0)", cumhaz=FALSE, firstx, ymin, \dots) } \arguments{ \item{x}{ an object of class \code{survfit}, usually returned by the \code{survfit} function. } \item{conf.int}{ determines whether pointwise confidence intervals will be plotted. The default is to do so if there is only 1 curve, i.e., no strata, using 95\% confidence intervals Alternatively, this can be a numeric value giving the desired confidence level. } \item{mark.time}{ controls the labeling of the curves. If set to \code{FALSE}, no labeling is done. If \code{TRUE}, then curves are marked at each censoring time. If \code{mark} is a numeric vector then curves are marked at the specified time points. } \item{pch}{ vector of characters which will be used to label the curves. The \code{points} help file contains examples of the possible marks. A single string such as "abcd" is treated as a vector \code{c("a", "b", "c", "d")}. The vector is reused cyclically if it is shorter than the number of curves. If it is present this implies \code{mark.time = TRUE}. } \item{col}{ a vector of integers specifying colors for each curve. The default value is 1. } \item{lty}{ a vector of integers specifying line types for each curve. The default value is 1. } \item{lwd}{ a vector of numeric values for line widths. The default value is 1. } \item{cex}{ a numeric value specifying the size of the marks. This is not treated as a vector; all marks have the same size. } \item{log}{ a logical value, if TRUE the y axis wll be on a log scale. Alternately, one of the standard character strings "x", "y", or "xy" can be given to specific logarithmic horizontal and/or vertical axes. } \item{xscale}{ a numeric value used like \code{yscale} for labels on the x axis. A value of 365.25 will give labels in years instead of the original days. } \item{yscale}{ a numeric value used to multiply the labels on the y axis. A value of 100, for instance, would be used to give a percent scale. Only the labels are changed, not the actual plot coordinates, so that adding a curve with "\code{lines(surv.exp(...))}", say, will perform as it did without the \code{yscale} argument. } \item{xlim,ylim}{optional limits for the plotting region. } \item{xmax}{ the maximum horizontal plot coordinate. This can be used to shrink the range of a plot. It shortens the curve before plotting it, so that unlike using the \code{xlim} graphical parameter, warning messages about out of bounds points are not generated. } \item{fun}{ an arbitrary function defining a transformation of the survival (or probability in state, or cumulative hazard) curves. For example \code{fun=log} is an alternative way to draw a log-survival curve (but with the axis labeled with log(S) values), and \code{fun=sqrt} would generate a curve on square root scale. Four often used transformations can be specified with a character argument instead: \code{"S"} gives the usual survival curve, \code{"log"} is the same as using the \code{log=T} option, \code{"event"} or \code{"F"} plots the empirical CDF \eqn{F(t)= 1-S(t)} (f(y) = 1-y), and \code{"cloglog"} creates a complimentary log-log survival plot (f(y) = log(-log(y)) along with log scale for the x-axis). The terms \code{"identity"} and \code{"surv"} are allowed as synonyms for \code{type="S"}. The argument \code{"cumhaz"} causes the cumulative hazard function to be plotted. } \item{xlab}{ label given to the x-axis. } \item{ylab}{ label given to the y-axis. } \item{xaxs}{ either \code{"S"} for a survival curve or a standard x axis style as listed in \code{par}; "r" (regular) is the R default. Survival curves have historically been displayed with the curve touching the y-axis, but not touching the bounding box of the plot on the other 3 sides, Type \code{"S"} accomplishes this by manipulating the plot range and then using the \code{"i"} style internally. The "S" style is becoming increasingly less common, however. } \item{conf.times}{optional vector of times at which to place a confidence bar on the curve(s). If present, these will be used instead of confidence bands.} \item{conf.cap}{width of the horizontal cap on top of the confidence bars; only used if conf.times is used. A value of 1 is the width of the plot region.} \item{conf.offset}{the offset for confidence bars, when there are multiple curves on the plot. A value of 1 is the width of the plot region. If this is a single number then each curve's bars are offset by this amount from the prior curve's bars, if it is a vector the values are used directly.} \item{conf.type}{ One of \code{"plain"}, \code{"log"} (the default), \code{"log-log"} or \code{"logit"}. Only enough of the string to uniquely identify it is necessary. The first option causes confidence intervals not to be generated. The second causes the standard intervals \code{curve +- k *se(curve)}, where k is determined from \code{conf.int}. The log option calculates intervals based on the cumulative hazard or log(survival). The log-log option bases the intervals on the log hazard or log(-log(survival)), and the logit option on log(survival/(1-survival)). } \item{mark}{a historical alias for \code{pch}} \item{noplot}{for multi-state models, curves with this label will not be plotted. (Also see the \code{istate0} argument in \code{survcheck}.)} \item{cumhaz}{plot the cumulative hazard rather than the probability in state or survival. Optionally, this can be a numeric vector specifying which columns of the \code{cumhaz} component to plot.} \item{ymin}{ this will normally be given as part of the \code{ylim} argument} \item{firstx}{this will normally be given as part of the \code{xlim} argument.} \item{\dots}{other arguments that will be passed forward to the underlying plot method, such as xlab or ylab.} } \value{ a list with components \code{x} and \code{y}, containing the coordinates of the last point on each of the curves (but not the confidence limits). This may be useful for labeling. } \description{ A plot of survival curves is produced, one curve for each strata. The \code{log=T} option does extra work to avoid log(0), and to try to create a pleasing result. If there are zeros, they are plotted by default at 0.8 times the smallest non-zero value on the curve(s). Curves are plotted in the same order as they are listed by \code{print} (which gives a 1 line summary of each). This will be the order in which \code{col}, \code{lty}, etc are used. } \details{ If the object contains a cumulative hazard curve, then \code{fun='cumhaz'} will plot that curve, otherwise it will plot -log(S) as an approximation. Theoretically, S = \eqn{\exp(-\Lambda)}{exp(-H)} where S is the survival and \eqn{\Lambda}{H} is the cumulative hazard. The same relationship holds for estimates of S and \eqn{\Lambda}{H} only in special cases, but the approximation is often close. When the \code{survfit} function creates a multi-state survival curve the resulting object also has class `survfitms'. Competing risk curves are a common case. In this situation the \code{fun} argument is ignored. When the \code{conf.times} argument is used, the confidence bars are offset by \code{conf.offset} units to avoid overlap. The bar on each curve are the confidence interval for the time point at which the bar is drawn, i.e., different time points for each curve. If curves are steep at that point, the visual impact can sometimes substantially differ for positive and negative values of \code{conf.offset}. } \note{In prior versions the behavior of \code{xscale} and \code{yscale} differed: the first changed the scale both for the plot and for all subsequent actions such as adding a legend, whereas \code{yscale} affected only the axis label. This was normalized in version 2-36.4, and both parameters now only affect the labeling. In versions prior to approximately 2.36 a \code{survfit} object did not contain the cumulative hazard as a separate result, and the use of fun="cumhaz" would plot the approximation -log(surv) to the cumulative hazard. When cumulative hazards were added to the object, the \code{cumhaz=TRUE} argument to the plotting function was added. In version 2.3-8 the use of fun="cumhaz" became a synonym for \code{cumhaz=TRUE}. } \seealso{ \code{\link{points.survfit}}, \code{\link{lines.survfit}}, \code{\link{par}}, \code{\link{survfit}} } \examples{ leukemia.surv <- survfit(Surv(time, status) ~ x, data = aml) plot(leukemia.surv, lty = 2:3) legend(100, .9, c("Maintenance", "No Maintenance"), lty = 2:3) title("Kaplan-Meier Curves\nfor AML Maintenance Study") lsurv2 <- survfit(Surv(time, status) ~ x, aml, type='fleming') plot(lsurv2, lty=2:3, fun="cumhaz", xlab="Months", ylab="Cumulative Hazard") } \keyword{survival} \keyword{hplot} survival/man/royston.Rd0000644000176200001440000000355714073031214014711 0ustar liggesusers\name{royston} \alias{royston} \title{Compute Royston's D for a Cox model} \description{ Compute the D statistic proposed by Royston and Sauerbrei along with several pseudo- R square values. } \usage{ royston(fit, newdata, ties = TRUE, adjust = FALSE) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{fit}{a coxph fit} \item{newdata}{optional validation data set} \item{ties}{make a correction for ties in the risk score} \item{adjust}{adjust for possible overfitting} } \details{ These values are called pseudo R-squared since they involve only the linear predictor, and not the outcome. \code{R.D} is the value that corresponsds the Royston and Sauerbrei \eqn{D}{D} statistic. \code{R.KO} is the value proposed by Kent and O'Quigley, \code{R.N} is the value proposed by Nagelkerke, and \code{C.GH} corresponds to Goen and Heller's concordance measure. An adjustment for D is based on the ratio r= (number of events)/(number of coefficients). For models which have sufficient sample size (r>20) the adjustment will be small. } \value{a vector containing the value of D, the estimated standard error of D, and four pseudo R-squared values. } \references{ M. Goen and G. Heller, Concordance probability and discriminatory power in proportional hazards regression. Biometrika 92:965-970, 2005. N. Nagelkerke, J. Oosting, J. and A. Hart, A simple test for goodness of fit of Cox's proportional hazards model. Biometrics 40:483-486, 1984. P. Royston and W. Sauerbrei, A new measure of prognostic separation in survival data. Statistics in Medicine 23:723-748, 2004. } \examples{ # An example used in Royston and Sauerbrei pbc2 <- na.omit(pbc) # no missing values cfit <- coxph(Surv(time, status==2) ~ age + log(bili) + edema + albumin + stage + copper, data=pbc2, ties="breslow") royston(cfit) } \keyword{ survival } survival/man/survival-deprecated.Rd0000644000176200001440000000163113775460206017153 0ustar liggesusers\name{survival-deprecated} \alias{survival-deprecated} \alias{survConcordance} \alias{survConcordance.fit} \title{Deprecated functions in package \pkg{survival}} \description{ These functions are temporarily retained for compatability with older programs, and may transition to defunct status. } \usage{ survConcordance(formula, data, weights, subset, na.action) # use concordance survConcordance.fit(y, x, strata, weight) # use concordancefit } \arguments{ \item{formula}{ a formula object, with the response on the left of a \code{~} operator, and the terms on the right. The response must be a survival object as returned by the \code{Surv} function. } \item{data}{a data frame } \item{weights,subset,na.action}{as for \code{coxph}} \item{x, y, strata, weight}{predictor, response, strata, and weight vectors for the direct call} } \seealso{ \code{\link{Deprecated}} } \keyword{survival}survival/man/cipoisson.Rd0000644000176200001440000000410613670320607015202 0ustar liggesusers\name{cipoisson} \alias{cipoisson} \title{Confidence limits for the Poisson} \description{Confidence interval calculation for Poisson rates.} \usage{ cipoisson(k, time = 1, p = 0.95, method = c("exact", "anscombe")) } \arguments{ \item{k}{Number of successes} \item{time}{Total time on trial} \item{p}{Probability level for the (two-sided) interval} \item{method}{The method for computing the interval.} } \value{a vector, matrix, or array. If both \code{k} and \code{time} are single values the result is a vector of length 2 containing the lower an upper limits. If either or both are vectors the result is a matrix with two columns. If \code{k} is a matrix or array, the result will be an array with one more dimension; in this case the dimensions and dimnames (if any) of \code{k} are preserved. } \details{ The likelihood method is based on equation 10.10 of Feller, which relates poisson probabilities to tail area of the gamma distribution. The Anscombe approximation is based on the fact that sqrt(k + 3/8) has a nearly constant variance of 1/4, along with a continuity correction. There are many other proposed intervals: Patil and Kulkarni list and evaluate 19 different suggestions from the literature!. The exact intervals can be overly broad for very small values of \code{k}, many of the other approaches try to shrink the lengths, with varying success. } \examples{ cipoisson(4) # 95\\\% confidence limit # lower upper # 1.089865 10.24153 ppois(4, 10.24153) #chance of seeing 4 or fewer events with large rate # [1] 0.02500096 1-ppois(3, 1.08986) #chance of seeing 4 or more, with a small rate # [1] 0.02499961 } \references{ F.J. Anscombe (1949). Transformations of Poisson, binomial and negative-binomial data. Biometrika, 35:246-254. W.F. Feller (1950). An Introduction to Probability Theory and its Applications, Volume 1, Chapter 6, Wiley. V. V. Patil and H.F. Kulkarni (2012). Comparison of confidence intervals for the poisson mean: some new aspects. Revstat 10:211-227. } \seealso{ \code{\link[stats:Poisson]{ppois}}, \code{\link[stats:Poisson]{qpois}} } survival/man/coxph.control.Rd0000644000176200001440000000771313537676563016024 0ustar liggesusers\name{coxph.control} \alias{coxph.control} \title{Ancillary arguments for controlling coxph fits} \description{ This is used to set various numeric parameters controlling a Cox model fit. Typically it would only be used in a call to \code{coxph}. } \usage{ coxph.control(eps = 1e-09, toler.chol = .Machine$double.eps^0.75, iter.max = 20, toler.inf = sqrt(eps), outer.max = 10, timefix=TRUE) } \arguments{ \item{eps}{Iteration continues until the relative change in the log partial likelihood is less than eps, or the absolute change is less than sqrt(eps). Must be positive.} \item{toler.chol}{Tolerance for detection of singularity during a Cholesky decomposition of the variance matrix, i.e., for detecting a redundant predictor variable.} \item{iter.max}{Maximum number of iterations to attempt for convergence.} \item{toler.inf}{Tolerance criteria for the warning message about a possible infinite coefficient value.} \item{outer.max}{For a penalized coxph model, e.g. with pspline terms, there is an outer loop of iteration to determine the penalty parameters; maximum number of iterations for this outer loop.} \item{timefix}{Resolve any near ties in the time variables.} } \value{ a list containing the values of each of the above constants } \details{ The convergence tolerances are a balance. Users think they want THE maximum point of the likelihood surface, and for well behaved data sets where this is quadratic near the max a high accuracy is fairly inexpensive: the number of correct digits approximately doubles with each iteration. Conversely, a drop of .0001 from the maximum in any given direction will be correspond to only about 1/20 of a standard error change in the coefficient. Statistically, more precision than this is straining at a gnat. Based on this the author originally had set the tolerance to 1e-5, but relented in the face of multiple "why is the answer different than package X" queries. Asking for results that are too close to machine precision (double.eps) is a fool's errand; a reasonable critera is often the square root of that precision. The Cholesky decompostion needs to be held to a higher standard than the overall convergence criterion, however. The \code{tolerance.inf} value controls a warning message; if it is too small incorrect warnings can appear, if too large some actual cases of an infinite coefficient will not be detected. The most difficult cases are data sets where the MLE coefficient is infinite; an example is a data set where at each death time, it was the subject with the largest covariate value who perished. In that situation the coefficient increases at each iteration while the log-likelihood asymptotes to a maximum. As iteration proceeds there is a race condition condition for three endpoint: exp(coef) overflows, the Hessian matrix become singular, or the change in loglik is small enough to satisfy the convergence criterion. The first two are difficult to anticipate and lead to numeric diffculties, which is another argument for moderation in the choice of \code{eps}. See the vignette "Roundoff error and tied times" for a more detailed explanation of the \code{timefix} option. In short, when time intervals are created via subtraction then two time intervals that are actually identical can appear to be different due to floating point round off error, which in turn can make \code{coxph} and \code{survfit} results dependent on things such as the order in which operations were done or the particular computer that they were run on. Such cases are unfortunatedly not rare in practice. The \code{timefix=TRUE} option adds logic similar to \code{all.equal} to ensure reliable results. In analysis of simulated data sets, however, where often by defintion there can be no duplicates, the option will often need to be set to \code{FALSE} to avoid spurious merging of close numeric values. } \seealso{\code{\link{coxph}} } \keyword{survival} survival/man/survreg.Rd0000644000176200001440000001152613640437051014674 0ustar liggesusers\name{survreg} \alias{survreg} \alias{model.frame.survreg} \alias{labels.survreg} \alias{print.survreg.penal} \alias{print.summary.survreg} \alias{survReg} \alias{anova.survreg} \alias{anova.survreglist} \title{ Regression for a Parametric Survival Model } \description{ Fit a parametric survival regression model. These are location-scale models for an arbitrary transform of the time variable; the most common cases use a log transformation, leading to accelerated failure time models. } \usage{ survreg(formula, data, weights, subset, na.action, dist="weibull", init=NULL, scale=0, control,parms=NULL,model=FALSE, x=FALSE, y=TRUE, robust=FALSE, cluster, score=FALSE, \dots) } \arguments{ \item{formula}{ a formula expression as for other regression models. The response is usually a survival object as returned by the \code{Surv} function. See the documentation for \code{Surv}, \code{lm} and \code{formula} for details. } \item{data}{ a data frame in which to interpret the variables named in the \code{formula}, \code{weights} or the \code{subset} arguments. } \item{weights}{optional vector of case weights} \item{subset}{ subset of the observations to be used in the fit } \item{na.action}{ a missing-data filter function, applied to the model.frame, after any \code{subset} argument has been used. Default is \code{options()\$na.action}. } \item{dist}{ assumed distribution for y variable. If the argument is a character string, then it is assumed to name an element from \code{\link{survreg.distributions}}. These include \code{"weibull"}, \code{"exponential"}, \code{"gaussian"}, \code{"logistic"},\code{"lognormal"} and \code{"loglogistic"}. Otherwise, it is assumed to be a user defined list conforming to the format described in \code{\link{survreg.distributions}}. } \item{parms}{ a list of fixed parameters. For the t-distribution for instance this is the degrees of freedom; most of the distributions have no parameters. } \item{init}{ optional vector of initial values for the parameters. } \item{scale}{ optional fixed value for the scale. If set to <=0 then the scale is estimated. } \item{control}{ a list of control values, in the format produced by \code{\link{survreg.control}}. The default value is \code{survreg.control()} } \item{model,x,y}{ flags to control what is returned. If any of these is true, then the model frame, the model matrix, and/or the vector of response times will be returned as components of the final result, with the same names as the flag arguments.} \item{score}{return the score vector. (This is expected to be zero upon successful convergence.) } \item{robust}{Use robust sandwich error instead of the asymptotic formula. Defaults to TRUE if there is a \code{cluster} argument.} \item{cluster}{Optional variable that identifies groups of subjects, used in computing the robust variance. Like \code{model} variables, this is searched for in the dataset pointed to by the \code{data} argument. } \item{\dots}{ other arguments which will be passed to \code{survreg.control}. }} \value{ an object of class \code{survreg} is returned. } \details{ All the distributions are cast into a location-scale framework, based on chapter 2.2 of Kalbfleisch and Prentice. The resulting parameterization of the distributions is sometimes (e.g. gaussian) identical to the usual form found in statistics textbooks, but other times (e.g. Weibull) it is not. See the book for detailed formulas. } \seealso{ \code{\link{survreg.object}}, \code{\link{survreg.distributions}}, \code{\link{pspline}}, \code{\link{frailty}}, \code{\link{ridge}} } \examples{ # Fit an exponential model: the two fits are the same survreg(Surv(futime, fustat) ~ ecog.ps + rx, ovarian, dist='weibull', scale=1) survreg(Surv(futime, fustat) ~ ecog.ps + rx, ovarian, dist="exponential") # # A model with different baseline survival shapes for two groups, i.e., # two different scale parameters survreg(Surv(time, status) ~ ph.ecog + age + strata(sex), lung) # There are multiple ways to parameterize a Weibull distribution. The survreg # function embeds it in a general location-scale family, which is a # different parameterization than the rweibull function, and often leads # to confusion. # survreg's scale = 1/(rweibull shape) # survreg's intercept = log(rweibull scale) # For the log-likelihood all parameterizations lead to the same value. y <- rweibull(1000, shape=2, scale=5) survreg(Surv(y)~1, dist="weibull") # Economists fit a model called `tobit regression', which is a standard # linear regression with Gaussian errors, and left censored data. tobinfit <- survreg(Surv(durable, durable>0, type='left') ~ age + quant, data=tobin, dist='gaussian') } \references{ Kalbfleisch, J. D. and Prentice, R. L., The statistical analysis of failure time data, Wiley, 2002. } \keyword{survival} survival/man/summary.survexp.Rd0000644000176200001440000000266113537676563016431 0ustar liggesusers\name{summary.survexp} \alias{summary.survexp} \title{Summary function for a survexp object} \description{ Returns a list containing the values of the survival at specified times. } \usage{ \method{summary}{survexp}(object, times, scale = 1, ...) } \arguments{ \item{object}{ the result of a call to the \code{survexp} function } \item{times}{ vector of times; the returned matrix will contain 1 row for each time. Missing values are not allowed. } \item{scale}{ numeric value to rescale the survival time, e.g., if the input data to \code{survfit} were in days, \code{scale = 365.25} would scale the output to years. } \item{\dots}{For future methods} } \details{ A primary use of this function is to retrieve survival at fixed time points, which will be properly interpolated by the function. } \value{ a list with the following components: \item{surv}{ the estimate of survival at time t. } \item{time}{ the timepoints on the curve. } \item{n.risk}{ In expected survival each subject from the data set is matched to a hypothetical person from the parent population, matched on the characteristics of the parent population. The number at risk is the number of those hypothetical subject who are still part of the calculation. } } \author{Terry Therneau} \seealso{\code{\link{survexp}} } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ survival } survival/man/print.survfit.Rd0000644000176200001440000000724714110720327016033 0ustar liggesusers\name{print.survfit} \alias{print.survfit} \title{ Print a Short Summary of a Survival Curve } \description{ Print number of observations, number of events, the restricted mean survival and its standard error, and the median survival with confidence limits for the median. } \usage{ \method{print}{survfit}(x, scale=1, digits = max(options()$digits - 4,3), print.rmean=getOption("survfit.print.rmean"), rmean = getOption('survfit.rmean'),...) } \arguments{ \item{x}{ the result of a call to the \code{survfit} function. } \item{scale}{ a numeric value to rescale the survival time, e.g., if the input data to survfit were in days, \code{scale=365} would scale the printout to years. } \item{digits}{Number of digits to print} \item{print.rmean,rmean}{Options for computation and display of the restricted mean.} \item{\dots}{for future results} } \value{ x, with the invisible flag set to prevent printing. (The default for all print functions in R is to return the object passed to them; print.survfit complies with this pattern. If you want to capture these printed results for further processing, see the \code{table} component of \code{summary.survfit}.) } \section{Side Effects}{ The number of observations, the number of events, the median survival with its confidence interval, and optionally the restricted mean survival (\code{rmean}) and its standard error, are printed. If there are multiple curves, there is one line of output for each. } \details{ The mean and its variance are based on a truncated estimator. That is, if the last observation(s) is not a death, then the survival curve estimate does not go to zero and the mean is undefined. There are four possible approaches to resolve this, which are selected by the \code{rmean} option. The first is to set the upper limit to a constant, e.g.,\code{rmean=365}. In this case the reported mean would be the expected number of days, out of the first 365, that would be experienced by each group. This is useful if interest focuses on a fixed period. Other options are \code{"none"} (no estimate), \code{"common"} and \code{"individual"}. The \code{"common"} option uses the maximum time for all curves in the object as a common upper limit for the auc calculation. For the \code{"individual"}options the mean is computed as the area under each curve, over the range from 0 to the maximum observed time for that curve. Since the end point is random, values for different curves are not comparable and the printed standard errors are an underestimate as they do not take into account this random variation. This option is provided mainly for backwards compatability, as this estimate was the default (only) one in earlier releases of the code. Note that SAS (as of version 9.3) uses the integral up to the last \emph{event} time of each individual curve; we consider this the worst of the choices and do not provide an option for that calculation. The median and its confidence interval are defined by drawing a horizontal line at 0.5 on the plot of the survival curve and its confidence bands. The intersection of the line with the lower CI band defines the lower limit for the median's interval, and similarly for the upper band. If any of the intersections is not a point then we use the center of the intersection interval, e.g., if the survival curve were exactly equal to 0.5 over an interval. When data is uncensored this agrees with the usual definition of a median. } \section{References}{ Miller, Rupert G., Jr. (1981). \emph{Survival Analysis.} New York:Wiley, p 71. } \seealso{ \code{\link{summary.survfit}}, \code{\link{quantile.survfit}} } \keyword{survival} % docclass is function % Converted by Sd2Rd version 37351. survival/man/clogit.Rd0000644000176200001440000001076413537676563014505 0ustar liggesusers\name{clogit} \alias{clogit} \title{Conditional logistic regression } \description{ Estimates a logistic regression model by maximising the conditional likelihood. Uses a model formula of the form \code{case.status~exposure+strata(matched.set)}. The default is to use the exact conditional likelihood, a commonly used approximate conditional likelihood is provided for compatibility with older software. } \usage{ clogit(formula, data, weights, subset, na.action, method=c("exact", "approximate", "efron", "breslow"), \dots) } \arguments{ \item{formula}{Model formula} \item{data}{data frame } \item{weights}{optional, names the variable containing case weights} \item{subset}{optional, subset the data} \item{na.action}{optional na.action argument. By default the global option \code{na.action} is used.} \item{method}{use the correct (exact) calculation in the conditional likelihood or one of the approximations} \item{\dots}{optional arguments, which will be passed to \code{coxph.control}} } \value{ An object of class \code{"clogit"}, which is a wrapper for a \code{"coxph"} object. } \author{Thomas Lumley} \details{ It turns out that the loglikelihood for a conditional logistic regression model = loglik from a Cox model with a particular data structure. Proving this is a nice homework exercise for a PhD statistics class; not too hard, but the fact that it is true is surprising. When a well tested Cox model routine is available many packages use this `trick' rather than writing a new software routine from scratch, and this is what the clogit routine does. In detail, a stratified Cox model with each case/control group assigned to its own stratum, time set to a constant, status of 1=case 0=control, and using the exact partial likelihood has the same likelihood formula as a conditional logistic regression. The clogit routine creates the necessary dummy variable of times (all 1) and the strata, then calls coxph. The computation of the exact partial likelihood can be very slow, however. If a particular strata had say 10 events out of 20 subjects we have to add up a denominator that involves all possible ways of choosing 10 out of 20, which is 20!/(10! 10!) = 184756 terms. Gail et al describe a fast recursion method which partly ameliorates this; it was incorporated into version 2.36-11 of the survival package. The computation remains infeasible for very large groups of ties, say 100 ties out of 500 subjects, and may even lead to integer overflow for the subscripts -- in this latter case the routine will refuse to undertake the task. The Efron approximation is normally a sufficiently accurate substitute. Most of the time conditional logistic modeling is applied data with 1 case + k controls per set, in which case all of the approximations for ties lead to exactly the same result. The 'approximate' option maps to the Breslow approximation for the Cox model, for historical reasons. Case weights are not allowed when the exact option is used, as the likelihood is not defined for fractional weights. Even with integer case weights it is not clear how they should be handled. For instance if there are two deaths in a strata, one with weight=1 and one with weight=2, should the likelihood calculation consider all subsets of size 2 or all subsets of size 3? Consequently, case weights are ignored by the routine in this case. } \seealso{\code{\link{strata}},\code{\link{coxph}},\code{\link{glm}} } \examples{ \dontrun{clogit(case ~ spontaneous + induced + strata(stratum), data=infert)} # A multinomial response recoded to use clogit # The revised data set has one copy per possible outcome level, with new # variable tocc = target occupation for this copy, and case = whether # that is the actual outcome for each subject. # See the reference below for the data. resp <- levels(logan$occupation) n <- nrow(logan) indx <- rep(1:n, length(resp)) logan2 <- data.frame(logan[indx,], id = indx, tocc = factor(rep(resp, each=n))) logan2$case <- (logan2$occupation == logan2$tocc) clogit(case ~ tocc + tocc:education + strata(id), logan2) } \section{References}{ Michell H Gail, Jay H Lubin and Lawrence V Rubinstein. Likelihood calculations for matched case-control studies and survival studies with tied death times. Biometrika 68:703-707, 1980. John A. Logan. A multivariate model for mobility tables. Am J Sociology 89:324-349, 1983. } \keyword{survival} \keyword{models} survival/man/yates_setup.Rd0000644000176200001440000000214313537676563015561 0ustar liggesusers\name{yates_setup} \alias{yates_setup} \title{Method for adding new models to the \code{yates} function. } \description{This is a method which is called by the \code{yates} function, in order to setup the code to handle a particular model type. Methods for glm, coxph, and default are part of the survival package. } \usage{ yates_setup(fit, \ldots) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{fit}{a fitted model object} \item{\ldots}{optional arguments for some methods} } \details{ If the predicted value should be the linear predictor, the function should return NULL. The \code{yates} routine has particularly efficient code for this case. Otherwise it should return a prediction function or a list of two elements containing the prediction function and a summary function. The prediction function will be passed the linear predictor as a single argument and should return a vector of predicted values. } \author{Terry Therneau} \note{See the vignette on population prediction for more details.} \seealso{ \code{\link{yates}} } \keyword{ models } \keyword{ survival } survival/man/survSplit.Rd0000644000176200001440000000726713537676563015243 0ustar liggesusers\name{survSplit} \alias{survSplit} \title{Split a survival data set at specified times } \description{ Given a survival data set and a set of specified cut times, split each record into multiple subrecords at each cut time. The new data set will be in `counting process' format, with a start time, stop time, and event status for each record. } \usage{ survSplit(formula, data, subset, na.action=na.pass, cut, start="tstart", id, zero=0, episode, end="tstop", event="event") } %- maybe also `usage' for other objects documented here. \arguments{ \item{formula}{a model formula} \item{data}{a data frame} \item{subset, na.action}{rows of the data to be retained} \item{cut}{the vector of timepoints to cut at} \item{start}{character string with the name of a start time variable (will be created if needed) } \item{id}{character string with the name of new id variable to create (optional). This can be useful if the data set does not already contain an identifier.} \item{zero}{If \code{start} doesn't already exist, this is the time that the original records start.} \item{episode}{character string with the name of new episode variable (optional)} \item{end}{character string with the name of event time variable } \item{event}{character string with the name of censoring indicator } } \value{ New, longer, data frame. } \details{ Each interval in the original data is cut at the given points; if an original row were (15, 60] with a cut vector of (10,30, 40) the resulting data set would have intervals of (15,30], (30,40] and (40, 60]. Each row in the final data set will lie completely within one of the cut intervals. Which interval for each row of the output is shown by the \code{episode} variable, where 1= less than the first cutpoint, 2= between the first and the second, etc. For the example above the values would be 2, 3, and 4. The routine is called with a formula as the first argument. The right hand side of the formula can be used to delimit variables that should be retained; normally one will use \code{ ~ .} as a shorthand to retain them all. The routine will try to retain variable names, e.g. \code{Surv(adam, joe, fred)~.} will result in a data set with those same variable names for \code{tstart}, \code{end}, and \code{event} options rather than the defaults. Any user specified values for these options will be used if they are present, of course. However, the routine is not sophisticated; it only does this substitution for simple names. A call of \code{Surv(time, stat==2)} for instance will not retain "stat" as the name of the event variable. Rows of data with a missing time or status are copied across unchanged, unless the na.action argument is changed from its default value of \code{na.pass}. But in the latter case any row that is missing for any variable will be removed, which is rarely what is desired. } \seealso{\code{\link{Surv}}, \code{\link{cut}}, \code{\link{reshape}} } \examples{ fit1 <- coxph(Surv(time, status) ~ karno + age + trt, veteran) plot(cox.zph(fit1)[1]) # a cox.zph plot of the data suggests that the effect of Karnofsky score # begins to diminish by 60 days and has faded away by 120 days. # Fit a model with separate coefficients for the three intervals. # vet2 <- survSplit(Surv(time, status) ~., veteran, cut=c(60, 120), episode ="timegroup") fit2 <- coxph(Surv(tstart, time, status) ~ karno* strata(timegroup) + age + trt, data= vet2) c(overall= coef(fit1)[1], t0_60 = coef(fit2)[1], t60_120= sum(coef(fit2)[c(1,4)]), t120 = sum(coef(fit2)[c(1,5)])) } \keyword{survival } \keyword{utilities} survival/man/flchain.Rd0000644000176200001440000000720614013461126014576 0ustar liggesusers\name{flchain} \alias{flchain} \docType{data} \title{Assay of serum free light chain for 7874 subjects.} \description{ This is a stratified random sample containing 1/2 of the subjects from a study of the relationship between serum free light chain (FLC) and mortality. The original sample contains samples on approximately 2/3 of the residents of Olmsted County aged 50 or greater. } \usage{flchain data(flchain, package="survival") } \format{ A data frame with 7874 persons containing the following variables. \describe{ \item{\code{age }}{age in years} \item{\code{sex}}{F=female, M=male} \item{\code{sample.yr}}{the calendar year in which a blood sample was obtained} \item{\code{kappa}}{serum free light chain, kappa portion} \item{\code{lambda}}{serum free light chain, lambda portion} \item{\code{flc.grp}}{the FLC group for the subject, as used in the original analysis} \item{\code{creatinine}}{serum creatinine} \item{\code{mgus}}{1 if the subject had been diagnosed with monoclonal gammapothy (MGUS)} \item{\code{futime}}{days from enrollment until death. Note that there are 3 subjects whose sample was obtained on their death date.} \item{\code{death}}{0=alive at last contact date, 1=dead} \item{\code{chapter}}{for those who died, a grouping of their primary cause of death by chapter headings of the International Code of Diseases ICD-9} } } \details{In 1995 Dr. Robert Kyle embarked on a study to determine the prevalence of monoclonal gammopathy of undetermined significance (MGUS) in Olmsted County, Minnesota, a condition which is normally only found by chance from a test (serum electrophoresis) which is ordered for other causes. Later work suggested that one component of immunoglobulin production, the serum free light chain, might be a possible marker for immune disregulation. In 2010 Dr. Angela Dispenzieri and colleagues assayed FLC levels on those samples from the original study for which they had patient permission and from which sufficient material remained for further testing. They found that elevated FLC levels were indeed associated with higher death rates. Patients were recruited when they came to the clinic for other appointments, with a final random sample of those who had not yet had a visit since the study began. An interesting side question is whether there are differences between early, mid, and late recruits. This data set contains an age and sex stratified random sample that includes 7874 of the original 15759 subjects. The original subject identifiers and dates have been removed to protect patient identity. Subsampling was done to further protect this information. } \source{The primary investigator (A Dispenzieri) and statistician (T Therneau) for the study.} \references{ A Dispenzieri, J Katzmann, R Kyle, D Larson, T Therneau, C Colby, R Clark, G Mead, S Kumar, LJ Melton III and SV Rajkumar (2012). Use of monclonal serum immunoglobulin free light chains to predict overall survival in the general population, Mayo Clinic Proceedings 87:512-523. R Kyle, T Therneau, SV Rajkumar, D Larson, M Plevak, J Offord, A Dispenzieri, J Katzmann, and LJ Melton, III, 2006, Prevalence of monoclonal gammopathy of undetermined significance, New England J Medicine 354:1362-1369. } \examples{ data(flchain) age.grp <- cut(flchain$age, c(49,54, 59,64, 69,74,79, 89, 110), labels= paste(c(50,55,60,65,70,75,80,90), c(54,59,64,69,74,79,89,109), sep='-')) table(flchain$sex, age.grp) } \keyword{datasets} survival/man/udca.Rd0000644000176200001440000000425614013474164014116 0ustar liggesusers\name{udca} \alias{udca} \alias{udca1} \alias{udca2} \docType{data} \title{Data from a trial of usrodeoxycholic acid } \description{ Data from a trial of ursodeoxycholic acid (UDCA) in patients with primary biliary cirrohosis (PBC). } \usage{udca udca2 data(udca, package="survival") } \format{ A data frame with 170 observations on the following 15 variables. \describe{ \item{\code{id}}{subject identifier} \item{\code{trt}}{treatment of 0=placebo, 1=UDCA} \item{\code{entry.dt}}{date of entry into the study} \item{\code{last.dt}}{date of last on-study visit} \item{\code{stage}}{stage of disease} \item{\code{bili}}{bilirubin value at entry} \item{\code{riskscore}}{the Mayo PBC risk score at entry} \item{\code{death.dt}}{date of death} \item{\code{tx.dt}}{date of liver transplant} \item{\code{hprogress.dt}}{date of histologic progression} \item{\code{varices.dt}}{appearance of esphogeal varices} \item{\code{ascites.dt}}{appearance of ascites} \item{\code{enceph.dt}}{appearance of encephalopathy} \item{\code{double.dt}}{doubling of initial bilirubin} \item{\code{worsen.dt}}{worsening of symptoms by two stages} } } \details{ This data set is used in the Therneau and Grambsh. The \code{udca1} data set contains the baseline variables along with the time until the first endpoint (any of death, transplant, \ldots, worsening). The \code{udca2} data set treats all of the endpoints as parallel events and has a stratum for each. } \references{ T. M. Therneau and P. M. Grambsch, Modeling survival data: extending the Cox model. Springer, 2000. K. D. Lindor, E. R. Dickson, W. P Baldus, R.A. Jorgensen, J. Ludwig, P. A. Murtaugh, J. M. Harrison, R. H. Weisner, M. L. Anderson, S. M. Lange, G. LeSage, S. S. Rossi and A. F. Hofman. Ursodeoxycholic acid in the treatment of primary biliary cirrhosis. Gastroenterology, 106:1284-1290, 1994. } \examples{ # values found in table 8.3 of the book fit1 <- coxph(Surv(futime, status) ~ trt + log(bili) + stage, cluster =id , data=udca1) fit2 <- coxph(Surv(futime, status) ~ trt + log(bili) + stage + strata(endpoint), cluster=id, data=udca2) } \keyword{datasets} survival/man/rhDNase.Rd0000644000176200001440000000764314027425473014535 0ustar liggesusers\name{rhDNase} \alias{rhDNase} \docType{data} \title{rhDNASE data set} \description{ Results of a randomized trial of rhDNase for the treatment of cystic fibrosis. } \usage{rhDNase data(rhDNase, package="survival") } \format{ A data frame with 767 observations on the following 8 variables. \describe{ \item{\code{id}}{subject id} \item{\code{inst}}{enrolling institution} \item{\code{trt}}{treatment arm: 0=placebo, 1= rhDNase} \item{\code{entry.dt}}{date of entry into the study} \item{\code{end.dt}}{date of last follow-up} \item{\code{fev}}{forced expriatory volume at enrollment, a measure of lung capacity} \item{\code{ivstart}}{days from enrollment to the start of IV antibiotics} \item{\code{ivstop}}{days from enrollment to the cessation of IV antibiotics} } } \details{ In patients with cystic fibrosis, extracellular DNA is released by leukocytes that accumulate in the airways in response to chronic bacterial infection. This excess DNA thickens the mucus, which then cannot be cleared from the lung by the cilia. The accumulation leads to exacerbations of respiratory symptoms and progressive deterioration of lung function. At the time of this study more than 90\% of cystic fibrosis patients eventually died of lung disease. Deoxyribonuclease I (DNase I) is a human enzyme normally present in the mucus of human lungs that digests extracellular DNA. Genentech, Inc. cloned a highly purified recombinant DNase I (rhDNase or Pulmozyme) which when delivered to the lungs in an aerosolized form cuts extracellular DNA, reducing the viscoelasticity of airway secretions and improving clearance. In 1992 the company conducted a randomized double-blind trial comparing rhDNase to placebo. Patients were then monitored for pulmonary exacerbations, along with measures of lung volume and flow. The primary endpoint was the time until first pulmonary exacerbation; however, data on all exacerbations were collected for 169 days. The definition of an exacerbation was an infection that required the use of intravenous (IV) antibiotics. Subjects had 0--5 such episodes during the trial, those with more than one have multiple rows in the data set, those with none have NA for the IV start and end times. A few subjects were infected at the time of enrollment, subject 173 for instance has a first infection interval of -21 to 7. We do not count this first infection as an "event", and the subject first enters the risk set at day 7. Subjects who have an event are not considered to be at risk for another event during the course of antibiotics, nor for an additional 6 days after they end. (If the symptoms reappear immediately after cessation then from a medical standpoint this would not be a new infection.) This data set reproduces the data in Therneau and Grambsch, is does not exactly reproduce those in Therneau and Hamilton due to data set updates. } \references{ T. M. Therneau and P. M. Grambsch, Modeling Survival Data: Extending the Cox Model, Springer, 2000. T. M. Therneau and S.A. Hamilton, rhDNase as an example of recurrent event analysis, Statistics in Medicine, 16:2029-2047, 1997. } \examples{ # Build the start-stop data set for analysis, and # replicate line 2 of table 8.13 first <- subset(rhDNase, !duplicated(id)) #first row for each subject dnase <- tmerge(first, first, id=id, tstop=as.numeric(end.dt -entry.dt)) # Subjects whose fu ended during the 6 day window are the reason for # this next line temp.end <- with(rhDNase, pmin(ivstop+6, end.dt-entry.dt)) dnase <- tmerge(dnase, rhDNase, id=id, infect=event(ivstart), end= event(temp.end)) # toss out the non-at-risk intervals, and extra variables # 3 subjects had an event on their last day of fu, infect=1 and end=1 dnase <- subset(dnase, (infect==1 | end==0), c(id:trt, fev:infect)) agfit <- coxph(Surv(tstart, tstop, infect) ~ trt + fev, cluster=id, data=dnase) } \keyword{datasets} survival/man/aareg.Rd0000644000176200001440000001712713537676563014303 0ustar liggesusers\name{aareg} \alias{aareg} \title{ Aalen's additive regression model for censored data } \description{ Returns an object of class \code{"aareg"} that represents an Aalen model. } \usage{ aareg(formula, data, weights, subset, na.action, qrtol=1e-07, nmin, dfbeta=FALSE, taper=1, test = c('aalen', 'variance', 'nrisk'), model=FALSE, x=FALSE, y=FALSE) } \arguments{ \item{formula}{ a formula object, with the response on the left of a `~' operator and the terms, separated by \code{+} operators, on the right. The response must be a \code{Surv} object. Due to a particular computational approach that is used, the model MUST include an intercept term. If "-1" is used in the model formula the program will ignore it. } \item{data}{ data frame in which to interpret the variables named in the \code{formula}, \code{subset}, and \code{weights} arguments. This may also be a single number to handle some speci al cases -- see below for details. If \code{data} is missing, the variables in the model formula should be in the search path. } \item{weights}{ vector of observation weights. If supplied, the fitting algorithm minimizes the sum of the weights multiplied by the squared residuals (see below for additional technical details). The length of \code{weights} must be the same as the number of observations. The weights must be nonnegative and it i s recommended that they be strictly positive, since zero weights are ambiguous. To exclude particular observations from the model, use the \code{subset} argument instead of zero weights. } \item{subset}{ expression specifying which subset of observations should be used in the fit. Th is can be a logical vector (which is replicated to have length equal to the numb er of observations), a numeric vector indicating the observation numbers to be included, or a character vector of the observation names that should be included. All observations are included by default. } \item{na.action}{ a function to filter missing data. This is applied to the \code{model.fr ame} after any \code{subset} argument has be en applied. The default is \code{na.fail}, which returns a n error if any missing values are found. An alternative is \code{na.excl ude}, which deletes observations that contain one or more missing values. } \item{qrtol}{ tolerance for detection of singularity in the QR decomposition } \item{nmin}{ minimum number of observations for an estimate; defaults to 3 times the number of covariates. This essentially truncates the computations near the tail of the data set, when n is small and the calculations can become numerically unstable. } \item{dfbeta}{ should the array of dfbeta residuals be computed. This implies computation of the sandwich variance estimate. The residuals will always be computed if there is a \code{cluster} term in the model formula. } \item{taper}{ allows for a smoothed variance estimate. Var(x), where x is the set of covariates, is an important component of the calculations for the Aalen regression model. At any given time point t, it is computed over all subjects who are still at risk at time t. The tape argument allows smoothing these estimates, for example \code{taper=(1:4)/4} would cause the variance estimate used at any event time to be a weighted average of the estimated variance matrices at the last 4 death times, with a weight of 1 for the current death time and decreasing to 1/4 for prior event times. The default value gives the standard Aalen model. } \item{test}{ selects the weighting to be used, for computing an overall ``average'' coefficient vector over time and the subsequent test for equality to zero. } \item{model, x, y }{ should copies of the model frame, the x matrix of predictors, or the response vector y be included in the saved result. } } \value{ an object of class \code{"aareg"} representing the fit, with the following components: \item{n}{vector containing the number of observations in the data set, the number of event times, and the number of event times used in the computation} \item{times}{vector of sorted event times, which may contain duplicates} \item{nrisk}{vector containing the number of subjects at risk, of the same length as \code{times}} \item{coefficient}{matrix of coefficients, with one row per event and one column per covariate} \item{test.statistic}{the value of the test statistic, a vector with one element per covariate} \item{test.var}{variance-covariance matrix for the test} \item{test}{the type of test; a copy of the \code{test} argument above} \item{tweight}{matrix of weights used in the computation, one row per event} \item{call}{a copy of the call that produced this result} } \details{ The Aalen model assumes that the cumulative hazard H(t) for a subject can be expressed as a(t) + X B(t), where a(t) is a time-dependent intercept term, X is the vector of covariates for the subject (possibly time-dependent), and B(t) is a time-dependent matrix of coefficients. The estimates are inherently non-parametric; a fit of the model will normally be followed by one or more plots of the estimates. The estimates may become unstable near the tail of a data set, since the increment to B at time t is based on the subjects still at risk at time t. The tolerance and/or nmin parameters may act to truncate the estimate before the last death. The \code{taper} argument can also be used to smooth out the tail of the curve. In practice, the addition of a taper such as 1:10 appears to have little effect on death times when n is still reasonably large, but can considerably dampen wild occilations in the tail of the plot. } \section{References}{ Aalen, O.O. (1989). A linear regression model for the analysis of life times. Statistics in Medicine, 8:907-925. Aalen, O.O (1993). Further results on the non-parametric linear model in survival analysis. Statistics in Medicine. 12:1569-1588. } \seealso{ print.aareg, summary.aareg, plot.aareg } \examples{ # Fit a model to the lung cancer data set lfit <- aareg(Surv(time, status) ~ age + sex + ph.ecog, data=lung, nmin=1) \dontrun{ lfit Call: aareg(formula = Surv(time, status) ~ age + sex + ph.ecog, data = lung, nmin = 1 ) n=227 (1 observations deleted due to missing values) 138 out of 138 unique event times used slope coef se(coef) z p Intercept 5.26e-03 5.99e-03 4.74e-03 1.26 0.207000 age 4.26e-05 7.02e-05 7.23e-05 0.97 0.332000 sex -3.29e-03 -4.02e-03 1.22e-03 -3.30 0.000976 ph.ecog 3.14e-03 3.80e-03 1.03e-03 3.70 0.000214 Chisq=26.73 on 3 df, p=6.7e-06; test weights=aalen plot(lfit[4], ylim=c(-4,4)) # Draw a plot of the function for ph.ecog } lfit2 <- aareg(Surv(time, status) ~ age + sex + ph.ecog, data=lung, nmin=1, taper=1:10) \dontrun{lines(lfit2[4], col=2) # Nearly the same, until the last point} # A fit to the mulitple-infection data set of children with # Chronic Granuomatous Disease. See section 8.5 of Therneau and Grambsch. fita2 <- aareg(Surv(tstart, tstop, status) ~ treat + age + inherit + steroids + cluster(id), data=cgd) \dontrun{ n= 203 69 out of 70 unique event times used slope coef se(coef) robust se z p Intercept 0.004670 0.017800 0.002780 0.003910 4.55 5.30e-06 treatrIFN-g -0.002520 -0.010100 0.002290 0.003020 -3.36 7.87e-04 age -0.000101 -0.000317 0.000115 0.000117 -2.70 6.84e-03 inheritautosomal 0.001330 0.003830 0.002800 0.002420 1.58 1.14e-01 steroids 0.004620 0.013200 0.010600 0.009700 1.36 1.73e-01 Chisq=16.74 on 4 df, p=0.0022; test weights=aalen } } \keyword{survival} % docclass is function survival/man/Surv.Rd0000644000176200001440000001453213761063712014141 0ustar liggesusers\name{Surv} \alias{Surv} \alias{is.Surv} \alias{[.Surv} \title{ Create a Survival Object } \description{ Create a survival object, usually used as a response variable in a model formula. Argument matching is special for this function, see Details below. } \usage{ Surv(time, time2, event, type=c('right', 'left', 'interval', 'counting', 'interval2', 'mstate'), origin=0) is.Surv(x) } \arguments{ \item{time}{ for right censored data, this is the follow up time. For interval data, the first argument is the starting time for the interval. } \item{event}{ The status indicator, normally 0=alive, 1=dead. Other choices are \code{TRUE}/\code{FALSE} (\code{TRUE} = death) or 1/2 (2=death). For interval censored data, the status indicator is 0=right censored, 1=event at \code{time}, 2=left censored, 3=interval censored. For multiple endpoint data the event variable will be a factor, whose first level is treated as censoring. Although unusual, the event indicator can be omitted, in which case all subjects are assumed to have an event. } \item{time2}{ ending time of the interval for interval censored or counting process data only. Intervals are assumed to be open on the left and closed on the right, \code{(start, end]}. For counting process data, \code{event} indicates whether an event occurred at the end of the interval. } \item{type}{ character string specifying the type of censoring. Possible values are \code{"right"}, \code{"left"}, \code{"counting"}, \code{"interval"}, \code{"interval2"} or \code{"mstate"}. } \item{origin}{ for counting process data, the hazard function origin. This option was intended to be used in conjunction with a model containing time dependent strata in order to align the subjects properly when they cross over from one strata to another, but it has rarely proven useful.} \item{x}{ any R object. } } \value{ An object of class \code{Surv}. There are methods for \code{print}, \code{is.na}, and subscripting survival objects. \code{Surv} objects are implemented as a matrix of 2 or 3 columns that has further attributes. These include the type (left censored, right censored, counting process, etc.) and labels for the states for multi-state objects. Any attributes of the input arguments are also preserved in \code{inputAttributes}. This may be useful for other packages that have attached further information to data items such as labels; none of the routines in the survival package make use of these values, however. In the case of \code{is.Surv}, a logical value \code{TRUE} if \code{x} inherits from class \code{"Surv"}, otherwise an \code{FALSE}. } \details{ When the \code{type} argument is missing the code assumes a type based on the following rules: \itemize{ \item If there are two unnamed arguments, they will match \code{time} and \code{event} in that order. If there are three unnamed arguments they match \code{time}, \code{time2} and \code{event}. \item If the event variable is a factor then type \code{mstate} is assumed. Otherwise type \code{right} if there is no \code{time2} argument, and type \code{counting} if there is. } As a consequence the \code{type} argument will normally be omitted. When the survival type is "mstate" then the status variable will be treated as a factor. The first level of the factor is taken to represent censoring and remaining ones a transition to the given state. (If the status variable is a factor then \code{mstate} is assumed.) Interval censored data can be represented in two ways. For the first use \code{type = "interval"} and the codes shown above. In that usage the value of the \code{time2} argument is ignored unless event=3. The second approach is to think of each observation as a time interval with (-infinity, t) for left censored, (t, infinity) for right censored, (t,t) for exact and (t1, t2) for an interval. This is the approach used for type = interval2. Infinite values can be represented either by actual infinity (Inf) or NA. The second form has proven to be the more useful one. Presently, the only methods allowing interval censored data are the parametric models computed by \code{survreg} and survival curves computed by \code{survfit}; for both of these, the distinction between open and closed intervals is unimportant. The distinction is important for counting process data and the Cox model. The function tries to distinguish between the use of 0/1 and 1/2 coding for censored data via the condition \code{if (max(status)==2)}. If 1/2 coding is used and all the subjects are censored, it will guess wrong. In any questionable case it is safer to use logical coding, e.g., \code{Surv(time, status==3)} would indicate that '3' is the code for an event. For multi-state survival the status variable will be a factor, whose first level is assumed to correspond to censoring. Surv objects can be subscripted either as a vector, e.g. \code{x[1:3]} using a single subscript, in which case the \code{drop} argument is ignored and the result will be a survival object; or as a matrix by using two subscripts. If the second subscript is missing and \code{drop=F} (the default), the result of the subscripting will be a Surv object, e.g., \code{x[1:3,,drop=F]}, otherwise the result will be a matrix (or vector), in accordance with the default behavior for subscripting matrices. } \note{ The use of 1/2 coding for status is an interesting historical artifact. For data contained on punch cards, IBM 360 Fortran treated blank as a zero, which led to a policy within the Mayo Clinic section of Biostatistics to never use "0" as a data value since one could not distinguish it from a missing value. Policy became habit, as is often the case, and the use of 1/2 coding for alive/dead endured long after the demise of the punch cards that had sired the practice. At the time \code{Surv} was written many Mayo data sets still used this convention, e.g., the 1994 \code{lung} data set found in the package. } \seealso{ \code{\link{coxph}}, \code{\link{survfit}}, \code{\link{survreg}}, \code{\link{lung}}. } \examples{ with(aml, Surv(time, status)) survfit(Surv(time, status) ~ ph.ecog, data=lung) Surv(heart$start, heart$stop, heart$event) } \keyword{survival} survival/man/strata.Rd0000644000176200001440000000260013537676563014510 0ustar liggesusers\name{strata} \alias{strata} \title{ Identify Stratification Variables } \description{ This is a special function used in the context of the Cox survival model. It identifies stratification variables when they appear on the right hand side of a formula. } \usage{ strata(..., na.group=FALSE, shortlabel, sep=', ') } \arguments{ \item{\dots}{ any number of variables. All must be the same length. } \item{na.group}{ a logical variable, if \code{TRUE}, then missing values are treated as a distinct level of each variable. } \item{shortlabel}{if \code{TRUE} omit variable names from resulting factor labels. The default action is to omit the names if all of the arguments are factors, and none of them was named.} \item{sep}{ the character used to separate groups, in the created label } } \value{ a new factor, whose levels are all possible combinations of the factors supplied as arguments. } \details{ When used outside of a \code{coxph} formula the result of the function is essentially identical to the \code{interaction} function, though the labels from \code{strata} are often more verbose. } \seealso{ \code{\link{coxph}}, \code{\link{interaction}} } \examples{ a <- factor(rep(1:3,4), labels=c("low", "medium", "high")) b <- factor(rep(1:4,3)) levels(strata(b)) levels(strata(a,b,shortlabel=TRUE)) coxph(Surv(futime, fustat) ~ age + strata(rx), data=ovarian) } \keyword{survival} survival/man/Surv2.Rd0000644000176200001440000000306214004023664014210 0ustar liggesusers\name{Surv2} \alias{Surv2} \title{Create a survival object} \description{ Create a survival object from a timeline style data set. This will almost always be the response variable in a formula. } \usage{ Surv2(time, event, repeated=FALSE) } \arguments{ \item{time}{a timeline variable, such as age, time from enrollment, date, etc.} \item{event}{the outcome at that time. This can be a 0/1 variable, TRUE/FALSE, or a factor. If the latter, the first level of the factor corresponds to `no event was observed at this time'.} \item{repeated}{if the same level of the outcome repeats, without an intervening event of another type, should this be treated as a new event?} } \value{ An object of class \code{Surv2}. There are methods for \code{print}, \code{is.na} and subscripting. } \details{ This function is still experimental. When used in a \code{coxph} or \code{survfit} model, Surv2 acts as a trigger to internally convert a timeline style data set into counting process style data, which is then acted on by the routine. The \code{repeated} argument controls how repeated instances of the same event code are treated. If TRUE, they are treated as new events, an example where this might be desired is repeated infections in a subject. If FALSE, then repeats are not a new event. An example would be a data set where we wanted to use diabetes, say, as an endpoint, but this is repeated at each medical visit. } \seealso{ \code{\link{Surv2data}}, \code{\link{coxph}}, \code{\link{survfit}} } \keyword{survival} survival/man/dsurvreg.Rd0000644000176200001440000000630413537676563015060 0ustar liggesusers\name{dsurvreg} \alias{dsurvreg} \alias{psurvreg} \alias{qsurvreg} \alias{rsurvreg} \title{ Distributions available in survreg. } \description{ Density, cumulative distribution function, quantile function and random generation for the set of distributions supported by the \code{survreg} function. } \usage{ dsurvreg(x, mean, scale=1, distribution='weibull', parms) psurvreg(q, mean, scale=1, distribution='weibull', parms) qsurvreg(p, mean, scale=1, distribution='weibull', parms) rsurvreg(n, mean, scale=1, distribution='weibull', parms) } \arguments{ \item{x}{ vector of quantiles. Missing values (\code{NA}s) are allowed. } \item{q}{ vector of quantiles. Missing values (\code{NA}s) are allowed. } \item{p}{ vector of probabilities. Missing values (\code{NA}s) are allowed. } \item{n}{number of random deviates to produce} \item{mean}{vector of linear predictors for the model. This is replicated to be the same length as \code{p}, \code{q} or \code{n}. } \item{scale}{ vector of (positive) scale factors. This is replicated to be the same length as \code{p}, \code{q} or \code{n}. } \item{distribution}{ character string giving the name of the distribution. This must be one of the elements of \code{survreg.distributions} } \item{parms}{ optional parameters, if any, of the distribution. For the t-distribution this is the degrees of freedom. } } \value{ density (\code{dsurvreg}), probability (\code{psurvreg}), quantile (\code{qsurvreg}), or for the requested distribution with mean and scale parameters \code{mean} and \code{sd}. } \details{ Elements of \code{q} or \code{p} that are missing will cause the corresponding elements of the result to be missing. The \code{location} and \code{scale} values are as they would be for \code{survreg}. The label "mean" was an unfortunate choice (made in mimicry of qnorm); since almost none of these distributions are symmetric it will not actually be a mean, but corresponds instead to the linear predictor of a fitted model. Translation to the usual parameterization found in a textbook is not always obvious. For example, the Weibull distribution is fit using the Extreme value distribution along with a log transformation. Letting \eqn{F(t) = 1 - \exp[-(at)^p]}{F(t) = 1 - exp(-(at)^p)} be the cumulative distribution of the Weibull using a standard parameterization in terms of \eqn{a} and \eqn{p}, the survreg location corresponds to \eqn{-\log(a)}{-log(a)} and the scale to \eqn{1/p} (Kalbfleisch and Prentice, section 2.2.2). } \section{References}{ Kalbfleisch, J. D. and Prentice, R. L. (1970). \emph{The Statistical Analysis of Failure Time Data} Wiley, New York. } \seealso{ \code{\link{survreg}}, \code{\link{Normal}} } \examples{ # List of distributions available names(survreg.distributions) \dontrun{ [1] "extreme" "logistic" "gaussian" "weibull" "exponential" [6] "rayleigh" "loggaussian" "lognormal" "loglogistic" "t" } # Compare results all.equal(dsurvreg(1:10, 2, 5, dist='lognormal'), dlnorm(1:10, 2, 5)) # Hazard function for a Weibull distribution x <- seq(.1, 3, length=30) haz <- dsurvreg(x, 2, 3)/ (1-psurvreg(x, 2, 3)) \dontrun{ plot(x, haz, log='xy', ylab="Hazard") #line with slope (1/scale -1) } } \keyword{distribution} survival/man/survcheck.Rd0000644000176200001440000001105213640437051015166 0ustar liggesusers\name{survcheck} \alias{survcheck} \title{Checks of a survival data set} \description{ Perform a set of consistency checks on survival data } \usage{ survcheck(formula, data, subset, na.action, id, istate, istate0="(s0)", timefix=TRUE,...) } \arguments{ \item{formula}{a model formula with a \code{Surv} object as the response} \item{data}{data frame in which to find the \code{id}, \code{istate} and formula variables} \item{subset}{expression indicating which subset of the rows of data should be used in the fit. All observations are included by default. } \item{na.action}{ a missing-data filter function. This is applied to the model.frame after any subset argument has been used. Default is \code{options()\$na.action}. } \item{id}{an identifier that labels unique subjects} \item{istate}{an optional vector giving the current state at the start of each interval} \item{istate0}{default label for the initial state of each subject (at their first interval) when \code{istate} is missing} \item{timefix}{process times through the \code{aeqSurv} function to eliminate potential roundoff issues.} \item{\ldots}{other arguments, which are ignored (but won't give an error if someone added \code{weights} for instance)} } \details{ This routine will examine a multi-state data set for consistency of the data. The basic rules are that if a subject is at risk they have to be somewhere, can not be two states at once, and should make sensible transitions from state to state. It reports the number of instances of the following conditions: \describe{ \item{overlap}{two observations for the same subject that overlap in time, e.g. intervals of (0, 100) and (90, 120). If \code{y} is simple (time, status) survival observation intervals implicitly start at 0, so in that case any duplicate identifiers will generate an overlap.} \item{jump}{a hole in a subject's timeline, where they are in one state at the end of the prior interval, but a new state in the at the start subsequent interval.} \item{gap}{one or more gaps in a subject's timeline; they are presumably in the same state at their return as when they left.} \item{teleport}{two adjacent intervals for a subject, with the first interval ending in one state and the subsequent interval starting in another. They have instantaneously changed states with experiencing a transition.} } The total number of occurences of each is present in the \code{flags} vector. Optional components give the location and identifiers of the flagged observations. } \value{ a list with components \item{states}{the vector of possible states} \item{transitions}{a matrix giving the count of transitions from one state to another} \item{statecount}{table of the number of visits per state, e.g., 18 subjects had 2 visits to the "infection" state} \item{flags}{a vector giving the counts of each check} \item{istate}{a copy of the istate vector, if it was supplied; otherwise a constructed istate that satisfies all the checks} \item{overlap}{a list with the row number and id of overlaps (not present if there are no overlaps)} \item{gaps}{a list with the row number and id of gaps (not present if there are no gaps)} \item{teleport}{a list with the row number and id of inconsistent rows (not present if there are none)} \item{jumps}{a list with the row number and id of jumps (not present if there are no jumps} } \note{ For data sets with time-dependent covariates, a given subject will often have intermediate rows with a status of `no event at this time'. (numeric value of 0). For instance a subject who started in state 1 at time 0, transitioned to state 2 at time 10, had a covariate \code{x} change from 135 to 156 at time 20, and a final transition to state 3 at time 30. The response would be \code{Surv(c(0, 10, 20), c(10, 20, 30), c(2,0,3))}: the status variable records \emph{changes} in state, and there was no change at time 20. The \code{istate} variable would be (1, 2, 2); it contains the \emph{current} state, and so the value is unchanged when status = censored. Thus, when there are intermediate observations \code{istate} is not simply a lagged version of the status, and may be more challenging to create. One approach is to let \code{survcheck} do the work: call it with an \code{istate} argument that is correct for the first row of each subject, or no \code{istate} argument at all, and then insert the returned value into ones data frame. } \keyword{ survival } survival/man/uspop2.Rd0000644000176200001440000000202713744672570014437 0ustar liggesusers\name{uspop2} \alias{uspop2} \docType{data} \title{Projected US Population} \description{US population by age and sex, for 2000 through 2020} \format{ The data is a matrix with dimensions age, sex, and calendar year. Age goes from 0 through 100, where the value for age 100 is the total for all ages of 100 or greater. } \details{ This data is often used as a "standardized" population for epidemiology studies.} \source{ NP2008_D1: Projected Population by Single Year of Age, Sex, Race, and Hispanic Origin for the United States: July 1, 2000 to July 1, 2050, www.census.gov/population/projections. } \examples{ us50 <- uspop2[51:101,, "2000"] #US 2000 population, 50 and over age <- as.integer(dimnames(us50)[[1]]) smat <- model.matrix( ~ factor(floor(age/5)) -1) ustot <- t(smat) \%*\% us50 #totals by 5 year age groups temp <- c(50,55, 60, 65, 70, 75, 80, 85, 90, 95) dimnames(ustot) <- list(c(paste(temp, temp+4, sep="-"), "100+"), c("male", "female")) } \seealso{\code{\link{uspop}}} \keyword{datasets} survival/man/levels.Surv.Rd0000644000176200001440000000116213537676563015444 0ustar liggesusers\name{levels.Surv} \alias{levels.Surv} \title{Return the states of a multi-state Surv object } \description{ For a multi-state \code{Surv} object, this will return the names of the states. } \usage{ \method{levels}{Surv}(x) } \arguments{ \item{x}{a \code{Surv} object} } \value{ for a multi-state \code{Surv} object, the vector of state names (excluding censoring); or NULL for an ordinary \code{Surv} object } \examples{ y1 <- Surv(c(1,5, 9, 17,21, 30), factor(c(0, 1, 2,1,0,2), 0:2, c("censored", "progression", "death"))) levels(y1) y2 <- Surv(1:6, rep(0:1, 3)) y2 levels(y2) } \keyword{ survival } survival/man/vcov.coxph.Rd0000644000176200001440000000231113537676563015306 0ustar liggesusers\name{vcov.coxph} \alias{vcov.coxph} \alias{vcov.survreg} \title{Variance-covariance matrix} \description{Extract and return the variance-covariance matrix.} \usage{ \method{vcov}{coxph}(object, complete=TRUE, ...) \method{vcov}{survreg}(object, complete=TRUE, ...) } \arguments{ \item{object}{a fitted model object} \item{complete}{logical indicating if the full variance-covariance matrix should be returned. This has an effect only for an over-determined fit where some of the coefficients are undefined, and \code{coef(object)} contains corresponding NA values. If \code{complete=TRUE} the returned matrix will have row/column for each coefficient, if FALSE it will contain rows/columns corresponding to the non-missing coefficients. The coef() function has a simpilar \code{complete} argument. } \item{\ldots}{additional arguments for method functions} } \value{a matrix} \details{ For the \code{coxph} and \code{survreg} functions the returned matrix is a particular generalized inverse: the row and column corresponding to any NA coefficients will be zero. This is a side effect of the generalized cholesky decomposion used in the unerlying compuatation. } \keyword{survival} survival/man/yates.Rd0000644000176200001440000000500113537676563014335 0ustar liggesusers\name{yates} \alias{yates} \title{Population prediction} \description{Compute population marginal means (PMM) from a model fit, for a chosen population and statistic. } \usage{ yates(fit, term, population = c("data", "factorial", "sas"), levels, test = c("global", "trend", "pairwise"), predict = "linear", options, nsim = 200, method = c("direct", "sgtt")) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{fit}{a model fit. Examples using lm, glm, and coxph objects are given in the vignette. } \item{term}{the term from the model whic is to be evaluated. This can be written as a character string or as a formula. } \item{population}{the population to be used for the adjusting variables. User can supply their own data frame or select one of the built in choices. The argument also allows "empirical" and "yates" as aliases for data and factorial, respectively, and ignores case. } \item{levels}{optional, what values for \code{term} should be used. } \item{test}{the test for comparing the population predictions. } \item{predict}{what to predict. For a glm model this might be the 'link' or 'response'. For a coxph model it can be linear, risk, or survival. User written functions are allowed. } \item{options}{optional arguments for the prediction method. } \item{nsim}{number of simulations used to compute a variance for the predictions. This is not needed for the linear predictor. } \item{method}{the computational approach for testing equality of the population predictions. Either the direct approach or the algorithm used by the SAS glim procedure for "type 3" tests. } } \details{ The many options and details of this function are best described in a vignette on population prediction. } \value{an object of class \code{yates} with components of \item{estimate}{a data frame with one row for each level of the term, and columns containing the level, the mean population predicted value (mppv) and its standard deviation.} \item{tests}{a matrix giving the test statistics} \item{mvar}{the full variance-covariance matrix of the mppv values} \item{summary}{optional: any further summary if the values provided by the prediction method.} } \author{Terry Therneau} \examples{ fit1 <- lm(skips ~ Solder*Opening + Mask, data = solder) yates(fit1, ~Opening, population = "factorial") fit2 <- coxph(Surv(time, status) ~ factor(ph.ecog)*sex + age, lung) yates(fit2, ~ ph.ecog, predict="risk") # hazard ratio } \keyword{ models } \keyword{ survival } survival/man/attrassign.Rd0000644000176200001440000000333313537676563015375 0ustar liggesusers\name{attrassign} \alias{attrassign.default} \alias{attrassign} \alias{attrassign.lm} \title{Create new-style "assign" attribute} \description{ The \code{"assign"} attribute on model matrices describes which columns come from which terms in the model formula. It has two versions. R uses the original version, but the alternate version found in S-plus is sometimes useful. } \usage{ \method{attrassign}{default}(object, tt,...) \method{attrassign}{lm}(object,...) } %- maybe also `usage' for other objects documented here. \arguments{ \item{object}{model matrix or linear model object} \item{tt}{terms object} \item{...}{ignored} } \value{ A list with names corresponding to the term names and elements that are vectors indicating which columns come from which terms } \details{ For instance consider the following \preformatted{ survreg(Surv(time, status) ~ age + sex + factor(ph.ecog), lung) } R gives the compact for for assign, a vector (0, 1, 2, 3, 3, 3); which can be read as ``the first column of the X matrix (intercept) goes with none of the terms, the second column of X goes with term 1 of the model equation, the third column of X with term 2, and columns 4-6 with term 3''. The alternate (S-Plus default) form is a list \preformatted{ $(Intercept) 1 $age 2 $sex 3 $factor(ph.ecog) 4 5 6 } } \seealso{\code{\link{terms}},\code{\link{model.matrix}}} \examples{ formula <- Surv(time,status)~factor(ph.ecog) tt <- terms(formula) mf <- model.frame(tt,data=lung) mm <- model.matrix(tt,mf) ## a few rows of data mm[1:3,] ## old-style assign attribute attr(mm,"assign") ## alternate style assign attribute attrassign(mm,tt) } \keyword{models} survival/man/pspline.Rd0000644000176200001440000000743714013474062014655 0ustar liggesusers\name{pspline} \alias{pspline} \alias{psplineinverse} \title{Smoothing splines using a pspline basis} \usage{ pspline(x, df=4, theta, nterm=2.5 * df, degree=3, eps=0.1, method, Boundary.knots=range(x), intercept=FALSE, penalty=TRUE, combine, ...) psplineinverse(x)} \arguments{ \item{x}{for psline: a covariate vector. The function does not apply to factor variables. For psplineinverse x will be the result of a pspline call.} \item{df}{the desired degrees of freedom. One of the arguments \code{df} or \code{theta}' must be given, but not both. If \code{df=0}, then the AIC = (loglik -df) is used to choose an "optimal" degrees of freedom. If AIC is chosen, then an optional argument `caic=T' can be used to specify the corrected AIC of Hurvich et. al. } \item{theta}{roughness penalty for the fit. It is a monotone function of the degrees of freedom, with theta=1 corresponding to a linear fit and theta=0 to an unconstrained fit of nterm degrees of freedom. } \item{nterm}{ number of splines in the basis } \item{degree}{ degree of splines } \item{eps}{accuracy for \code{df} } \item{method}{the method for choosing the tuning parameter \code{theta}. If theta is given, then 'fixed' is assumed. If the degrees of freedom is given, then 'df' is assumed. If method='aic' then the degrees of freedom is chosen automatically using Akaike's information criterion.} \item{\dots}{optional arguments to the control function} \item{Boundary.knots}{the spline is linear beyond the boundary knots. These default to the range of the data.} \item{intercept}{if TRUE, the basis functions include the intercept.} \item{penalty}{if FALSE a large number of attributes having to do with penalized fits are excluded. This is useful to create a pspline basis matrix for other uses.} \item{combine}{an optional vector of increasing integers. If two adjacent values of \code{combine} are equal, then the corresponding coefficients of the fit are forced to be equal. This is useful for monotone fits, see the vignette for more details. } } \description{ Specifies a penalised spline basis for the predictor. This is done by fitting a comparatively small set of splines and penalising the integrated second derivative. Traditional smoothing splines use one basis per observation, but several authors have pointed out that the final results of the fit are indistinguishable for any number of basis functions greater than about 2-3 times the degrees of freedom. Eilers and Marx point out that if the basis functions are evenly spaced, this leads to significant computational simplification, they refer to the result as a p-spline. } \value{ Object of class \code{pspline, coxph.penalty} containing the spline basis, with the appropriate attributes to be recognized as a penalized term by the coxph or survreg functions. For psplineinverse the original x vector is reconstructed. } \seealso{\code{\link{coxph}},\code{\link{survreg}},\code{\link{ridge}}, \code{\link{frailty}} } \references{ Eilers, Paul H. and Marx, Brian D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11, 89-121. Hurvich, C.M. and Simonoff, J.S. and Tsai, Chih-Ling (1998). Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion, JRSSB, volume 60, 271--293. } \examples{ lfit6 <- survreg(Surv(time, status)~pspline(age, df=2), lung) plot(lung$age, predict(lfit6), xlab='Age', ylab="Spline prediction") title("Cancer Data") fit0 <- coxph(Surv(time, status) ~ ph.ecog + age, lung) fit1 <- coxph(Surv(time, status) ~ ph.ecog + pspline(age,3), lung) fit3 <- coxph(Surv(time, status) ~ ph.ecog + pspline(age,8), lung) fit0 fit1 fit3 } \keyword{ survival} survival/man/agreg.fit.Rd0000644000176200001440000000357614012554315015050 0ustar liggesusers\name{agreg.fit} \alias{agreg.fit} \alias{coxph.fit} \title{Cox model fitting functions} \description{ These are the the functions called by coxph that do the actual computation. In certain situations, e.g. a simulation, it may be advantageous to call these directly rather than the usual \code{coxph} call using a model formula. } \usage{ agreg.fit(x, y, strata, offset, init, control, weights, method, rownames, resid=TRUE, nocenter=NULL) coxph.fit(x, y, strata, offset, init, control, weights, method, rownames, resid=TRUE, nocenter=NULL) } \arguments{ \item{x}{Matix of predictors. This should \emph{not} include an intercept.} \item{y}{a \code{Surv} object containing either 2 columns (coxph.fit) or 3 columns (agreg.fit).} \item{strata}{a vector containing the stratification, or NULL} \item{offset}{optional offset vector} \item{init}{initial values for the coefficients} \item{control}{the result of a call to \code{coxph.control}} \item{weights}{optional vector of weights} \item{method}{method for hanling ties, one of "breslow" or "efron"} \item{rownames}{this is only needed for a NULL model, in which case it contains the rownames (if any) of the original data.} \item{resid}{compute and return residuals.} \item{nocenter}{an optional list of values. Any column of the X matrix whose values lie strictly within that set will not be recentered. Note that the coxph function has (-1, 0, 1) as the default.} } \details{ This routine does no checking that arguments are the proper length or type. Only use it if you know what you are doing! The \code{resid} and \code{concordance} arguments will save some compute time for calling routines that only need the likelihood, the generation of a permutation distribution for instance. } \value{ a list containing results of the fit} \author{Terry Therneau} \seealso{\code{\link{coxph}}} \keyword{ survival } survival/man/survexp.object.Rd0000644000176200001440000000411313537676563016174 0ustar liggesusers\name{survexp.object} \alias{survexp.object} \title{ Expected Survival Curve Object } \description{ This class of objects is returned by the \code{survexp} class of functions to represent a fitted survival curve. Objects of this class have methods for \code{summary}, and inherit the \code{print}, \code{plot}, \code{points} and \code{lines} methods from \code{survfit}. } \section{Structure}{ The following components must be included in a legitimate \code{survfit} object. } \arguments{ \item{surv}{ the estimate of survival at time t+0. This may be a vector or a matrix. } \item{n.risk}{ the number of subjects who contribute at this time. } \item{time}{ the time points at which the curve has a step. } \item{std.err}{ the standard error of the cumulative hazard or -log(survival). } \item{strata}{ if there are multiple curves, this component gives the number of elements of the \code{time} etc. vectors corresponding to the first curve, the second curve, and so on. The names of the elements are labels for the curves. } \item{method}{the estimation method used. One of "Ederer", "Hakulinen", or "conditional".} \item{na.action}{ the returned value from the na.action function, if any. It will be used in the printout of the curve, e.g., the number of observations deleted due to missing values. } \item{call}{ an image of the call that produced the object. } } \section{Subscripts}{ Survexp objects that contain multiple survival curves can be subscripted. This is most often used to plot a subset of the curves. } \section{Details}{ In expected survival each subject from the data set is matched to a hypothetical person from the parent population, matched on the characteristics of the parent population. The number at risk printed here is the number of those hypothetical subject who are still part of the calculation. In particular, for the Ederer method all hypotheticals are retained for all time, so \code{n.risk} will be a constant. } \seealso{ \code{\link{plot.survfit}}, \code{\link{summary.survexp}}, \code{\link{print.survfit}}, \code{\link{survexp}}. } \keyword{survival} survival/man/myeloid.Rd0000644000176200001440000000245414013460434014635 0ustar liggesusers\name{myeloid} \alias{myeloid} \docType{data} \title{Acute myeloid leukemia} \description{ This simulated data set is based on a trial in acute myeloid leukemia. } \usage{myeloid data(cancer, package="survival") } \format{ A data frame with 646 observations on the following 9 variables. \describe{ \item{\code{id}}{subject identifier, 1-646} \item{\code{trt}}{treatment arm A or B} \item{\code{sex}}{f=female, m=male} \item{\code{futime}}{time to death or last follow-up} \item{\code{death}}{1 if \code{futime} is a death, 0 for censoring} \item{\code{txtime}}{time to hematropetic stem cell transplant} \item{\code{crtime}}{time to complete response} \item{\code{rltime}}{time to relapse of disease} } } \details{ This data set is used to illustrate multi-state survival curves. The correlation between within-subject event times strongly resembles that from an actual trial, but none of the actual data values are from that source. } \examples{ coxph(Surv(futime, death) ~ trt, data=myeloid) # See the mstate vignette for a more complete analysis } \keyword{datasets} \references{ Le-Rademacher JG, Peterson RA, Therneau TM, Sanford BL, Stone RM, Mandrekar SJ. Application of multi-state models in cancer clinical trials. Clin Trials. 2018 Oct; 15 (5):489-498 }survival/man/predict.coxph.Rd0000644000176200001440000001344513727654272015767 0ustar liggesusers\name{predict.coxph} \alias{predict.coxph} \alias{predict.coxph.penal} \title{ Predictions for a Cox model } \description{ Compute fitted values and regression terms for a model fitted by \code{\link{coxph}} } \usage{ \method{predict}{coxph}(object, newdata, type=c("lp", "risk", "expected", "terms", "survival"), se.fit=FALSE, na.action=na.pass, terms=names(object$assign), collapse, reference=c("strata", "sample", "zero"), ...) } \arguments{ \item{object}{ the results of a coxph fit. } \item{newdata}{ Optional new data at which to do predictions. If absent predictions are for the data frame used in the original fit. When coxph has been called with a formula argument created in another context, i.e., coxph has been called within another function and the formula was passed as an argument to that function, there can be problems finding the data set. See the note below. } \item{type}{ the type of predicted value. Choices are the linear predictor (\code{"lp"}), the risk score exp(lp) (\code{"risk"}), the expected number of events given the covariates and follow-up time (\code{"expected"}), and the terms of the linear predictor (\code{"terms"}). The survival probability for a subject is equal to exp(-expected). } \item{se.fit}{ if TRUE, pointwise standard errors are produced for the predictions. } \item{na.action}{ applies only when the \code{newdata} argument is present, and defines the missing value action for the new data. The default is to include all observations. When there is no newdata, then the behavior of missing is dictated by the na.action option of the original fit.} \item{terms}{ if type="terms", this argument can be used to specify which terms should be included; the default is all. } \item{collapse}{ optional vector of subject identifiers. If specified, the output will contain one entry per subject rather than one entry per observation. } \item{reference}{reference for centering predictions, see details below} \item{\dots}{For future methods} } \value{ a vector or matrix of predictions, or a list containing the predictions (element "fit") and their standard errors (element "se.fit") if the se.fit option is TRUE. } \details{ The Cox model is a \emph{relative} risk model; predictions of type "linear predictor", "risk", and "terms" are all relative to the sample from which they came. By default, the reference value for each of these is the mean covariate within strata. The underlying reason is both statistical and practial. First, a Cox model only predicts relative risks between pairs of subjects within the same strata, and hence the addition of a constant to any covariate, either overall or only within a particular stratum, has no effect on the fitted results. Second, downstream calculations depend on the risk score exp(linear predictor), which will fall prey to numeric overflow for a linear predictor greater than \code{.Machine\$double.max.exp}. The \code{coxph} routines try to approximately center the predictors out of self protection. Using the \code{reference="strata"} option is the safest centering, since strata occassionally have different means. When the results of \code{predict} are used in further calculations it may be desirable to use a single reference level for all observations. Use of \code{reference="sample"} will use the overall means, and agrees with the \code{linear.predictors} component of the coxph object (which uses the overall mean for backwards compatability with older code). Predictions of \code{type="terms"} are almost invariably passed forward to further calculation, so for these we default to using the sample as the reference. A reference of \code{"zero"} causes no centering to be done. Predictions of type "expected" incorporate the baseline hazard and are thus absolute instead of relative; the \code{reference} option has no effect on these. These values depend on the follow-up time for the future subjects as well as covariates so the \code{newdata} argument needs to include both the right and \emph{left} hand side variables from the formula. (The status variable will not be used, but is required since the underlying code needs to reconstruct the entire formula.) Models that contain a \code{frailty} term are a special case: due to the technical difficulty, when there is a \code{newdata} argument the predictions will always be for a random effect of zero. } \note{ Some predictions can be obtained directly from the coxph object, and for others it is necessary for the routine to have the entirety of the original data set, e.g., for type = \code{terms} or if standard errors are requested. This extra information is saved in the coxph object if \code{model=TRUE}, if not the original data is reconstructed. If it is known that such residuals will be required overall execution will be slightly faster if the model information is saved. In some cases the reconstruction can fail. The most common is when coxph has been called inside another function and the formula was passed as one of the arguments to that enclosing function. Another is when the data set has changed between the original call and the time of the prediction call. In each of these the simple solution is to add \code{model=TRUE} to the original coxph call. } \seealso{ \code{\link{predict}},\code{\link{coxph}},\code{\link{termplot}} } \examples{ options(na.action=na.exclude) # retain NA in predictions fit <- coxph(Surv(time, status) ~ age + ph.ecog + strata(inst), lung) #lung data set has status coded as 1/2 mresid <- (lung$status-1) - predict(fit, type='expected') #Martingale resid predict(fit,type="lp") predict(fit,type="expected") predict(fit,type="risk",se.fit=TRUE) predict(fit,type="terms",se.fit=TRUE) # For someone who demands reference='zero' pzero <- function(fit) predict(fit, reference="sample") + sum(coef(fit) * fit$means, na.rm=TRUE) } \keyword{survival} survival/man/blogit.Rd0000644000176200001440000000420414005771437014457 0ustar liggesusers\name{blogit} \alias{blogit} \alias{bcloglog} \alias{bprobit} \alias{blog} \title{ Bounded link functions } \description{ Alternate link functions that impose bounds on the input of their link function } \usage{ blogit(edge = 0.05) bprobit(edge= 0.05) bcloglog(edge=.05) blog(edge=.05) } \arguments{ \item{edge}{input values less than the cutpoint are replaces with the cutpoint. For all be \code{blog} input values greater than (1-edge) are replaced with (1-edge)} } \details{ When using survival psuedovalues for binomial regression, the raw data can be outside the range (0,1), yet we want to restrict the predicted values to lie within that range. A natural way to deal with this is to use \code{glm} with \code{family = gaussian(link= "logit")}. But this will fail. The reason is that the \code{family} object has a component \code{linkfun} that does not accept values outside of (0,1). This function is only used to create initial values for the iteration step, however. Mapping the offending input argument into the range of (egde, 1-edge) before computing the link results in starting estimates that are good enough. The final result of the fit will be no different than if explicit starting estimates were given using the \code{etastart} or \code{mustart} arguments. These functions create copies of the logit, probit, and complimentary log-log families that differ from the standard ones only in this use of a bounded input argument, and are called a "bounded logit" = \code{blogit}, etc. The same argument hold when using RMST (area under the curve) pseudovalues along with a log link to ensure positive predictions, though in this case only the lower boundary needs to be mapped. } \value{a \code{family} object of the same form as \code{make.family}. } \seealso{\code{\link{stats}{make.family}}} \examples{ py <- pseudo(survfit(Surv(time, status) ~1, lung), time=730) #2 year survival range(py) pfit <- glm(py ~ ph.ecog, data=lung, family=gaussian(link=blogit())) # For each +1 change in performance score, the odds of 2 year survival # are multiplied by 1/2 = exp of the coefficient. } \keyword{survival}survival/man/survdiff.Rd0000644000176200001440000000724113537676563015050 0ustar liggesusers\name{survdiff} \alias{survdiff} \alias{print.survdiff} \title{ Test Survival Curve Differences } \description{ Tests if there is a difference between two or more survival curves using the \eqn{G^\rho}{G-rho} family of tests, or for a single curve against a known alternative. } \usage{ survdiff(formula, data, subset, na.action, rho=0, timefix=TRUE) } \arguments{ \item{formula}{ a formula expression as for other survival models, of the form \code{Surv(time, status) ~ predictors}. For a one-sample test, the predictors must consist of a single \code{offset(sp)} term, where \code{sp} is a vector giving the survival probability of each subject. For a k-sample test, each unique combination of predictors defines a subgroup. A \code{strata} term may be used to produce a stratified test. To cause missing values in the predictors to be treated as a separate group, rather than being omitted, use the \code{strata} function with its \code{na.group=T} argument. } \item{data}{ an optional data frame in which to interpret the variables occurring in the formula. } \item{subset}{ expression indicating which subset of the rows of data should be used in the fit. This can be a logical vector (which is replicated to have length equal to the number of observations), a numeric vector indicating which observation numbers are to be included (or excluded if negative), or a character vector of row names to be included. All observations are included by default. } \item{na.action}{ a missing-data filter function. This is applied to the \code{model.frame} after any subset argument has been used. Default is \code{options()$na.action}. } \item{rho}{ a scalar parameter that controls the type of test. } \item{timefix}{process times through the \code{aeqSurv} function to eliminate potential roundoff issues.} } \value{ a list with components: \item{n}{ the number of subjects in each group. } \item{obs}{ the weighted observed number of events in each group. If there are strata, this will be a matrix with one column per stratum. } \item{exp}{ the weighted expected number of events in each group. If there are strata, this will be a matrix with one column per stratum. } \item{chisq}{ the chisquare statistic for a test of equality. } \item{var}{ the variance matrix of the test. } \item{strata}{ optionally, the number of subjects contained in each stratum. }} \section{METHOD}{ This function implements the G-rho family of Harrington and Fleming (1982), with weights on each death of \eqn{S(t)^\rho}{S(t)^rho}, where \eqn{S(t)}{S} is the Kaplan-Meier estimate of survival. With \code{rho = 0} this is the log-rank or Mantel-Haenszel test, and with \code{rho = 1} it is equivalent to the Peto & Peto modification of the Gehan-Wilcoxon test. If the right hand side of the formula consists only of an offset term, then a one sample test is done. To cause missing values in the predictors to be treated as a separate group, rather than being omitted, use the \code{factor} function with its \code{exclude} argument. } \references{ Harrington, D. P. and Fleming, T. R. (1982). A class of rank test procedures for censored survival data. \emph{Biometrika} \bold{69}, 553-566.} \examples{ ## Two-sample test survdiff(Surv(futime, fustat) ~ rx,data=ovarian) ## Stratified 7-sample test survdiff(Surv(time, status) ~ pat.karno + strata(inst), data=lung) ## Expected survival for heart transplant patients based on ## US mortality tables expect <- survexp(futime ~ 1, data=jasa, cohort=FALSE, rmap= list(age=(accept.dt - birth.dt), sex=1, year=accept.dt), ratetable=survexp.us) ## actual survival is much worse (no surprise) survdiff(Surv(jasa$futime, jasa$fustat) ~ offset(expect)) } \keyword{survival} survival/man/aeqSurv.Rd0000644000176200001440000000255613537676563014652 0ustar liggesusers\name{aeqSurv} \alias{aeqSurv} \title{Adjudicate near ties in a Surv object} \description{The check for tied survival times can fail due to floating point imprecision, which can make actual ties appear to be distinct values. Routines that depend on correct identification of ties pairs will then give incorrect results, e.g., a Cox model. This function rectifies these. } \usage{ aeqSurv(x, tolerance = sqrt(.Machine$double.eps)) } \arguments{ \item{x}{a Surv object} \item{tolerance}{the tolerance used to detect values that will be considered equal} } \details{ This routine is called by both \code{survfit} and \code{coxph} to deal with the issue of ties that get incorrectly broken due to floating point imprecision. See the short vignette on tied times for a simple example. Use the \code{timefix} argument of \code{survfit} or \code{coxph.control} to control the option if desired. The rule for `equality' is identical to that used by the \code{all.equal} routine. Pairs of values that are within round off error of each other are replaced by the smaller value. An error message is generated if this process causes a 0 length time interval to be created. } \value{a Surv object identical to the original, but with ties restored.} \author{Terry Therneau} \seealso{\code{\link{survfit}}, \code{\link{coxph.control}}} \keyword{ survival } survival/man/predict.survreg.Rd0000644000176200001440000000547713537676563016357 0ustar liggesusers\name{predict.survreg} \alias{predict.survreg} \alias{predict.survreg.penal} \title{ Predicted Values for a `survreg' Object } \description{ Predicted values for a \code{survreg} object } \usage{ \method{predict}{survreg}(object, newdata, type=c("response", "link", "lp", "linear", "terms", "quantile", "uquantile"), se.fit=FALSE, terms=NULL, p=c(0.1, 0.9), na.action=na.pass, ...) } \arguments{ \item{object}{ result of a model fit using the \code{survreg} function. } \item{newdata}{ data for prediction. If absent predictions are for the subjects used in the original fit. } \item{type}{ the type of predicted value. This can be on the original scale of the data (response), the linear predictor (\code{"linear"}, with \code{"lp"} as an allowed abbreviation), a predicted quantile on the original scale of the data (\code{"quantile"}), a quantile on the linear predictor scale (\code{"uquantile"}), or the matrix of terms for the linear predictor (\code{"terms"}). At this time \code{"link"} and linear predictor (\code{"lp"}) are identical. } \item{se.fit}{ if \code{TRUE}, include the standard errors of the prediction in the result. } \item{terms}{ subset of terms. The default for residual type \code{"terms"} is a matrix with one column for every term (excluding the intercept) in the model. } \item{p}{ vector of percentiles. This is used only for quantile predictions. } \item{na.action}{ applies only when the \code{newdata} argument is present, and defines the missing value action for the new data. The default is to include all observations.} \item{\dots}{for future methods} } \value{ a vector or matrix of predicted values. } \references{ Escobar and Meeker (1992). Assessing influence in regression analysis with censored data. \emph{Biometrics,} 48, 507-528. } \seealso{ \code{\link{survreg}}, \code{\link{residuals.survreg}} } \examples{ # Draw figure 1 from Escobar and Meeker, 1992. fit <- survreg(Surv(time,status) ~ age + I(age^2), data=stanford2, dist='lognormal') with(stanford2, plot(age, time, xlab='Age', ylab='Days', xlim=c(0,65), ylim=c(.1, 10^5), log='y', type='n')) with(stanford2, points(age, time, pch=c(2,4)[status+1], cex=.7)) pred <- predict(fit, newdata=list(age=1:65), type='quantile', p=c(.1, .5, .9)) matlines(1:65, pred, lty=c(2,1,2), col=1) # Predicted Weibull survival curve for a lung cancer subject with # ECOG score of 2 lfit <- survreg(Surv(time, status) ~ ph.ecog, data=lung) pct <- 1:98/100 # The 100th percentile of predicted survival is at +infinity ptime <- predict(lfit, newdata=data.frame(ph.ecog=2), type='quantile', p=pct, se=TRUE) matplot(cbind(ptime$fit, ptime$fit + 2*ptime$se.fit, ptime$fit - 2*ptime$se.fit)/30.5, 1-pct, xlab="Months", ylab="Survival", type='l', lty=c(1,2,2), col=1) } \keyword{survival} survival/man/veteran.Rd0000644000176200001440000000144114013460646014637 0ustar liggesusers\name{veteran} \alias{veteran} \docType{data} \title{Veterans' Administration Lung Cancer study} \description{Randomised trial of two treatment regimens for lung cancer. This is a standard survival analysis data set.} \usage{veteran data(cancer, package="survival") } \format{ \tabular{ll}{ trt:\tab 1=standard 2=test\cr celltype:\tab 1=squamous, 2=smallcell, 3=adeno, 4=large\cr time:\tab survival time\cr status:\tab censoring status\cr karno:\tab Karnofsky performance score (100=good)\cr diagtime:\tab months from diagnosis to randomisation\cr age:\tab in years\cr prior:\tab prior therapy 0=no, 10=yes\cr } } \source{ D Kalbfleisch and RL Prentice (1980), \emph{The Statistical Analysis of Failure Time Data}. Wiley, New York. } \keyword{datasets} survival/man/aml.Rd0000644000176200001440000000122214013461011013724 0ustar liggesusers\name{aml} \docType{data} \alias{aml} \alias{leukemia} \title{Acute Myelogenous Leukemia survival data} \description{Survival in patients with Acute Myelogenous Leukemia. The question at the time was whether the standard course of chemotherapy should be extended ('maintainance') for additional cycles.} \usage{ aml leukemia data(cancer, package="survival") } \format{ \tabular{ll}{ time:\tab survival or censoring time\cr status:\tab censoring status\cr x: \tab maintenance chemotherapy given? (factor)\cr } } \source{ Rupert G. Miller (1997), \emph{Survival Analysis}. John Wiley & Sons. ISBN: 0-471-25218-2. } \keyword{datasets} survival/man/myeloma.Rd0000644000176200001440000000212114073031214014621 0ustar liggesusers\name{myeloma} \alias{myeloma} \docType{data} \title{ Survival times of patients with multiple myeloma } \description{ Survival times of 3882 subjects with multiple myeloma, seen at Mayo Clinic from 1947--1996. } \usage{myeloma data("cancer", package="survival")} \format{ A data frame with 3882 observations on the following 5 variables. \describe{ \item{\code{id}}{subject identifier} \item{\code{year}}{year of entry into the study} \item{\code{entry}}{time from diagnosis of MM until entry (days)} \item{\code{futime}}{follow up time (days)} \item{\code{death}}{status at last follow-up: 0 = alive, 1 = death} } } \details{ Subjects who were diagnosed at Mayo will have \code{entry} =0, those who were diagnosed elsewhere and later referred will have positive values. } \references{ R. Kyle, Long term survival in multiple myeloma. New Eng J Medicine, 1997 } \examples{ # Incorrect survival curve, which ignores left truncation fit1 <- survfit(Surv(futime, death) ~ 1, myeloma) # Correct curve fit2 <- survfit(Surv(entry, futime, death) ~1, myeloma) } \keyword{datasets} survival/man/plot.aareg.Rd0000644000176200001440000000155113537676563015252 0ustar liggesusers\name{plot.aareg} \alias{plot.aareg} \title{ Plot an aareg object. } \description{ Plot the estimated coefficient function(s) from a fit of Aalen's additive regression model. } \usage{ \method{plot}{aareg}(x, se=TRUE, maxtime, type='s', ...) } \arguments{ \item{x}{ the result of a call to the \code{aareg} function } \item{se}{ if TRUE, standard error bands are included on the plot } \item{maxtime}{ upper limit for the x-axis. } \item{type}{ graphical parameter for the type of line, default is "steps". } \item{\dots }{ other graphical parameters such as line type, color, or axis labels. } } \section{Side Effects}{ A plot is produced on the current graphical device. } \section{References}{ Aalen, O.O. (1989). A linear regression model for the analysis of life times. Statistics in Medicine, 8:907-925. } \seealso{ aareg } survival/man/coxph.wtest.Rd0000644000176200001440000000123013537676563015476 0ustar liggesusers\name{coxph.wtest} \alias{coxph.wtest} \title{Compute a quadratic form} \description{ This function is used internally by several survival routines. It computes a simple quadratic form, while properly dealing with missings. } \usage{ coxph.wtest(var, b, toler.chol = 1e-09) } \arguments{ \item{var}{variance matrix} \item{b}{vector} \item{toler.chol}{tolerance for the internal cholesky decomposition} } \details{ Compute b' V-inverse b. Equivalent to sum(b * solve(V,b)), except for the case of redundant covariates in the original model, which lead to NA values in V and b. } \value{a real number} \author{Terry Therneau} \keyword{ survival } survival/man/plot.cox.zph.Rd0000644000176200001440000000437614010070201015530 0ustar liggesusers\name{plot.cox.zph} \alias{plot.cox.zph} \title{ Graphical Test of Proportional Hazards } \description{ Displays a graph of the scaled Schoenfeld residuals, along with a smooth curve. } \usage{ \method{plot}{cox.zph}(x, resid=TRUE, se=TRUE, df=4, nsmo=40, var, xlab="Time", ylab, lty=1:2, col=1, lwd=1, hr=FALSE, ...) } \arguments{ \item{x}{ result of the \code{cox.zph} function. } \item{resid}{ a logical value, if \code{TRUE} the residuals are included on the plot, as well as the smooth fit. } \item{se}{ a logical value, if \code{TRUE}, confidence bands at two standard errors will be added. } \item{df}{ the degrees of freedom for the fitted natural spline, \code{df=2} leads to a linear fit. } \item{nsmo}{number of points to use for the lines} \item{var}{ the set of variables for which plots are desired. By default, plots are produced in turn for each variable of a model. Selection of a single variable allows other features to be added to the plot, e.g., a horizontal line at zero or a main title. This has been superseded by a subscripting method; see the example below. } \item{hr}{if TRUE, label the y-axis using the estimated hazard ratio rather than the estimated coefficient. (The plot does not change, only the axis label.)} \item{xlab}{label for the x-axis of the plot} \item{ylab}{optional label for the y-axis of the plot. If missing a default label is provided. This can be a vector of labels.} \item{lty, col, lwd}{line type, color, and line width for the overlaid curve. Each of these can be vector of length 2, in which case the second element is used for the confidence interval.} \item{\dots}{ additional graphical arguments passed to the \code{plot} function. } } \section{Side Effects}{ a plot is produced on the current graphics device. } \seealso{ \code{\link{coxph}}, \code{\link{cox.zph}}. } \examples{ vfit <- coxph(Surv(time,status) ~ trt + factor(celltype) + karno + age, data=veteran, x=TRUE) temp <- cox.zph(vfit) plot(temp, var=3) # Look at Karnofsy score, old way of doing plot plot(temp[3]) # New way with subscripting abline(0, 0, lty=3) # Add the linear fit as well abline(lm(temp$y[,3] ~ temp$x)$coefficients, lty=4, col=3) title(main="VA Lung Study") } \keyword{survival} survival/man/summary.aareg.Rd0000644000176200001440000000641313537676563015773 0ustar liggesusers\name{summary.aareg} \alias{summary.aareg} \title{ Summarize an aareg fit } \description{ Creates the overall test statistics for an Aalen additive regression model } \usage{ \method{summary}{aareg}(object, maxtime, test=c("aalen", "nrisk"), scale=1,...) } \arguments{ \item{object}{ the result of a call to the \code{aareg} function } \item{maxtime}{ truncate the input to the model at time "maxtime" } \item{test}{ the relative time weights that will be used to compute the test } \item{scale}{ scales the coefficients. For some data sets, the coefficients of the Aalen model will be very small (10-4); this simply multiplies the printed values by a constant, say 1e6, to make the printout easier to read. } \item{\dots}{for future methods} } \value{ a list is returned with the following components \item{ table }{ a matrix with rows for the intercept and each covariate, and columns giving a slope estimate, the test statistic, it's standard error, the z-score and a p-value } \item{ test }{ the time weighting used for computing the test statistics } \item{ test.statistic }{ the vector of test statistics } \item{ test.var }{ the model based variance matrix for the test statistic } \item{ test.var2 }{ optionally, a robust variance matrix for the test statistic } \item{ chisq }{ the overall test (ignoring the intercept term) for significance of any variable } \item{ n }{ a vector containing the number of observations, the number of unique death times used in the computation, and the total number of unique death times } } \details{ It is not uncommon for the very right-hand tail of the plot to have large outlying values, particularly for the standard error. The \code{maxtime} parameter can then be used to truncate the range so as to avoid these. This gives an updated value for the test statistics, without refitting the model. The slope is based on a weighted linear regression to the cumulative coefficient plot, and may be a useful measure of the overall size of the effect. For instance when two models include a common variable, "age" for instance, this may help to assess how much the fit changed due to the other variables, in leiu of overlaying the two plots. (Of course the plots are often highly non-linear, so it is only a rough substitute). The slope is not directly related to the test statistic, as the latter is invariant to any monotone transformation of time. } \seealso{ aareg, plot.aareg } \examples{ afit <- aareg(Surv(time, status) ~ age + sex + ph.ecog, data=lung, dfbeta=TRUE) summary(afit) \dontrun{ slope test se(test) robust se z p Intercept 5.05e-03 1.9 1.54 1.55 1.23 0.219000 age 4.01e-05 108.0 109.00 106.00 1.02 0.307000 sex -3.16e-03 -19.5 5.90 5.95 -3.28 0.001030 ph.ecog 3.01e-03 33.2 9.18 9.17 3.62 0.000299 Chisq=22.84 on 3 df, p=4.4e-05; test weights=aalen } summary(afit, maxtime=600) \dontrun{ slope test se(test) robust se z p Intercept 4.16e-03 2.13 1.48 1.47 1.450 0.146000 age 2.82e-05 85.80 106.00 100.00 0.857 0.392000 sex -2.54e-03 -20.60 5.61 5.63 -3.660 0.000256 ph.ecog 2.47e-03 31.60 8.91 8.67 3.640 0.000271 Chisq=27.08 on 3 df, p=5.7e-06; test weights=aalen }} \keyword{survival} survival/man/lung.Rd0000644000176200001440000000350114056226642014142 0ustar liggesusers\name{lung} \docType{data} \alias{cancer} \alias{lung} \title{NCCTG Lung Cancer Data} \description{ Survival in patients with advanced lung cancer from the North Central Cancer Treatment Group. Performance scores rate how well the patient can perform usual daily activities. } \usage{lung data(cancer, package="survival") } \format{ \tabular{ll}{ inst:\tab Institution code\cr time:\tab Survival time in days\cr status:\tab censoring status 1=censored, 2=dead\cr age:\tab Age in years\cr sex:\tab Male=1 Female=2\cr ph.ecog:\tab ECOG performance score as rated by the physician. 0=asymptomatic, 1= symptomatic but completely ambulatory, 2= in bed <50\% of the day, 3= in bed > 50\% of the day but not bedbound, 4 = bedbound\cr ph.karno:\tab Karnofsky performance score (bad=0-good=100) rated by physician\cr pat.karno:\tab Karnofsky performance score as rated by patient\cr meal.cal:\tab Calories consumed at meals\cr wt.loss:\tab Weight loss in last six months (pounds)\cr } } \note{ The use of 1/2 for alive/dead instead of the usual 0/1 is a historical footnote. For data contained on punch cards, IBM 360 Fortran treated blank as a zero, which led to a policy within the section of Biostatistics to never use "0" as a data value since one could not distinguish it from a missing value. The policy became a habit, as is often the case; and the 1/2 coding endured long beyond the demise of punch cards and Fortran. } \source{Terry Therneau} \references{ Loprinzi CL. Laurie JA. Wieand HS. Krook JE. Novotny PJ. Kugler JW. Bartel J. Law M. Bateman M. Klatt NE. et al. Prospective evaluation of prognostic variables from patient-completed questionnaires. North Central Cancer Treatment Group. Journal of Clinical Oncology. 12(3):601-7, 1994. } \keyword{datasets} survival/man/rats2.Rd0000644000176200001440000000143614013460564014231 0ustar liggesusers\name{rats2} \alias{rats2} \docType{data} \title{Rat data from Gail et al.} \description{48 rats were injected with a carcinogen, and then randomized to either drug or placebo. The number of tumors ranges from 0 to 13; all rats were censored at 6 months after randomization. } \usage{rats2 data(cancer, package="survival") } \format{ \tabular{ll}{ rat:\tab id\cr trt:\tab treatment,(1=drug, 0=control) \cr observation:\tab within rat\cr start:\tab entry time\cr stop:\tab exit time\cr status:\tab event status, 1=tumor, 0=censored\cr } } \source{ MH Gail, TJ Santner, and CC Brown (1980), An analysis of comparative carcinogenesis experiments based on multiple times to tumor. \emph{Biometrics} \bold{36}, 255--266. } \keyword{survival} \keyword{datasets} survival/man/coxsurv.fit.Rd0000644000176200001440000000544313537676563015514 0ustar liggesusers\name{coxsurv.fit} \alias{coxsurv.fit} \title{ A direct interface to the `computational engine' of survfit.coxph } \description{ This program is mainly supplied to allow other packages to invoke the survfit.coxph function at a `data' level rather than a `user' level. It does no checks on the input data that is provided, which can lead to unexpected errors if that data is wrong. } \usage{ coxsurv.fit(ctype, stype, se.fit, varmat, cluster, y, x, wt, risk, position, strata, oldid, y2, x2, risk2, strata2, id2, unlist=TRUE) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{stype}{survival curve computation: 1=direct, 2=exp(-cumulative hazard)} \item{ctype}{cumulative hazard computation: 1=Breslow, 2=Efron} \item{se.fit}{if TRUE, compute standard errors} \item{varmat}{the variance matrix of the coefficients } \item{cluster}{vector to control robust variance} \item{y}{the response variable used in the Cox model. (Missing values removed of course.) } \item{x}{covariate matrix used in the Cox model } \item{wt}{weight vector for the Cox model. If the model was unweighted use a vector of 1s. } \item{risk}{the risk score exp(X beta + offset) from the fitted Cox model.} \item{position}{optional argument controlling what is counted as 'censored'. Due to time dependent covariates, for instance, a subject might have start, stop times of (1,5)(5,30)(30,100). Times 5 and 30 are not 'real' censorings. Position is 1 for a real start, 2 for an actual end, 3 for both, 0 for neither.} \item{strata}{strata variable used in the Cox model. This will be a factor.} \item{oldid}{identifier for subjects with multiple rows in the original data.} \item{y2, x2, risk2, strata2}{variables for the hypothetical subjects, for which prediction is desired} \item{id2}{optional; if present and not NULL this should be a vector of identifiers of length \code{nrow(x2)}. A non-null value signifies that \code{x2} contains time dependent covariates, in which case this identifies which rows of \code{x2} go with each subject. } \item{unlist}{if \code{FALSE} the result will be a list with one element for each strata. Otherwise the strata are ``unpacked'' into the form found in a \code{survfit} object.} } \value{a list containing nearly all the components of a \code{survfit} object. All that is missing is to add the confidence intervals, the type of the original model's response (as in a coxph object), and the class. } \note{The source code for for both this function and \code{survfit.coxph} is written using noweb. For complete documentation see the \code{inst/sourcecode.pdf} file. } \author{Terry Therneau} \seealso{\code{\link{survfit.coxph}} } \keyword{survival} survival/man/nafld.Rd0000644000176200001440000000637514013461444014267 0ustar liggesusers\name{nafld} \alias{nafld1} \alias{nafld2} \alias{nafld3} \docType{data} \title{Non-alcohol fatty liver disease} \description{ Data sets containing the data from a population study of non-alcoholic fatty liver disease (NAFLD). Subjects with the condition and a set of matched control subjects were followed forward for metabolic conditions, cardiac endpoints, and death. } \usage{nafld1 nafld2 nafld3 data(nafld, package="survival") } \format{ \code{nafld1} is a data frame with 17549 observations on the following 10 variables. \describe{ \item{\code{id}}{subject identifier} \item{\code{age}}{age at entry to the study} \item{\code{male}}{0=female, 1=male} \item{\code{weight}}{weight in kg} \item{\code{height}}{height in cm} \item{\code{bmi}}{body mass index} \item{\code{case.id}}{the id of the NAFLD case to whom this subject is matched} \item{\code{futime}}{time to death or last follow-up} \item{\code{status}}{0= alive at last follow-up, 1=dead} } \code{nafld2} is a data frame with 400123 observations and 4 variables containing laboratory data \describe{ \item{\code{id}}{subject identifier} \item{\code{days}}{days since index date} \item{\code{test}}{the type of value recorded} \item{\code{value}}{the numeric value} } \code{nafld3} is a data frame with 34340 observations and 3 variables containing outcomes \describe{ \item{\code{id}}{subject identifier} \item{\code{days}}{days since index date} \item{\code{event}}{the endpoint that occurred} } } \details{ The primary reference for the NAFLD study is Allen (2018). The incidence of non-alcoholic fatty liver disease (NAFLD) has been rising rapidly in the last decade and it is now one of the main drivers of hepatology practice \cite{Tapper2018}. It is essentially the presence of excess fat in the liver, and parallels the ongoing obesity epidemic. Approximately 20-25\% of NAFLD patients will develop the inflammatory state of non-alcoholic steatohepatitis (NASH), leading to fibrosis and eventual end-stage liver disease. NAFLD can be accurately diagnosed by MRI methods, but NASH diagnosis currently requires a biopsy. The current study constructed a population cohort of all adult NAFLD subjects from 1997 to 2014 along with 4 potential controls for each case. To protect patient confidentiality all time intervals are in days since the index date; none of the dates from the original data were retained. Subject age is their integer age at the index date, and the subject identifier is an arbitrary integer. As a final protection, we include only a 90\% random sample of the data. As a consequence analyses results will not exactly match the original paper. There are 3 data sets: \code{nafld1} contains baseline data and has one observation per subject, \code{nafld2} has one observation for each (time dependent) continuous measurement, and \code{nafld3} has one observation for each yes/no outcome that occured. } \source{Data obtained from the author.} \references{ AM Allen, TM Therneau, JJ Larson, A Coward, VK Somers and PS Kamath, Nonalcoholic Fatty Liver Disease Incidence and Impact on Metabolic Burden and Death: A 20 Year Community Study, Hepatology 67:1726-1736, 2018. } \keyword{datasets} survival/man/print.summary.survfit.Rd0000644000176200001440000000144113537676563017545 0ustar liggesusers\name{print.summary.survfit} \alias{print.summary.survfit} \title{ Print Survfit Summary } \description{ Prints the result of \code{summary.survfit}. } \usage{ \method{print}{summary.survfit}(x, digits = max(options() $digits-4, 3), ...) } \arguments{ \item{x}{ an object of class \code{"summary.survfit"}, which is the result of the \code{summary.survfit} function. } \item{digits}{ the number of digits to use in printing the numbers. } \item{\dots}{for future methods} } \value{ \code{x}, with the invisible flag set to prevent printing. } \section{Side Effects}{ prints the summary created by \code{summary.survfit}. } \seealso{ \code{\link{options}}, \code{\link{print}}, \code{\link{summary.survfit}}. } \keyword{print} % docclass is function % Converted by Sd2Rd version 37351. survival/man/survfit0.Rd0000644000176200001440000000465013775422642014772 0ustar liggesusers\name{survfit0} \alias{survfit0} \title{ Convert the format of a survfit object. } \description{ Add the point for a starting time (time 0) to a survfit object's elements. This is useful for plotting. } \usage{ survfit0(x, start.time=0) } \arguments{ \item{x}{a survfit object} \item{start.time}{the desired starting time; see details below.} } \value{a reformulated version of the object with an initial data point at \code{start.time} added. The \code{time}, \code{surv}, \code{pstate}, \code{cumhaz}, \code{std.err}, \code{std.cumhaz} and other components will all be aligned, so as to make plots and summaries easier to produce. } \details{ Survival curves are traditionally plotted forward from time 0, but since the true starting time is not known as a part of the data, the \code{survfit} routine does not include a time 0 value in the resulting object. Someone might look at cumulative mortgage defaults versus calendar year, for instance, with the `time' value a Date object. The plotted curve probably should not start at 0 = 1970/01/01. Due to this uncertainty, it was decided not to include a "time 0" as part of a survfit object. If the original \code{survfit} call included a \code{start.time} argument, that value is of course retained. Whether that (1989) decision was wise or foolish, it is now far too late to change it. (We tried it once as a trial, resulting in over 20 errors in the survival test suite. We extrapolate that it might break 1/2 - 2/3 of the other CRAN packages that depend on survival, if made a default.) If the original \code{survfit} call included a \code{start.time} argument, that value is of course retained. One problem with this choice is that some functions must choose a starting point, plots for example. This utility function is used by \code{plot.survfit} and \code{summary.survfit} to do so, adding a new time point at the front of each curve in a consistent way: the optional argument to the \code{survfit0} function as the first choice (if supplied), then the user's \code{start.time} if present, otherwise \code{min(0, x$time)}. The resulting object is \emph{not} guarranteed to work with functions that further manipulate a \code{survfit} object such as subscripting, aggregation, pseudovalues, etc. (remember the 20 errors). Rather it is intended as a penultimate step, most often when creating a plot. } \keyword{survival} survival/man/coxph.detail.Rd0000644000176200001440000000605113733357707015572 0ustar liggesusers\name{coxph.detail} \alias{coxph.detail} \title{ Details of a Cox Model Fit } \description{ Returns the individual contributions to the first and second derivative matrix, at each unique event time. } \usage{ coxph.detail(object, riskmat=FALSE, rorder=c("data", "time")) } \arguments{ \item{object}{ a Cox model object, i.e., the result of \code{coxph}. } \item{riskmat}{ include the at-risk indicator matrix in the output? } \item{rorder}{if \code{riskmat=TRUE}, the rows of riskmat will be in the original data order, otherwise sorted by time within strata. } } \value{ a list with components \item{time}{ the vector of unique event times } \item{nevent}{ the number of events at each of these time points. } \item{means}{ a matrix with one row for each event time and one column for each variable in the Cox model, containing the weighted mean of the variable at that time, over all subjects still at risk at that time. The weights are the risk weights \code{exp(x \%*\% fit$coef)}. } \item{nrisk}{ number of subjects at risk. } \item{score}{ the contribution to the score vector (first derivative of the log partial likelihood) at each time point. } \item{imat}{ the contribution to the information matrix (second derivative of the log partial likelihood) at each time point. } \item{hazard}{ the hazard increment. Note that the hazard and variance of the hazard are always for some particular future subject. This routine uses \code{object$mean} as the future subject. } \item{varhaz}{ the variance of the hazard increment. } \item{x,y}{ copies of the input data. } \item{strata}{ only present for a stratified Cox model, this is a table giving the number of time points of component \code{time} that were contributed by each of the strata. } \item{riskmat}{ a matrix with one row for each observation and one colum for each unique event time, containing a 0/1 value to indicate whether that observation was (1) or was not (0) at risk at the given time point. Rows are in the order of the original data (after removal of any missings by \code{coxph}), or in time order. } } \details{ This function may be useful for those who wish to investigate new methods or extensions to the Cox model. The example below shows one way to calculate the Schoenfeld residuals. } \seealso{ \code{\link{coxph}}, \code{\link{residuals.coxph}} } \examples{ fit <- coxph(Surv(futime,fustat) ~ age + rx + ecog.ps, ovarian, x=TRUE) fitd <- coxph.detail(fit) # There is one Schoenfeld residual for each unique death. It is a # vector (covariates for the subject who died) - (weighted mean covariate # vector at that time). The weighted mean is defined over the subjects # still at risk, with exp(X beta) as the weight. events <- fit$y[,2]==1 etime <- fit$y[events,1] #the event times --- may have duplicates indx <- match(etime, fitd$time) schoen <- fit$x[events,] - fitd$means[indx,] } \keyword{survival} survival/man/rats.Rd0000644000176200001440000000226114013460532014137 0ustar liggesusers\name{rats} \alias{rats} \docType{data} \title{Rat treatment data from Mantel et al} \description{Rat treatment data from Mantel et al. Three rats were chosen from each of 100 litters, one of which was treated with a drug, and then all followed for tumor incidence. } \usage{rats data(cancer, package="survival") } \format{ \tabular{ll}{ litter:\tab litter number from 1 to 100\cr rx:\tab treatment,(1=drug, 0=control) \cr time:\tab time to tumor or last follow-up\cr status:\tab event status, 1=tumor and 0=censored\cr sex:\tab male or female } } \source{ N. Mantel, N. R. Bohidar and J. L. Ciminera. Mantel-Haenszel analyses of litter-matched time to response data, with modifications for recovery of interlitter information. Cancer Research, 37:3863-3868, 1977. } \references{ E. W. Lee, L. J. Wei, and D. Amato, Cox-type regression analysis for large number of small groups of correlated failure time observations, in "Survival Analysis, State of the Art", Kluwer, 1992. } \note{Since only 2/150 of the male rats have a tumor, most analyses use only females (odd numbered litters), e.g. Lee et al.} \keyword{survival} \keyword{datasets} survival/man/pbcseq.Rd0000644000176200001440000000671314013461712014452 0ustar liggesusers\name{pbcseq} \alias{pbcseq} \docType{data} \title{Mayo Clinic Primary Biliary Cirrhosis, sequential data} \description{ This data is a continuation of the PBC data set, and contains the follow-up laboratory data for each study patient. An analysis based on the data can be found in Murtagh, et. al. The primary PBC data set contains only baseline measurements of the laboratory parameters. This data set contains multiple laboratory results, but only on the 312 randomized patients. Some baseline data values in this file differ from the original PBC file, for instance, the data errors in prothrombin time and age which were discovered after the original analysis (see Fleming and Harrington, figure 4.6.7). One "feature" of the data deserves special comment. The last observation before death or liver transplant often has many more missing covariates than other data rows. The original clinical protocol for these patients specified visits at 6 months, 1 year, and annually thereafter. At these protocol visits lab values were obtained for a large pre-specified battery of tests. "Extra" visits, often undertaken because of worsening medical condition, did not necessarily have all this lab work. The missing values are thus potentially informative. } \usage{pbcseq data(pbc, package="survival") } \format{ \tabular{ll}{ id:\tab case number\cr age:\tab in years\cr sex:\tab m/f\cr trt:\tab 1/2/NA for D-penicillmain, placebo, not randomised\cr time:\tab number of days between registration and the earlier of death,\cr \tab transplantion, or study analysis in July, 1986\cr status:\tab status at endpoint, 0/1/2 for censored, transplant, dead\cr day:\tab number of days between enrollment and this visit date\cr \tab all measurements below refer to this date\cr albumin:\tab serum albumin (mg/dl)\cr alk.phos:\tab alkaline phosphotase (U/liter)\cr ascites:\tab presence of ascites \cr ast:\tab aspartate aminotransferase, once called SGOT (U/ml)\cr bili:\tab serum bilirunbin (mg/dl)\cr chol:\tab serum cholesterol (mg/dl)\cr copper:\tab urine copper (ug/day)\cr edema:\tab 0 no edema, 0.5 untreated or successfully treated\cr \tab 1 edema despite diuretic therapy\cr hepato:\tab presence of hepatomegaly or enlarged liver\cr platelet:\tab platelet count\cr protime:\tab standardised blood clotting time\cr spiders:\tab blood vessel malformations in the skin\cr stage:\tab histologic stage of disease (needs biopsy)\cr trig:\tab triglycerides (mg/dl)\cr } } \source{ T Therneau and P Grambsch, "Modeling Survival Data: Extending the Cox Model", Springer-Verlag, New York, 2000. ISBN: 0-387-98784-3. } \seealso{\code{\link{pbc}}} \examples{ # Create the start-stop-event triplet needed for coxph first <- with(pbcseq, c(TRUE, diff(id) !=0)) #first id for each subject last <- c(first[-1], TRUE) #last id time1 <- with(pbcseq, ifelse(first, 0, day)) time2 <- with(pbcseq, ifelse(last, futime, c(day[-1], 0))) event <- with(pbcseq, ifelse(last, status, 0)) fit1 <- coxph(Surv(time1, time2, event) ~ age + sex + log(bili), pbcseq) } \references{ Murtaugh PA. Dickson ER. Van Dam GM. Malinchoc M. Grambsch PM. Langworthy AL. Gips CH. "Primary biliary cirrhosis: prediction of short-term survival based on repeated patient visits." Hepatology. 20(1.1):126-34, 1994. Fleming T and Harrington D., "Counting Processes and Survival Analysis", Wiley, New York, 1991. } \keyword{datasets} survival/DESCRIPTION0000644000176200001440000000234014111165372013633 0ustar liggesusersTitle: Survival Analysis Priority: recommended Package: survival Version: 3.2-13 Date: 2021-08-23 Depends: R (>= 3.5.0) Imports: graphics, Matrix, methods, splines, stats, utils LazyData: Yes LazyDataCompression: xz ByteCompile: Yes Authors@R: c(person(c("Terry", "M"), "Therneau", email="therneau.terry@mayo.edu", role=c("aut", "cre")), person("Thomas", "Lumley", role=c("ctb", "trl"), comment="original S->R port and R maintainer until 2009"), person("Atkinson", "Elizabeth", role="ctb"), person("Crowson", "Cynthia", role="ctb")) Description: Contains the core survival analysis routines, including definition of Surv objects, Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models, and parametric accelerated failure time models. License: LGPL (>= 2) URL: https://github.com/therneau/survival NeedsCompilation: yes Packaged: 2021-08-23 14:47:31 UTC; therneau Author: Terry M Therneau [aut, cre], Thomas Lumley [ctb, trl] (original S->R port and R maintainer until 2009), Atkinson Elizabeth [ctb], Crowson Cynthia [ctb] Maintainer: Terry M Therneau Repository: CRAN Date/Publication: 2021-08-24 12:50:02 UTC survival/build/0000755000176200001440000000000014110732403013217 5ustar liggesuserssurvival/build/vignette.rds0000644000176200001440000000107114110732403015555 0ustar liggesusers‹½TËn1&i!¤²!À+V¨uƒ*ª6 ¶fì¤nglËv’vÇ?³'Ø3¾ö¶]Ìx||ŸÇ÷ÌQQ½bÐóï¾ÿìŸù׉^úç´C¿N(»+7fËg×r±)ÕÚ¨QS‡áq©jÍ;дT²T†QYv,™°¥5‚†õ¦rÊÝrƒ€ZéMEP‡²º’[M¬¯Wli…°S'8s¢îŽÀ¸Æ¾ÞM0è«KÅësv·±Ž3ry`ÆÆó7ç‰!ׄ’¹z —Šñ*|¼ =~¶ÎÇ'u8°„JFZÞ‚öÂç™»½_´¼ ÙÐÐ8Ï}Á?M³Voqà‚”¸Ôé×@.ÙŠµäÎ%øÕU¢Ø%¡ÖÁ¹i‰þDt¥œ_Br!7´ )ÆòeÕ´¼§k¨ÿݵÚH¦V+ÂQ¦ .†47>|³Š¥‡È‚k.—Îs¹¥FÐ\ëè{{S¡ïÿ¬f«l†ÓÈ"K<²ÈF6CqdÐŽlðÈ¢P0²Ê#›14²ÈFù¦‘ X—ŠQV/Díh OÊ«ŽnÁ*©6ÅÁ:Á §¤O()«3÷”´ ^I™à•uéwgM‹m«Å¬Oœ/ü‘‹£Øù±¤AíáI¢Q/…K›þÕâ"~wÏZpžá;·ö ÑÐ¨Ý ’…n{¿ük¿ßÿ>¬¨¬¨…ŠùË¡³•ñþ~÷ç/Ûzg>\survival/tests/0000755000176200001440000000000014110732403013262 5ustar liggesuserssurvival/tests/neardate.R0000644000176200001440000000317413753312702015205 0ustar liggesuserslibrary(survival) # the second data set is not sorted by id/date, on purpose df1 <- data.frame(id= 1:10, y1= as.Date(c("1992-01-01", "1996-01-01", "1997-03-20", "2000-01-01", "2001-01-01", "2004-01-01", "2014-03-27", "2014-01-30", "2000-08-01", "1997-04-29"))) df2 <- data.frame(id= c(1, 1, 2, 3, 4, 4, 5, 6, 7, 7, 8, 9, 9, 9, 10, 3, 3, 6, 6, 8), y2= as.Date(c("1998-04-30", "2004-07-01", "1999-04-14", "2001-02-22", "2003-11-19", "2005-02-15", "2006-06-22", "2007-09-20", "2013-08-02", "2015-01-09", "2014-01-15", "2006-12-06", "1999-10-20", "2010-06-30", "1997-04-28", "1995-04-20", "1997-03-20", "1998-04-30", "1995-04-20", "2006-12-06"))) if (FALSE) { # plot for visual check plot(y2 ~ id, df2, ylim=range(c(df1$y1, df2$y2)), type='n') text(df2$id, df2$y2, as.numeric(1:nrow(df2))) points(y1~id, df1, col=2, pch='+') } i1 <- neardate(df1$id, df2$id, df1$y1, df2$y2) all.equal(i1, c(1, 3, 17, 5, 7, 8, 10, NA, 12, NA)) i2 <- neardate(df1$id, df2$id, df1$y1, df2$y2, best="prior") all.equal(i2, c(NA, NA, 17, NA, NA, 18, 9, 11, 13, 15)) indx <- order(df2$id, df2$y2) df3 <- df2[indx,] i3 <- neardate(df1$id, df3$id, df1$y1, df3$y2) all.equal(indx[i3], i1) i4 <- neardate(df1$id, df3$id, df1$y1, df3$y2, best="prior") all.equal(indx[i4], i2) indx <- c(2,3,10,9, 4,5, 7,8,1,6) df4 <- df1[indx,] i5 <- neardate(df4$id, df2$id, df4$y1, df2$y2) all.equal(i1[indx], i5) survival/tests/book2.Rout.save0000644000176200001440000001664314012570615016126 0ustar liggesusers R Under development (unstable) (2021-01-28 r79896) -- "Unsuffered Consequences" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(survival) > options(na.action=na.exclude) # preserve missings > options(contrasts=c('contr.treatment', 'contr.poly')) #ensure constrast type > > # > # Tests from the appendix of Therneau and Grambsch > # b. Data set 1 and Efron estimate > # > test1 <- data.frame(time= c(9, 3,1,1,6,6,8), + status=c(1,NA,1,0,1,1,0), + x= c(0, 2,1,1,1,0,0)) > > byhand <- function(beta, newx=0) { + r <- exp(beta) + loglik <- 2*beta - (log(3*r +3) + log((r+5)/2) + log(r+3)) + u <- (30 + 23*r - r^3)/ ((r+1)*(r+3)*(r+5)) + tfun <- function(x) x - x^2 + imat <- tfun(r/(r+1)) + tfun(r/(r+5)) + tfun(r/(r+3)) + + # The matrix of weights, one row per obs, one col per time + # Time of 1, 6, 6+0 (second death), and 9 + wtmat <- matrix(c(1,1,1,1,1,1, + 0,0,1,1,1,1, + 0,0,.5, .5, 1,1, + 0,0,0,0,0,1), ncol=4) + wtmat <- diag(c(r,r,r,1,1,1)) %*% wtmat + + x <- c(1,1,1,0,0,0) + status <- c(1,0,1,1,0,1) + xbar <- colSums(wtmat*x)/ colSums(wtmat) + haz <- 1/ colSums(wtmat) # one death at each of the times + + hazmat <- wtmat %*% diag(haz) #each subject's hazard over time + mart <- status - rowSums(hazmat) + + a <- r+1; b<- r+3; d<- r+5 # 'c' in the book, 'd' here + score <- c((2*r + 3)/ (3*a^2), + -r/ (3*a^2), + (675+ r*(1305 +r*(756 + r*(-4 +r*(-79 -13*r)))))/(3*(a*b*d)^2), + r*(1/(3*a^2) - a/(2*b^2) - b/(2*d^2)), + 2*r*(177 + r*(282 +r*(182 + r*(50 + 5*r)))) /(3*(a*b*d)^2), + 2*r*(177 + r*(282 +r*(182 + r*(50 + 5*r)))) /(3*(a*b*d)^2)) + + # Schoenfeld residual + d <- mean(xbar[2:3]) + scho <- c(1/(r+1), 1- d, 0- d , 0) + + surv <- exp(-cumsum(haz)* exp(beta*newx))[c(1,3,4)] + varhaz.g <- cumsum(haz^2) # since all numerators are 1 + + varhaz.d <- cumsum((newx-xbar) * haz) + + varhaz <- (varhaz.g + varhaz.d^2/ imat) * exp(2*beta*newx) + + list(loglik=loglik, u=u, imat=imat, xbar=xbar, haz=haz, + mart=mart, score=score, var.g=varhaz.g, var.d=varhaz.d, + scho=scho, surv=surv, var=varhaz[c(1,3,4)]) + } > > > aeq <- function(x,y) all.equal(as.vector(x), as.vector(y)) > > fit0 <-coxph(Surv(time, status) ~x, test1, iter=0) > truth0 <- byhand(0,0) > aeq(truth0$loglik, fit0$loglik[1]) [1] TRUE > aeq(1/truth0$imat, fit0$var) [1] TRUE > aeq(truth0$mart, fit0$resid[c(2:6,1)]) [1] TRUE > aeq(resid(fit0), c(-3/4, NA, 5/6, -1/6, 5/12, 5/12, -3/4)) [1] TRUE > aeq(truth0$scho, resid(fit0, 'schoen')) [1] TRUE > aeq(truth0$score, resid(fit0, 'score')[c(3:7,1)]) [1] TRUE > sfit <- survfit(fit0, list(x=0), censor=FALSE) > aeq(sfit$std.err^2, truth0$var) [1] TRUE > aeq(sfit$surv, truth0$surv) [1] TRUE > > fit <- coxph(Surv(time, status) ~x, test1, eps=1e-8, nocenter=NULL) > aeq(round(fit$coef,6), 1.676857) [1] TRUE > truebeta <- log(cos(acos((45/23)*sqrt(3/23))/3) * 2* sqrt(23/3)) > truth <- byhand(truebeta, 0) > aeq(truth$loglik, fit$loglik[2]) [1] TRUE > aeq(1/truth$imat, fit$var) [1] TRUE > aeq(truth$mart, fit$resid[c(2:6,1)]) [1] TRUE > aeq(truth$scho, resid(fit, 'schoen')) [1] TRUE > aeq(truth$score, resid(fit, 'score')[c(3:7,1)]) [1] TRUE > > # Per comments in the source code, the below is expected to fail for Efron > # at the tied death times. (When predicting for new data, predict > # treats a time in the new data set that exactly matches one in the original > # as being just after the original, i.e., experiences the full hazard > # jump there, in the same way that censors do.) > expect <- predict(fit, type='expected', newdata=test1) #force recalc > use <- !(test1$time==6 | is.na(test1$status)) > aeq(test1$status[use] - resid(fit)[use], expect[use]) [1] TRUE > > sfit <- survfit(fit, list(x=0), censor=FALSE) > aeq(sfit$surv, truth$surv) [1] TRUE > aeq(sfit$std.err^2, truth$var) [1] TRUE > > # > # Done with the formal test, now print out lots of bits > # > resid(fit) 1 2 3 4 5 6 7 -0.3655434 NA 0.7191707 -0.2808293 -0.4383414 0.7310869 -0.3655434 > resid(fit, 'scor') 1 2 3 4 5 6 7 0.2208584 NA 0.1132780 -0.0442340 -0.1029199 -0.4078409 0.2208584 > resid(fit, 'scho') 1 6 6 9 0.157512 0.421244 -0.578756 0.000000 > > predict(fit, type='lp') [1] -0.8384287 NA 0.8384287 0.8384287 0.8384287 -0.8384287 -0.8384287 > predict(fit, type='risk') [1] 0.4323894 NA 2.3127302 2.3127302 2.3127302 0.4323894 0.4323894 > predict(fit, type='expected') 1 2 3 4 5 6 7 1.3655434 NA 0.2808293 0.2808293 1.4383414 0.2689131 0.3655434 > predict(fit, type='terms') x 1 -0.8384287 2 NA 3 0.8384287 4 0.8384287 5 0.8384287 6 -0.8384287 7 -0.8384287 > predict(fit, type='lp', se.fit=T) $fit 1 2 3 4 5 6 7 -0.8384287 NA 0.8384287 0.8384287 0.8384287 -0.8384287 -0.8384287 $se.fit 1 2 3 4 5 6 7 0.6388078 NA 0.6388078 0.6388078 0.6388078 0.6388078 0.6388078 > predict(fit, type='risk', se.fit=T) $fit 1 2 3 4 5 6 7 0.4323894 NA 2.3127302 2.3127302 2.3127302 0.4323894 0.4323894 $se.fit 1 2 3 4 5 6 7 0.4200565 NA 0.9714774 0.9714774 0.9714774 0.4200565 0.4200565 > predict(fit, type='expected', se.fit=T) $fit 1 2 3 4 5 6 7 1.3655434 NA 0.2808293 0.2808293 1.4383414 0.2689131 0.3655434 $se.fit [1] 1.0649293 NA 0.2864593 0.2864593 1.5922983 0.3661617 0.3661617 > predict(fit, type='terms', se.fit=T) $fit x 1 -0.8384287 2 NA 3 0.8384287 4 0.8384287 5 0.8384287 6 -0.8384287 7 -0.8384287 $se.fit x 1 0.6388078 2 NA 3 0.6388078 4 0.6388078 5 0.6388078 6 0.6388078 7 0.6388078 > > summary(survfit(fit)) Call: survfit(formula = fit) time n.risk n.event survival std.err lower 95% CI upper 95% CI 1 6 1 0.8857 0.117 0.683036 1 6 4 2 0.4294 0.237 0.145743 1 9 1 1 0.0425 0.116 0.000198 1 > summary(survfit(fit, list(x=2))) Call: survfit(formula = fit, newdata = list(x = 2)) time n.risk n.event survival std.err lower 95% CI upper 95% CI 1 6 1 2.23e-01 5.97e-01 1.16e-03 1 6 4 2 2.87e-05 5.69e-04 3.96e-22 1 9 1 1 1.08e-17 1.04e-15 1.07e-99 1 > > proc.time() user system elapsed 1.070 0.096 1.169 survival/tests/zph.R0000644000176200001440000001540413632523317014224 0ustar liggesuserslibrary(survival) aeq <- function(x,y) all.equal(as.vector(x), as.vector(y)) test1 <- data.frame(time= c(9, 3,1,1,6,6,8), status=c(1,NA,1,0,1,1,0), x= c(0, 2,1,1,1,0,0)) # Verify that cox.zph computes a score test # First for the Breslow estimate r <- (3 + sqrt(33))/2 # actual MLE for log(beta) U <- c(1/(r+1), 3/(r+3), -r/(r+3), 0) # score statistic imat <- c(r/(r+1)^2, 3*r/(r+3)^2, 3*r/(r+3)^2, 0) # information matrix g = c(1, 6, 6, 9) # death times u2 <- c(sum(U), sum(g*U)) # first derivative i2 <- matrix(c(sum(imat), sum(g*imat), sum(g*imat), sum(g^2*imat)), 2,2) # second derivative sctest <- solve(i2, u2) %*% u2 # Verify that centering makes no difference for the test (though i2 changes) g2 <- g - mean(g) u2b <- c(sum(U), sum(g2*U)) i2b <- matrix(c(sum(imat), sum(g2*imat), sum(g2*imat), sum(g2^2*imat)), 2,2) sctest2 <- solve(i2b, u2b) %*% u2b all.equal(sctest, sctest2) # Now check the program fit1 <- coxph(Surv(time, status) ~ x, test1, ties='breslow') aeq(fit1$coef, log(r)) zp1 <- cox.zph(fit1, transform='identity', global=FALSE) aeq(zp1$table[,1], sctest) aeq(zp1$y, resid(fit1, type="scaledsch")) dummy <- rep(0, nrow(test1)) fit1b <- coxph(Surv(dummy, time, status) ~ x, test1, ties='breslow') aeq(fit1b$coef, log(r)) zp1b <- cox.zph(fit1b, transform='identity', global=FALSE) aeq(zp1b$table[,1], sctest) # the pair of tied times gets reversed in the zph result # but since the 'y' values are only used to plot it doesn't matter aeq(zp1b$y[c(1,3,2,4)], resid(fit1b, type="scaledsch")) # log time check g3 <- log(g) - mean(log(g)) u3 <- c(sum(U), sum(g3*U)) # first derivative i3 <- matrix(c(sum(imat), sum(g3*imat), sum(g3*imat), sum(g3^2*imat)), 2,2) # second derivative sctest3 <- solve(i3, u3) %*% u3 zp3 <- cox.zph(fit1, transform='log', global=FALSE) aeq(zp3$table[,1], sctest3) # Efron approximation phi <- acos((45/23)*sqrt(3/23)) r <- 2*sqrt(23/3)* cos(phi/3) # actual MLE for log(beta) U <- c(1/(r+1), 3/(r+3), -r/(r+5), 0) # score statistic imat <- c(r/(r+1)^2, 3*r/(r+3)^2, 5*r/(r+5)^2, 0) # information matrix u4 <- c(sum(U), sum(g3*U)) # first derivative i4 <- matrix(c(sum(imat), sum(g3*imat), sum(g3*imat), sum(g3^2*imat)), 2,2) # second derivative sctest4 <- solve(i4, u4) %*% u4 fit4 <- coxph(Surv(time, status) ~ x, test1, ties='efron') aeq(fit4$coef, log(r)) zp4 <- cox.zph(fit4, transform='log', global=FALSE) aeq(zp4$table[,1], sctest4) aeq(zp4$y, resid(fit4, type="scaledsch")) fit5 <- coxph(Surv(dummy, time, status) ~ x, test1, ties="efron") aeq(fit5$coef, log(r)) zp5 <- cox.zph(fit5, transform="log", global=FALSE) aeq(zp5$table[,1], sctest4) # Artificial stratification test2 <- rbind(test1, test1) test2$group <- rep(letters[1:2], each=nrow(test1)) # U, imat, and sctest will all double dummy <- c(dummy, dummy) fit6 <- coxph(Surv(dummy, time, status) ~ x + strata(group), test2) aeq(fit6$coef, log(r)) zp6 <- cox.zph(fit6, transform="log", globa=FALSE) aeq(zp6$table[,1], 2*sctest4) # A multi-state check, 2 covariates # Verify that the multi-state result = the single state Cox models etime <- with(mgus2, ifelse(pstat==0, futime, ptime)) event <- with(mgus2, ifelse(pstat==0, 2*death, 1)) event <- factor(event, 0:2, labels=c("censor", "pcm", "death")) table(event) ct1 <- coxph(Surv(etime, event) ~ sex + age, mgus2, id=id) ct2 <- coxph(Surv(etime, event=='pcm') ~ sex + age, mgus2) ct3 <- coxph(Surv(etime, event=='death') ~ sex + age, mgus2) zp1 <- cox.zph(ct1, transform='identity') zp2 <- cox.zph(ct2, transform='identity') zp3 <- cox.zph(ct3, transform='identity') aeq(zp1$table[1:2,], zp2$table[1:2,]) aeq(zp1$table[3:4,], zp3$table[1:2,]) # Now add a starting time of zero dummy <- rep(0, nrow(mgus2)) ct4 <- coxph(Surv(dummy, etime, event) ~ sex + age, mgus2, id=id) ct5 <- coxph(Surv(dummy, etime, event=='pcm') ~ sex + age, mgus2) ct6 <- coxph(Surv(dummy, etime, event=='death') ~ sex + age, mgus2) zp4 <- cox.zph(ct4, transform='identity') zp5 <- cox.zph(ct5, transform='identity') zp6 <- cox.zph(ct6, transform='identity') aeq(zp4$table[1:2,], zp5$table[1:2,]) aeq(zp4$table[3:4,], zp6$table[1:2,]) # Direct check of a multivariate model with start, stop data p1 <- pbcseq[!duplicated(pbcseq$id), 1:6] pdata <- tmerge(p1[, c("id", "trt", "age", "sex")], p1, id=id, death = event(futime, status==2)) pdata <- tmerge(pdata, pbcseq, id=id, bili=tdc(day, bili), edema = tdc(day, edema), albumin=tdc(day, albumin), protime = tdc(day, protime)) pfit <- coxph(Surv(tstart, tstop, death) ~ log(bili) + albumin + edema + age + log(protime), data = pdata, ties='efron') zp7 <- cox.zph(pfit, transform='log', global=FALSE) direct <- function(fit) { nvar <- length(fit$coef) dt <- coxph.detail(fit) gtime <- log(dt$time) - mean(log(dt$time)) # key idea: at any event time I have a first deriviative vector # c(dt$score[i,], gtime[i]* dt$score[i,]) # and second derivative matrix # dt$imat[,,i] gtime[i] * dt$imat[,,i] # gtime[i]*dt$imat[,,i] gtime[i]^2 * dt$imat[,,i] # for the expanded model, where imat[,,i] is symmetric, # and colSums(dt$score) =0 (since the model converged) # # Create score tests for adding one time-dependent variable # gtime * x[,j] at a time: first derivative of this test is # c(dt$score[i,], gtime[i]* dt$score[i,j]) # and etc. unew <- colSums(gtime * dt$score) temp1 <- apply(dt$imat, 1:2, sum) temp2 <- apply(dt$imat, 1:2, function(x) sum(x*gtime)) temp3 <- apply(dt$imat, 1:2, function(x) sum(x * gtime^2)) score <- double(nvar) smat <- matrix(0., nvar+1, nvar+1) # second deriv matrix for the test smat[1:nvar, 1:nvar] <- temp1 for (i in 1:nvar) { smat[nvar+1,] <- c(temp2[i,], temp3[i,i]) smat[,nvar+1] <- c(temp2[,i], temp3[i,i]) utemp <- c(rep(0,nvar), unew[i]) score[i] <- solve(smat, utemp) %*% utemp } list(sctest = score, u= c(colSums(dt$score), unew), imat=cbind(rbind(temp1, temp2), rbind(temp2, temp3))) } aeq(zp7$table[,1], direct(pfit)$sctest) # Last, make sure that NA coefficients are ignored d1 <- survSplit(Surv(time, status) ~ ., veteran, cut=150, episode="epoch") fit <- coxph(Surv(tstart, time, status) ~ celltype:strata(epoch) + age, d1) zz <- cox.zph(fit) fit2 <- coxph(Surv(tstart, time, status) ~ celltype:strata(epoch) + age, d1, x=TRUE) zz2 <- cox.zph(fit2) x2 <- fit2$x[, !is.na(fit$coefficients)][,-1] fit3 <- coxph(Surv(tstart, time, status) ~ age + x2, d1) all.equal(fit3$loglik, fit2$loglik) zz3 <- cox.zph(fit3) all.equal(unclass(zz)[1:7], unclass(zz2)[1:7]) #ignore the call component all.equal(as.vector(zz$table), as.vector(zz3$table)) # variable names change survival/tests/coxsurv3.Rout.save0000644000176200001440000001147013763711634016711 0ustar liggesusers R Under development (unstable) (2020-09-23 r79248) -- "Unsuffered Consequences" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(survival) > aeq <- function(x,y) all.equal(as.vector(x), as.vector(y)) > # One more test on coxph survival curves, to test out the individual > # option. First fit a model with a time dependent covariate > # > test2 <- data.frame(start=c(1, 2, 5, 2, 1, 7, 3, 4, 8, 8), + stop =c(2, 3, 6, 7, 8, 9, 9, 9,14,17), + event=c(1, 1, 1, 1, 1, 1, 1, 0, 0, 0), + x =c(1, 0, 0, 1, 0, 1, 1, 1, 0, 0) ) > > # True hazard function, from the validation document > lambda <- function(beta, x=0, method='efron') { + r <- exp(beta) + lambda <- c(1/(r+1), 1/(r+2), 1/(3*r +2), 1/(3*r+1), + 1/(3*r+1), 1/(3*r+2) + 1/(2*r +2)) + if (method == 'breslow') lambda[9] <- 2/(3*r +2) + list(time=c(2,3,6,7,8,9), lambda=lambda) + } > > fit <- coxph(Surv(start, stop, event) ~x, test2) > # A curve for someone who never changes > surv1 <-survfit(fit, newdata=list(x=0), censor=FALSE) > > true <- lambda(fit$coef, 0) > > aeq(true$time, surv1$time) [1] TRUE > aeq(-log(surv1$surv), cumsum(true$lambda)) [1] TRUE > > # Reprise it with a time dependent subject who doesn't change > data2 <- data.frame(start=c(0, 4, 9, 11), stop=c(4, 9, 11, 17), + event=c(0,0,0,0), x=c(0,0,0,0), patn=c(1,1,1,1)) > surv2 <- survfit(fit, newdata=data2, id=patn, censor=FALSE) > aeq(surv2$surv, surv1$surv) [1] TRUE > > > # > # Now a more complex data set with multiple strata > # > test3 <- data.frame(start=c(1, 2, 5, 2, 1, 7, 3, 4, 8, 8, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), + stop =c(2, 3, 6, 7, 8, 9, 9, 9,14,17, + 1:11), + event=c(1, 1, 1, 1, 1, 1, 1, 0, 0, 0, + 0, 1, 1, 0, 0, 1, 1, 0, 1, 0,1), + x =c(1, 0, 0, 1, 0, 1, 1, 1, 0, 0, + 1, 2, 3, 2, 1, 1, 1, 0, 2, 1,0), + grp = c(rep('a', 10), rep('b', 11))) > > fit2 <- coxph(Surv(start, stop, event) ~ x + strata(grp), test3) > > # The above tests show the program works for a simple case, use it to > # get a true baseline for strata 2 > fit2b <- coxph(Surv(start, stop, event) ~x, test3, + subset=(grp=='b'), init=fit2$coef, iter=0) > temp <- survfit(fit2b, newdata=list(x=0), censor=F) > true2 <- list(time=temp$time, lambda=diff(c(0, -log(temp$surv)))) > true1 <- lambda(fit2$coef, x=0) > > # Separate strata, one value > surv3 <- survfit(fit2, list(x=0), censor=FALSE) > aeq(true1$time, (surv3[1])$time) [1] TRUE > aeq(-log(surv3[1]$surv), cumsum(true1$lambda)) [1] TRUE > > data4 <- data.frame(start=c(0, 4, 9, 11), stop=c(4, 9, 11, 17), + event=c(0,0,0,0), x=c(0,0,0,0), grp=rep('a', 4), + patid= rep("Jones", 4)) > surv4a <- survfit(fit2, newdata=data4, id=patid, censor=FALSE) > aeq(-log(surv4a$surv), cumsum(true1$lambda)) [1] TRUE > > data4$grp <- rep('b',4) > surv4b <- survfit(fit2, newdata=data4, id=patid, censor=FALSE) > aeq(-log(surv4b$surv), cumsum(true2$lambda)) [1] TRUE > > > # Now for something more complex > # Subject 1 skips day 4. Since there were no events that day the survival > # will be the same, but the times will be different. > # Subject 2 spends some time in strata 1, some in strata 2, with > # moving covariates > # > data5 <- data.frame(start=c(0,5,9,11, + 0, 4, 3), + stop =c(4,9,11,17, 4,8,7), + event=rep(0,7), + x=c(1,1,1,1, 0,1,2), + grp=c('a', 'a', 'a', 'a', 'a', 'a', 'b'), + subject=c(1,1,1,1, 2,2,2)) > surv5 <- survfit(fit2, newdata=data5, censor=FALSE, id=subject) > > aeq(surv5[1]$time, c(2,3,5,6,7,8)) #surv1 has 2, 3, 6, 7, 8, 9 [1] TRUE > aeq(surv5[1]$surv, surv3[1]$surv ^ exp(fit2$coef)) [1] TRUE > > tlam <- c(true1$lambda[1:2]* exp(fit2$coef * data5$x[5]), + true1$lambda[3:5]* exp(fit2$coef * data5$x[6]), + true2$lambda[3:4]* exp(fit2$coef * data5$x[7])) > aeq(-log(surv5[2]$surv), cumsum(tlam)) [1] TRUE > > > > > proc.time() user system elapsed 0.873 0.052 0.922 survival/tests/infcox.R0000644000176200001440000000232113537676563014723 0ustar liggesusersoptions(na.action=na.exclude) # preserve missings options(contrasts=c('contr.treatment', 'contr.poly')) #ensure constrast type library(survival) # # A test to exercise the "infinity" check on 2 variables # test3 <- data.frame(futime=1:12, fustat=c(1,0,1,0,1,0,0,0,0,0,0,0), x1=rep(0:1,6), x2=c(rep(0,6), rep(1,6))) # This will produce a warning message, which is the point of the test. # The variance is close to singular and gives different answers # on different machines fit3 <- coxph(Surv(futime, fustat) ~ x1 + x2, test3, iter=25) all(fit3$coef < -22) all.equal(round(fit3$log, 4),c(-6.8669, -1.7918)) # # Actual solution # time 1, 12 at risk, 3 each of x1/x2 = 00, 01, 10, 11 # time 2, 10 at risk, 2, 3, 2 , 3 # time 5, 8 at risk, 1, 3, 1, 3 # Let r1 = exp(beta1), r2= exp(beta2) # loglik = -log(3 + 3r1 + 3r2 + 3 r1*r2) - log(2 + 2r1 + 3r2 + 3 r1*r2) - # log(1 + r1 + 3r2 + 3 r1*r2) true <- function(beta) { r1 <- exp(beta[1]) r2 <- exp(beta[2]) loglik <- -log(3*(1+ r1+ r2+ r1*r2)) - log(2+ 2*r1 + 3*r2 + 3*r1*r2) - log(1 + r1 + 3*r2 + 3*r1*r2) loglik } all.equal(fit3$loglik[2], true(fit3$coef), check.attributes=FALSE) survival/tests/book3.R0000644000176200001440000001177514012565307014445 0ustar liggesuserslibrary(survival) options(na.action=na.exclude) # preserve missings options(contrasts=c('contr.treatment', 'contr.poly')) #ensure constrast type # # Tests from the appendix of Therneau and Grambsch # c. Data set 2 and Breslow estimate # test2 <- data.frame(start=c(1, 2, 5, 2, 1, 7, 3, 4, 8, 8), stop =c(2, 3, 6, 7, 8, 9, 9, 9,14,17), event=c(1, 1, 1, 1, 1, 1, 1, 0, 0, 0), x =c(1, 0, 0, 1, 0, 1, 1, 1, 0, 0)) byhand <- function(beta, newx=0) { r <- exp(beta) loglik <- 4*beta - log(r+1) - log(r+2) - 3*log(3*r+2) - 2*log(3*r+1) u <- 1/(r+1) + 1/(3*r+1) + 4/(3*r+2) - ( r/(r+2) +3*r/(3*r+2) + 3*r/(3*r+1)) imat <- r/(r+1)^2 + 2*r/(r+2)^2 + 6*r/(3*r+2)^2 + 3*r/(3*r+1)^2 + 3*r/(3*r+1)^2 + 12*r/(3*r+2)^2 hazard <-c( 1/(r+1), 1/(r+2), 1/(3*r+2), 1/(3*r+1), 1/(3*r+1), 2/(3*r+2) ) xbar <- c(r/(r+1), r/(r+2), 3*r/(3*r+2), 3*r/(3*r+1), 3*r/(3*r+1), 3*r/(3*r+2)) # The matrix of weights, one row per obs, one col per time # deaths at 2,3,6,7,8,9 wtmat <- matrix(c(1,0,0,0,1,0,0,0,0,0, 0,1,0,1,1,0,0,0,0,0, 0,0,1,1,1,0,1,1,0,0, 0,0,0,1,1,0,1,1,0,0, 0,0,0,0,1,1,1,1,0,0, 0,0,0,0,0,1,1,1,1,1), ncol=6) wtmat <- diag(c(r,1,1,r,1,r,r,r,1,1)) %*% wtmat x <- c(1,0,0,1,0,1,1,1,0,0) status <- c(1,1,1,1,1,1,1,0,0,0) xbar <- colSums(wtmat*x)/ colSums(wtmat) n <- length(x) # Table of sums for score and Schoenfeld resids hazmat <- wtmat %*% diag(hazard) #each subject's hazard over time dM <- -hazmat #Expected part for (i in 1:6) dM[i,i] <- dM[i,i] +1 #observed dM[7,6] <- dM[7,6] +1 # observed mart <- rowSums(dM) # Table of sums for score and Schoenfeld resids # Looks like the last table of appendix E.2.1 of the book resid <- dM * outer(x, xbar, '-') score <- rowSums(resid) scho <- colSums(resid) # We need to split the two tied times up, to match coxph scho <- c(scho[1:5], scho[6]/2, scho[6]/2) var.g <- cumsum(hazard*hazard /c(1,1,1,1,1,2)) var.d <- cumsum( (xbar-newx)*hazard) surv <- exp(-cumsum(hazard) * exp(beta*newx)) varhaz <- (var.g + var.d^2/imat)* exp(2*beta*newx) list(loglik=loglik, u=u, imat=imat, xbar=xbar, haz=hazard, mart=mart, score=score, rmat=resid, scho=scho, surv=surv, var=varhaz) } aeq <- function(x,y) all.equal(as.vector(x), as.vector(y)) fit0 <-coxph(Surv(start, stop, event) ~x, test2, iter=0, method='breslow') truth0 <- byhand(0,0) aeq(truth0$loglik, fit0$loglik[1]) aeq(1/truth0$imat, fit0$var) aeq(truth0$mart, fit0$resid) aeq(truth0$scho, resid(fit0, 'schoen')) aeq(truth0$score, resid(fit0, 'score')) sfit <- survfit(fit0, list(x=0), censor=FALSE) aeq(sfit$std.err^2, truth0$var) aeq(sfit$surv, truth0$surv) aeq(fit0$score, truth0$u^2/truth0$imat) beta1 <- truth0$u/truth0$imat fit1 <- coxph(Surv(start, stop, event) ~x, test2, iter=1, ties="breslow") aeq(beta1, coef(fit1)) truth <- byhand(-0.084526081, 0) fit <- coxph(Surv(start, stop, event) ~x, test2, eps=1e-8, method='breslow', nocenter= NULL) aeq(truth$loglik, fit$loglik[2]) aeq(1/truth$imat, fit$var) aeq(truth$mart, fit$resid) aeq(truth$scho, resid(fit, 'schoen')) aeq(truth$score, resid(fit, 'score')) expect <- predict(fit, type='expected', newdata=test2) #force recalc aeq(test2$event -fit$resid, expect) #tests the predict function sfit <- survfit(fit, list(x=0), censor=FALSE) aeq(sfit$std.err^2, truth$var) aeq(-log(sfit$surv), (cumsum(truth$haz))) # Reprise the test, with strata # offseting the times ensures that we will get the wrong risk sets # if strata were not kept separate test2b <- rbind(test2, test2, test2) test2b$group <- rep(1:3, each= nrow(test2)) test2b$start <- test2b$start + test2b$group test2b$stop <- test2b$stop + test2b$group fit0 <- coxph(Surv(start, stop, event) ~ x + strata(group), test2b, iter=0, method="breslow") aeq(3*truth0$loglik, fit0$loglik[1]) aeq(3*truth0$imat, 1/fit0$var) aeq(rep(truth0$mart,3), fit0$resid) aeq(rep(truth0$scho,3), resid(fit0, 'schoen')) aeq(rep(truth0$score,3), resid(fit0, 'score')) fit1 <- coxph(Surv(start, stop, event) ~ x + strata(group), test2b, iter=1, method="breslow") aeq(fit1$coef, beta1) fit3 <- coxph(Surv(start, stop, event) ~x + strata(group), test2b, eps=1e-8, method='breslow') aeq(3*truth$loglik, fit3$loglik[2]) aeq(3*truth$imat, 1/fit3$var) aeq(rep(truth$mart,3), fit3$resid) aeq(rep(truth$scho,3), resid(fit3, 'schoen')) aeq(rep(truth$score,3), resid(fit3, 'score')) # # Done with the formal test, now print out lots of bits # resid(fit) resid(fit, 'scor') resid(fit, 'scho') predict(fit, type='lp') predict(fit, type='risk') predict(fit, type='expected') predict(fit, type='terms') predict(fit, type='lp', se.fit=T) predict(fit, type='risk', se.fit=T) predict(fit, type='expected', se.fit=T) predict(fit, type='terms', se.fit=T) summary(survfit(fit)) summary(survfit(fit, list(x=2))) survival/tests/nested.Rout.save0000644000176200001440000000227513537676563016414 0ustar liggesusers R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(survival) Loading required package: splines > # > # A test of nesting. It makes sure the model.frame is built correctly > # > tfun <- function(fit, mydata) { + survfit(fit, newdata=mydata) + } > > myfit <- coxph(Surv(time, status) ~ age + factor(sex), lung) > > temp1 <- tfun(myfit, lung[1:5,]) > temp2 <- survfit(myfit, lung[1:5,]) > indx <- match('call', names(temp1)) #the call components won't match > > all.equal(unclass(temp1)[-indx], unclass(temp2)[-indx]) [1] TRUE > > > proc.time() user system elapsed 0.196 0.032 0.225 survival/tests/coxsurv.Rout.save0000644000176200001440000000604614013473206016616 0ustar liggesusers R Under development (unstable) (2021-02-16 r80015) -- "Unsuffered Consequences" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > options(na.action=na.exclude) # preserve missings > options(contrasts=c('contr.treatment', 'contr.poly')) #ensure constrast type > library(survival) > > # > # Test out subscripting in the case of a coxph survival curve > # > aeq <- function(x,y, ...) all.equal(as.vector(x), as.vector(y), ...) > > fit <- coxph(Surv(time, status) ~ age + sex + meal.cal + strata(ph.ecog), + data=lung) > surv1 <- survfit(fit) > temp <- surv1[2:3] > > which <- cumsum(surv1$strata) > zed <- (which[1]+1):(which[3]) > aeq(surv1$surv[zed], temp$surv) [1] TRUE > aeq(surv1$time[zed], temp$time) [1] TRUE > > # This call should not create a model frame in the code -- so same > # answer but a different path through the underlying code > fit <- coxph(Surv(time, status) ~ age + sex + meal.cal + strata(ph.ecog), + x=T, data=lung) > surv2 <- survfit(fit) > all.equal(surv1, surv2) [1] TRUE > > # > # Now a result with a matrix of survival curves > # > dummy <- data.frame(age=c(30,40,60), sex=c(1,2,2), meal.cal=c(500, 1000, 1500)) > surv2 <- survfit(fit, newdata=dummy) > > zed <- 1:which[1] > aeq(surv2$surv[zed,1], surv2[1,1]$surv) [1] TRUE > aeq(surv2$surv[zed,2], surv2[1,2]$surv) [1] TRUE > aeq(surv2$surv[zed,3], surv2[1,3]$surv) [1] TRUE > aeq(surv2$surv[zed, ], surv2[1,1:3]$surv) [1] TRUE > aeq(surv2$surv[zed], (surv2[1])$surv) [1] TRUE > aeq(surv2$surv[zed, ], surv2[1, ]$surv) [1] TRUE > > # And the depreciated form - call with a named vector as 'newdata' > # the resulting $call component won't match so delete it before comparing > surv3 <- survfit(fit, c(age=40, sex=2, meal.cal=1000)) > all.equal(unclass(surv2[,2])[-length(surv3)], unclass(surv3)[-length(surv3)]) [1] TRUE > > > # Test out offsets, which have recently become popular due to a Langholz paper > fit1 <- coxph(Surv(time, status) ~ age + ph.ecog, lung) > fit2 <- coxph(Surv(time, status) ~ age + offset(ph.ecog * fit1$coef[2]), lung) > > surv1 <- survfit(fit1, newdata=data.frame(age=50, ph.ecog=1)) > surv2 <- survfit(fit2, newdata=data.frame(age=50, ph.ecog=1)) > all.equal(surv1$surv, surv2$surv) [1] TRUE > > # > # Check out the start.time option > # > surv3 <- survfit(fit1, newdata=data.frame(age=50, ph.ecog=1), + start.time=100) > index <- match(surv3$time, surv1$time) > rescale <- summary(surv1, time=100)$surv > all.equal(surv3$surv, surv1$surv[index]/rescale) [1] TRUE > > > proc.time() user system elapsed 0.900 0.048 0.939 survival/tests/r_resid.Rout.save0000644000176200001440000003561213537676563016562 0ustar liggesusers R Under development (unstable) (2019-05-15 r76504) -- "Unsuffered Consequences" Copyright (C) 2019 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > options(na.action=na.exclude) # preserve missings > options(contrasts=c('contr.treatment', 'contr.poly')) #ensure constrast type > library(survival) > > aeq <- function(x,y, ...) all.equal(as.vector(x), as.vector(y), ...) > > fit1 <- survreg(Surv(futime, fustat) ~ age + ecog.ps, ovarian) > fit4 <- survreg(Surv(log(futime), fustat) ~age + ecog.ps, ovarian, + dist='extreme') > > print(fit1) Call: survreg(formula = Surv(futime, fustat) ~ age + ecog.ps, data = ovarian) Coefficients: (Intercept) age ecog.ps 12.28496723 -0.09702669 0.09977342 Scale= 0.6032744 Loglik(model)= -90 Loglik(intercept only)= -98 Chisq= 15.98 on 2 degrees of freedom, p= 0.000339 n= 26 > summary(fit4) Call: survreg(formula = Surv(log(futime), fustat) ~ age + ecog.ps, data = ovarian, dist = "extreme") Value Std. Error z p (Intercept) 12.2850 1.5015 8.18 2.8e-16 age -0.0970 0.0235 -4.13 3.7e-05 ecog.ps 0.0998 0.3657 0.27 0.785 Log(scale) -0.5054 0.2351 -2.15 0.032 Scale= 0.603 Extreme value distribution Loglik(model)= -21.8 Loglik(intercept only)= -29.8 Chisq= 15.98 on 2 degrees of freedom, p= 0.00034 Number of Newton-Raphson Iterations: 5 n= 26 > > > # Hypothesis (and I'm fairly sure): censorReg shares the fault of many > # iterative codes -- it returns the loglik and variance for iteration k > # but the coef vector of iteration k+1. Hence the "all.equal" tests > # below don't come out perfect. > # > if (exists('censorReg')) { #true for Splus, not R + fit2 <- censorReg(censor(futime, fustat) ~ age + ecog.ps, ovarian) + fit3 <- survreg(Surv(futime, fustat) ~ age + ecog.ps, ovarian, + iter=0, init=c(fit2$coef, log(fit2$scale))) + + aeq(resid(fit2, type='working')[,1], resid(fit3, type='working')) + aeq(resid(fit2, type='response')[,1], resid(fit3, type='response')) + + temp <- sign(resid(fit3, type='working')) + aeq(resid(fit2, type='deviance')[,1], + temp*abs(resid(fit3, type='deviance'))) + aeq(resid(fit2, type='deviance')[,1], resid(fit3, type='deviance')) + } > # > # Now check fit1 and fit4, which should follow identical iteration paths > # These tests should all be true > # > aeq(fit1$coef, fit4$coef) [1] TRUE > > resid(fit1, type='working') 1 2 3 4 5 6 -4.5081778 -0.5909810 -2.4878519 0.6032744 -5.8993431 0.6032744 7 8 9 10 11 12 -1.7462937 -0.8102883 0.6032744 -1.6593962 -0.8235265 0.6032744 13 14 15 16 17 18 0.6032744 0.6032744 0.6032744 0.6032744 0.6032744 0.6032744 19 20 21 22 23 24 0.6032744 0.6032744 0.6032744 0.2572623 -31.8006867 -0.7426277 25 26 -0.2857597 0.6032744 > resid(fit1, type='response') 1 2 3 4 5 6 -155.14523 -58.62744 -262.03173 -927.79842 -1377.84908 -658.86626 7 8 9 10 11 12 -589.74449 -318.93436 4.50671 -686.83338 -434.39281 -1105.68733 13 14 15 16 17 18 -42.43371 -173.09223 -4491.29974 -3170.49394 -5028.31053 -2050.91373 19 20 21 22 23 24 -150.65033 -2074.09345 412.32400 76.35826 -3309.40331 -219.81579 25 26 -96.19691 -457.76731 > resid(fit1, type='deviance') 1 2 3 4 5 6 7 -1.5842290 -0.6132746 -1.2876971 0.5387840 -1.7148539 0.6682580 -1.1102921 8 9 10 11 12 13 14 -0.7460191 1.4253843 -1.0849419 -0.7531720 0.6648130 1.3526380 1.1954382 15 16 17 18 19 20 21 0.2962391 0.3916044 0.3278067 0.5929057 1.2747643 0.6171130 1.9857606 22 23 24 25 26 0.6125492 -2.4504208 -0.7080652 -0.3642424 0.7317955 > resid(fit1, type='dfbeta') [,1] [,2] [,3] [,4] 1 0.43370970 -1.087867e-02 0.126322520 0.048379059 2 0.14426449 -5.144770e-03 0.088768478 -0.033939677 3 0.25768057 -3.066698e-03 -0.066578834 0.021817646 4 0.05772598 -5.068044e-04 -0.013121427 -0.007762466 5 -0.58773456 6.676156e-03 0.084189274 0.008064026 6 0.01499533 -7.881949e-04 0.026570173 -0.013513160 7 -0.17869321 4.126121e-03 -0.072760519 -0.015006956 8 -0.11851540 2.520303e-03 -0.045549628 -0.035686269 9 0.08327656 3.206404e-03 -0.141835350 0.024490806 10 -0.25083921 5.321702e-03 -0.073986269 -0.020648720 11 -0.21333934 4.155746e-03 -0.049832434 -0.040215681 12 0.13889770 -1.586136e-03 -0.019701151 -0.004686340 13 0.07892133 -2.706713e-03 0.085242459 0.007847879 14 0.29690157 -1.987141e-03 -0.085553120 0.017447343 15 0.04344618 -6.319243e-04 -0.001944285 -0.003533279 16 0.04866809 -1.068317e-03 0.012398602 -0.006340983 17 0.04368104 -9.248316e-04 0.009428718 -0.004869178 18 0.15684611 -2.081485e-03 -0.013068320 -0.003265399 19 0.48839511 -4.775829e-03 -0.093258090 0.032703354 20 0.17598922 -2.349254e-03 -0.014202966 -0.002486428 21 0.37869758 -8.442011e-03 0.163476417 0.100850775 22 -0.59761427 8.803638e-03 0.052784598 -0.053085234 23 -0.79017984 1.092304e-02 0.053690092 0.080780399 24 -0.02348526 8.331002e-04 -0.039028433 -0.032765737 25 -0.13948485 3.687927e-04 0.056781884 -0.055647859 26 0.05778937 3.766350e-06 -0.029232389 -0.008927920 > resid(fit1, type='dfbetas') [,1] [,2] [,3] [,4] 1 0.288846658 -0.4627232074 0.345395116 0.20574292 2 0.096078819 -0.2188323823 0.242713641 -0.14433617 3 0.171612884 -0.1304417700 -0.182041999 0.09278449 4 0.038444974 -0.0215568869 -0.035877029 -0.03301165 5 -0.391425795 0.2839697749 0.230193032 0.03429410 6 0.009986751 -0.0335258093 0.072649027 -0.05746778 7 -0.119008027 0.1755042532 -0.198944162 -0.06382048 8 -0.078930164 0.1072008799 -0.124543264 -0.15176395 9 0.055461420 0.1363841532 -0.387810796 0.10415271 10 -0.167056601 0.2263581990 -0.202295647 -0.08781336 11 -0.142082031 0.1767643342 -0.136253451 -0.17102630 12 0.092504589 -0.0674661531 -0.053867524 -0.01992972 13 0.052560878 -0.1151298322 0.233072686 0.03337488 14 0.197733705 -0.0845228882 -0.233922105 0.07419878 15 0.028934753 -0.0268788526 -0.005316126 -0.01502607 16 0.032412497 -0.0454407662 0.033900659 -0.02696647 17 0.029091172 -0.0393376416 0.025780305 -0.02070728 18 0.104458066 -0.0885357994 -0.035731824 -0.01388685 19 0.325266641 -0.2031395176 -0.254989284 0.13907843 20 0.117207199 -0.0999253459 -0.038834208 -0.01057410 21 0.252209096 -0.3590802699 0.446982501 0.42889079 22 -0.398005596 0.3744620571 0.144325354 -0.22575700 23 -0.526252483 0.4646108448 0.146801184 0.34353696 24 -0.015640965 0.0354358527 -0.106712804 -0.13934372 25 -0.092895624 0.0156865706 0.155254862 -0.23665514 26 0.038487186 0.0001602014 -0.079928144 -0.03796800 > resid(fit1, type='ldcase') 1 2 3 4 5 6 0.374432175 0.145690278 0.112678800 0.006399163 0.261176992 0.013280058 7 8 9 10 11 12 0.109842490 0.074103234 0.248285282 0.128482147 0.094038203 0.016111951 13 14 15 16 17 18 0.132812463 0.111857574 0.001698300 0.004730718 0.003131173 0.015840667 19 20 21 22 23 24 0.179925399 0.019071941 0.797119488 0.233096445 0.666613755 0.062959708 25 26 0.080117437 0.015922378 > resid(fit1, type='ldresp') 1 2 3 4 5 6 0.076910173 0.173810883 0.078356928 0.005310644 0.060742612 0.010002154 7 8 9 10 11 12 0.067356838 0.067065693 0.355103899 0.067043195 0.068142828 0.016740944 13 14 15 16 17 18 0.193444572 0.165021262 0.001494685 0.004083386 0.002767560 0.016400993 19 20 21 22 23 24 0.269571809 0.020129806 1.409736499 1.040266083 0.058637282 0.071819025 25 26 0.112702844 0.015105534 > resid(fit1, type='ldshape') 1 2 3 4 5 6 0.870628250 0.383362440 0.412503605 0.005534970 0.513991064 0.003310847 7 8 9 10 11 12 0.291860593 0.154910362 0.256160646 0.312329770 0.183191309 0.004184904 13 14 15 16 17 18 0.110215710 0.049299495 0.007678445 0.011633336 0.011588605 0.008641251 19 20 21 22 23 24 0.112967758 0.008271358 2.246729275 0.966929220 1.022043272 0.143857170 25 26 0.079754096 0.001606647 > resid(fit1, type='matrix') g dg ddg ds dds dsg 1 -1.74950763 -1.46198129 -0.32429540 0.88466493 -2.42358635 1.8800360 2 -0.68266980 -0.82027857 -1.38799493 -0.66206188 -0.57351872 1.3921043 3 -1.32369884 -1.33411374 -0.53625126 0.31503768 -1.83606321 1.8626973 4 -0.14514412 0.24059386 -0.39881329 -0.28013223 -0.26053084 0.2237590 5 -1.96497889 -1.50383619 -0.25491587 1.15700933 -2.68145423 1.8694717 6 -0.22328436 0.37012071 -0.61351964 -0.33477229 -0.16715487 0.1848047 7 -1.11099124 -1.23201028 -0.70550005 0.01052036 -1.48515401 1.8106760 8 -0.77288913 -0.95018808 -1.17265428 -0.51190170 -0.79753045 1.5525642 9 -1.01586016 1.68391053 -2.79128447 0.01598527 -0.01623681 -1.7104080 10 -1.08316634 -1.21566480 -0.73259465 -0.03052447 -1.43539383 1.7998987 11 -0.77825093 -0.95675178 -1.16177415 -0.50314979 -0.81016011 1.5600720 12 -0.22098818 0.36631452 -0.60721042 -0.33361394 -0.17002503 0.1866908 13 -0.91481479 1.51641567 -2.51364157 -0.08144930 0.07419757 -1.3814037 14 -0.71453621 1.18442981 -1.96333502 -0.24017106 0.15944438 -0.7863174 15 -0.04387880 0.07273440 -0.12056602 -0.13717935 -0.29168773 0.1546569 16 -0.07667699 0.12710134 -0.21068577 -0.19691828 -0.30879813 0.1993144 17 -0.05372862 0.08906165 -0.14763041 -0.15709224 -0.30221555 0.1713377 18 -0.17576861 0.29135764 -0.48296037 -0.30558900 -0.22570402 0.2151929 19 -0.81251205 1.34683655 -2.23254376 -0.16869744 0.13367171 -1.0672002 20 -0.19041424 0.31563454 -0.52320225 -0.31581218 -0.20797917 0.2078622 21 -1.97162252 3.26820173 -5.41743790 1.33844939 -2.24706488 -5.4868428 22 -0.68222519 1.23245193 -4.79064290 -0.58668577 -0.95209805 -2.8390386 23 -3.49689798 -1.62675999 -0.05115487 2.90949868 -4.20494743 1.7496975 24 -0.74529506 -0.91462436 -1.23160543 -0.55723389 -0.73139169 1.5108398 25 -0.56095318 -0.53280415 -1.86451840 -0.87536233 -0.22666819 0.9689667 26 -0.26776235 0.44384834 -0.73573207 -0.35281852 -0.11207472 0.1409908 > > aeq(resid(fit1, type='working'),resid(fit4, type='working')) [1] TRUE > #aeq(resid(fit1, type='response'), resid(fit4, type='response'))#should differ > aeq(resid(fit1, type='deviance'), resid(fit4, type='deviance')) [1] TRUE > aeq(resid(fit1, type='dfbeta'), resid(fit4, type='dfbeta')) [1] TRUE > aeq(resid(fit1, type='dfbetas'), resid(fit4, type='dfbetas')) [1] TRUE > aeq(resid(fit1, type='ldcase'), resid(fit4, type='ldcase')) [1] TRUE > aeq(resid(fit1, type='ldresp'), resid(fit4, type='ldresp')) [1] TRUE > aeq(resid(fit1, type='ldshape'), resid(fit4, type='ldshape')) [1] TRUE > aeq(resid(fit1, type='matrix'), resid(fit4, type='matrix')) [1] TRUE > # > # Some tests of the quantile residuals > # > # These should agree exactly with Ripley and Venables' book > fit1 <- survreg(Surv(time, status) ~ temp, data=imotor) > summary(fit1) Call: survreg(formula = Surv(time, status) ~ temp, data = imotor) Value Std. Error z p (Intercept) 16.31852 0.62296 26.2 < 2e-16 temp -0.04531 0.00319 -14.2 < 2e-16 Log(scale) -1.09564 0.21480 -5.1 3.4e-07 Scale= 0.334 Weibull distribution Loglik(model)= -147.4 Loglik(intercept only)= -169.5 Chisq= 44.32 on 1 degrees of freedom, p= 2.8e-11 Number of Newton-Raphson Iterations: 7 n= 40 > > # > # The first prediction has the SE that I think is correct > # The third is the se found in an early draft of Ripley; fit1 ignoring > # the variation in scale estimate, except via it's impact on the > # upper left corner of the inverse information matrix. > # Numbers 1 and 3 differ little for this dataset > # > predict(fit1, data.frame(temp=130), type='uquantile', p=c(.5, .1), se=T) $fit [1] 10.306068 9.676248 $se.fit [1] 0.2135247 0.2202088 > > fit2 <- survreg(Surv(time, status) ~ temp, data=imotor, scale=fit1$scale) > predict(fit2, data.frame(temp=130), type='uquantile', p=c(.5, .1), se=T) $fit [1] 10.306068 9.676248 $se.fit 1 1 0.2057964 0.2057964 > > fit3 <- fit2 > fit3$var <- fit1$var[1:2,1:2] > predict(fit3, data.frame(temp=130), type='uquantile', p=c(.5, .1), se=T) $fit [1] 10.306068 9.676248 $se.fit 1 1 0.2219959 0.2219959 > > pp <- seq(.05, .7, length=40) > xx <- predict(fit1, data.frame(temp=130), type='uquantile', se=T, + p=pp) > #matplot(pp, cbind(xx$fit, xx$fit+2*xx$se, xx$fit - 2*xx$se), type='l') > > > # > # Now try out the various combinations of strata, #predicted, and > # number of quantiles desired > # > fit1 <- survreg(Surv(time, status) ~ inst + strata(inst) + age + sex, lung) > qq1 <- predict(fit1, type='quantile', p=.3, se=T) > qq2 <- predict(fit1, type='quantile', p=c(.2, .3, .4), se=T) > aeq <- function(x,y) all.equal(as.vector(x), as.vector(y)) > aeq(qq1$fit, qq2$fit[,2]) [1] TRUE > aeq(qq1$se.fit, qq2$se.fit[,2]) [1] TRUE > > qq3 <- predict(fit1, type='quantile', p=c(.2, .3, .4), se=T, + newdata= lung[1:5,]) > aeq(qq3$fit, qq2$fit[1:5,]) [1] TRUE > > qq4 <- predict(fit1, type='quantile', p=c(.2, .3, .4), se=T, newdata=lung[7,]) > aeq(qq4$fit, qq2$fit[7,]) [1] TRUE > > qq5 <- predict(fit1, type='quantile', p=c(.2, .3, .4), se=T, newdata=lung) > aeq(qq2$fit, qq5$fit) [1] TRUE > aeq(qq2$se.fit, qq5$se.fit) [1] TRUE > > proc.time() user system elapsed 0.796 0.044 0.845 survival/tests/zph.Rout.save0000644000176200001440000002010413640437051015700 0ustar liggesusers R Under development (unstable) (2020-03-27 r78086) -- "Unsuffered Consequences" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(survival) > aeq <- function(x,y) all.equal(as.vector(x), as.vector(y)) > > test1 <- data.frame(time= c(9, 3,1,1,6,6,8), + status=c(1,NA,1,0,1,1,0), + x= c(0, 2,1,1,1,0,0)) > > # Verify that cox.zph computes a score test > # First for the Breslow estimate > r <- (3 + sqrt(33))/2 # actual MLE for log(beta) > U <- c(1/(r+1), 3/(r+3), -r/(r+3), 0) # score statistic > imat <- c(r/(r+1)^2, 3*r/(r+3)^2, 3*r/(r+3)^2, 0) # information matrix > g = c(1, 6, 6, 9) # death times > > u2 <- c(sum(U), sum(g*U)) # first derivative > i2 <- matrix(c(sum(imat), sum(g*imat), sum(g*imat), sum(g^2*imat)), + 2,2) # second derivative > sctest <- solve(i2, u2) %*% u2 > > # Verify that centering makes no difference for the test (though i2 changes) > g2 <- g - mean(g) > u2b <- c(sum(U), sum(g2*U)) > i2b <- matrix(c(sum(imat), sum(g2*imat), sum(g2*imat), sum(g2^2*imat)), + 2,2) > sctest2 <- solve(i2b, u2b) %*% u2b > all.equal(sctest, sctest2) [1] TRUE > > # Now check the program > fit1 <- coxph(Surv(time, status) ~ x, test1, ties='breslow') > aeq(fit1$coef, log(r)) [1] TRUE > zp1 <- cox.zph(fit1, transform='identity', global=FALSE) > aeq(zp1$table[,1], sctest) [1] TRUE > aeq(zp1$y, resid(fit1, type="scaledsch")) [1] TRUE > > dummy <- rep(0, nrow(test1)) > fit1b <- coxph(Surv(dummy, time, status) ~ x, test1, ties='breslow') > aeq(fit1b$coef, log(r)) [1] TRUE > zp1b <- cox.zph(fit1b, transform='identity', global=FALSE) > aeq(zp1b$table[,1], sctest) [1] TRUE > # the pair of tied times gets reversed in the zph result > # but since the 'y' values are only used to plot it doesn't matter > aeq(zp1b$y[c(1,3,2,4)], resid(fit1b, type="scaledsch")) [1] TRUE > > # log time check > g3 <- log(g) - mean(log(g)) > u3 <- c(sum(U), sum(g3*U)) # first derivative > i3 <- matrix(c(sum(imat), sum(g3*imat), sum(g3*imat), sum(g3^2*imat)), + 2,2) # second derivative > sctest3 <- solve(i3, u3) %*% u3 > zp3 <- cox.zph(fit1, transform='log', global=FALSE) > aeq(zp3$table[,1], sctest3) [1] TRUE > > # Efron approximation > phi <- acos((45/23)*sqrt(3/23)) > r <- 2*sqrt(23/3)* cos(phi/3) # actual MLE for log(beta) > U <- c(1/(r+1), 3/(r+3), -r/(r+5), 0) # score statistic > imat <- c(r/(r+1)^2, 3*r/(r+3)^2, 5*r/(r+5)^2, 0) # information matrix > > u4 <- c(sum(U), sum(g3*U)) # first derivative > i4 <- matrix(c(sum(imat), sum(g3*imat), sum(g3*imat), sum(g3^2*imat)), + 2,2) # second derivative > sctest4 <- solve(i4, u4) %*% u4 > > fit4 <- coxph(Surv(time, status) ~ x, test1, ties='efron') > aeq(fit4$coef, log(r)) [1] TRUE > zp4 <- cox.zph(fit4, transform='log', global=FALSE) > aeq(zp4$table[,1], sctest4) [1] TRUE > aeq(zp4$y, resid(fit4, type="scaledsch")) [1] TRUE > > fit5 <- coxph(Surv(dummy, time, status) ~ x, test1, ties="efron") > aeq(fit5$coef, log(r)) [1] TRUE > zp5 <- cox.zph(fit5, transform="log", global=FALSE) > aeq(zp5$table[,1], sctest4) [1] TRUE > > # Artificial stratification > test2 <- rbind(test1, test1) > test2$group <- rep(letters[1:2], each=nrow(test1)) > # U, imat, and sctest will all double > dummy <- c(dummy, dummy) > fit6 <- coxph(Surv(dummy, time, status) ~ x + strata(group), test2) > aeq(fit6$coef, log(r)) [1] TRUE > zp6 <- cox.zph(fit6, transform="log", globa=FALSE) > aeq(zp6$table[,1], 2*sctest4) [1] TRUE > > # A multi-state check, 2 covariates > # Verify that the multi-state result = the single state Cox models > etime <- with(mgus2, ifelse(pstat==0, futime, ptime)) > event <- with(mgus2, ifelse(pstat==0, 2*death, 1)) > event <- factor(event, 0:2, labels=c("censor", "pcm", "death")) > table(event) event censor pcm death 409 115 860 > > ct1 <- coxph(Surv(etime, event) ~ sex + age, mgus2, id=id) > ct2 <- coxph(Surv(etime, event=='pcm') ~ sex + age, mgus2) > ct3 <- coxph(Surv(etime, event=='death') ~ sex + age, mgus2) > > zp1 <- cox.zph(ct1, transform='identity') > zp2 <- cox.zph(ct2, transform='identity') > zp3 <- cox.zph(ct3, transform='identity') > aeq(zp1$table[1:2,], zp2$table[1:2,]) [1] TRUE > aeq(zp1$table[3:4,], zp3$table[1:2,]) [1] TRUE > > # Now add a starting time of zero > dummy <- rep(0, nrow(mgus2)) > ct4 <- coxph(Surv(dummy, etime, event) ~ sex + age, mgus2, id=id) > ct5 <- coxph(Surv(dummy, etime, event=='pcm') ~ sex + age, mgus2) > ct6 <- coxph(Surv(dummy, etime, event=='death') ~ sex + age, mgus2) > zp4 <- cox.zph(ct4, transform='identity') > zp5 <- cox.zph(ct5, transform='identity') > zp6 <- cox.zph(ct6, transform='identity') > aeq(zp4$table[1:2,], zp5$table[1:2,]) [1] TRUE > aeq(zp4$table[3:4,], zp6$table[1:2,]) [1] TRUE > > > # Direct check of a multivariate model with start, stop data > p1 <- pbcseq[!duplicated(pbcseq$id), 1:6] > pdata <- tmerge(p1[, c("id", "trt", "age", "sex")], p1, id=id, + death = event(futime, status==2)) > pdata <- tmerge(pdata, pbcseq, id=id, bili=tdc(day, bili), + edema = tdc(day, edema), albumin=tdc(day, albumin), + protime = tdc(day, protime)) > pfit <- coxph(Surv(tstart, tstop, death) ~ log(bili) + albumin + edema + + age + log(protime), data = pdata, ties='efron') > zp7 <- cox.zph(pfit, transform='log', global=FALSE) > > direct <- function(fit) { + nvar <- length(fit$coef) + dt <- coxph.detail(fit) + gtime <- log(dt$time) - mean(log(dt$time)) + # key idea: at any event time I have a first deriviative vector + # c(dt$score[i,], gtime[i]* dt$score[i,]) + # and second derivative matrix + # dt$imat[,,i] gtime[i] * dt$imat[,,i] + # gtime[i]*dt$imat[,,i] gtime[i]^2 * dt$imat[,,i] + # for the expanded model, where imat[,,i] is symmetric, + # and colSums(dt$score) =0 (since the model converged) + # + # Create score tests for adding one time-dependent variable + # gtime * x[,j] at a time: first derivative of this test is + # c(dt$score[i,], gtime[i]* dt$score[i,j]) + # and etc. + unew <- colSums(gtime * dt$score) + temp1 <- apply(dt$imat, 1:2, sum) + temp2 <- apply(dt$imat, 1:2, function(x) sum(x*gtime)) + temp3 <- apply(dt$imat, 1:2, function(x) sum(x * gtime^2)) + + score <- double(nvar) + smat <- matrix(0., nvar+1, nvar+1) # second deriv matrix for the test + smat[1:nvar, 1:nvar] <- temp1 + for (i in 1:nvar) { + smat[nvar+1,] <- c(temp2[i,], temp3[i,i]) + smat[,nvar+1] <- c(temp2[,i], temp3[i,i]) + utemp <- c(rep(0,nvar), unew[i]) + score[i] <- solve(smat, utemp) %*% utemp + } + list(sctest = score, u= c(colSums(dt$score), unew), + imat=cbind(rbind(temp1, temp2), rbind(temp2, temp3))) + } > > aeq(zp7$table[,1], direct(pfit)$sctest) [1] TRUE > > # Last, make sure that NA coefficients are ignored > d1 <- survSplit(Surv(time, status) ~ ., veteran, cut=150, episode="epoch") > fit <- coxph(Surv(tstart, time, status) ~ celltype:strata(epoch) + age, d1) > zz <- cox.zph(fit) > > fit2 <- coxph(Surv(tstart, time, status) ~ celltype:strata(epoch) + age, d1, + x=TRUE) > zz2 <- cox.zph(fit2) > > x2 <- fit2$x[, !is.na(fit$coefficients)][,-1] > fit3 <- coxph(Surv(tstart, time, status) ~ age + x2, d1) > all.equal(fit3$loglik, fit2$loglik) [1] TRUE > zz3 <- cox.zph(fit3) > > all.equal(unclass(zz)[1:7], unclass(zz2)[1:7]) #ignore the call component [1] TRUE > all.equal(as.vector(zz$table), as.vector(zz3$table)) # variable names change [1] TRUE > > > > > > proc.time() user system elapsed 1.239 0.059 1.290 survival/tests/frailty.Rout.save0000644000176200001440000000317613537676563016605 0ustar liggesusers R Under development (unstable) (2019-05-15 r76504) -- "Unsuffered Consequences" Copyright (C) 2019 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(survival) > # > # The constuction of a survival curve with sparse frailties > # > # In this case the coefficient vector is kept in two parts, the > # fixed coefs and the (often very large) random effects coefficients > # The survfit function treats the second set of coefficients as fixed > # values, to avoid an unmanagable variance matrix, and behaves like > # the second fit below. > > fit1 <- coxph(Surv(time, status) ~ age + frailty(inst), lung) > sfit1 <- survfit(fit1) > > # A parallel model with the frailties treated as fixed offsets > offvar <- fit1$frail[as.numeric(factor(lung$inst))] > fit2 <- coxph(Surv(time, status) ~ age + offset(offvar),lung) > fit2$var <- fit1$var #force variances to match > > all.equal(fit1$coef, fit2$coef) [1] TRUE > sfit2 <- survfit(fit2, newdata=list(age=fit1$means, offvar=0)) > all.equal(sfit1$surv, sfit2$surv, tol=1e-7) [1] TRUE > all.equal(sfit1$var, sfit2$var) [1] TRUE > > proc.time() user system elapsed 0.768 0.040 0.807 survival/tests/Examples/0000755000176200001440000000000014110731531015041 5ustar liggesuserssurvival/tests/Examples/survival-Ex.Rout.save0000644000176200001440000036030514110723730021107 0ustar liggesusers R Under development (unstable) (2021-08-12 r80744) -- "Unsuffered Consequences" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > pkgname <- "survival" > source(file.path(R.home("share"), "R", "examples-header.R")) > options(warn = 1) > library('survival') > > base::assign(".oldSearch", base::search(), pos = 'CheckExEnv') > base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv') > cleanEx() > nameEx("Surv") > ### * Surv > > flush(stderr()); flush(stdout()) > > ### Name: Surv > ### Title: Create a Survival Object > ### Aliases: Surv is.Surv [.Surv > ### Keywords: survival > > ### ** Examples > > with(aml, Surv(time, status)) [1] 9 13 13+ 18 23 28+ 31 34 45+ 48 161+ 5 5 8 8 [16] 12 16+ 23 27 30 33 43 45 > survfit(Surv(time, status) ~ ph.ecog, data=lung) Call: survfit(formula = Surv(time, status) ~ ph.ecog, data = lung) 1 observation deleted due to missingness n events median 0.95LCL 0.95UCL ph.ecog=0 63 37 394 348 574 ph.ecog=1 113 82 306 268 429 ph.ecog=2 50 44 199 156 288 ph.ecog=3 1 1 118 NA NA > Surv(heart$start, heart$stop, heart$event) [1] ( 0.0, 50.0] ( 0.0, 6.0] ( 0.0, 1.0+] ( 1.0, 16.0] [5] ( 0.0, 36.0+] ( 36.0, 39.0] ( 0.0, 18.0] ( 0.0, 3.0] [9] ( 0.0, 51.0+] ( 51.0, 675.0] ( 0.0, 40.0] ( 0.0, 85.0] [13] ( 0.0, 12.0+] ( 12.0, 58.0] ( 0.0, 26.0+] ( 26.0, 153.0] [17] ( 0.0, 8.0] ( 0.0, 17.0+] ( 17.0, 81.0] ( 0.0, 37.0+] [21] ( 37.0,1387.0] ( 0.0, 1.0] ( 0.0, 28.0+] ( 28.0, 308.0] [25] ( 0.0, 36.0] ( 0.0, 20.0+] ( 20.0, 43.0] ( 0.0, 37.0] [29] ( 0.0, 18.0+] ( 18.0, 28.0] ( 0.0, 8.0+] ( 8.0,1032.0] [33] ( 0.0, 12.0+] ( 12.0, 51.0] ( 0.0, 3.0+] ( 3.0, 733.0] [37] ( 0.0, 83.0+] ( 83.0, 219.0] ( 0.0, 25.0+] ( 25.0,1800.0+] [41] ( 0.0,1401.0+] ( 0.0, 263.0] ( 0.0, 71.0+] ( 71.0, 72.0] [45] ( 0.0, 35.0] ( 0.0, 16.0+] ( 16.0, 852.0] ( 0.0, 16.0] [49] ( 0.0, 17.0+] ( 17.0, 77.0] ( 0.0, 51.0+] ( 51.0,1587.0+] [53] ( 0.0, 23.0+] ( 23.0,1572.0+] ( 0.0, 12.0] ( 0.0, 46.0+] [57] ( 46.0, 100.0] ( 0.0, 19.0+] ( 19.0, 66.0] ( 0.0, 4.5+] [61] ( 4.5, 5.0] ( 0.0, 2.0+] ( 2.0, 53.0] ( 0.0, 41.0+] [65] ( 41.0,1408.0+] ( 0.0, 58.0+] ( 58.0,1322.0+] ( 0.0, 3.0] [69] ( 0.0, 2.0] ( 0.0, 40.0] ( 0.0, 1.0+] ( 1.0, 45.0] [73] ( 0.0, 2.0+] ( 2.0, 996.0] ( 0.0, 21.0+] ( 21.0, 72.0] [77] ( 0.0, 9.0] ( 0.0, 36.0+] ( 36.0,1142.0+] ( 0.0, 83.0+] [81] ( 83.0, 980.0] ( 0.0, 32.0+] ( 32.0, 285.0] ( 0.0, 102.0] [85] ( 0.0, 41.0+] ( 41.0, 188.0] ( 0.0, 3.0] ( 0.0, 10.0+] [89] ( 10.0, 61.0] ( 0.0, 67.0+] ( 67.0, 942.0+] ( 0.0, 149.0] [93] ( 0.0, 21.0+] ( 21.0, 343.0] ( 0.0, 78.0+] ( 78.0, 916.0+] [97] ( 0.0, 3.0+] ( 3.0, 68.0] ( 0.0, 2.0] ( 0.0, 69.0] [101] ( 0.0, 27.0+] ( 27.0, 842.0+] ( 0.0, 33.0+] ( 33.0, 584.0] [105] ( 0.0, 12.0+] ( 12.0, 78.0] ( 0.0, 32.0] ( 0.0, 57.0+] [109] ( 57.0, 285.0] ( 0.0, 3.0+] ( 3.0, 68.0] ( 0.0, 10.0+] [113] ( 10.0, 670.0+] ( 0.0, 5.0+] ( 5.0, 30.0] ( 0.0, 31.0+] [117] ( 31.0, 620.0+] ( 0.0, 4.0+] ( 4.0, 596.0+] ( 0.0, 27.0+] [121] ( 27.0, 90.0] ( 0.0, 5.0+] ( 5.0, 17.0] ( 0.0, 2.0] [125] ( 0.0, 46.0+] ( 46.0, 545.0+] ( 0.0, 21.0] ( 0.0, 210.0+] [129] (210.0, 515.0+] ( 0.0, 67.0+] ( 67.0, 96.0] ( 0.0, 26.0+] [133] ( 26.0, 482.0+] ( 0.0, 6.0+] ( 6.0, 445.0+] ( 0.0, 428.0+] [137] ( 0.0, 32.0+] ( 32.0, 80.0] ( 0.0, 37.0+] ( 37.0, 334.0] [141] ( 0.0, 5.0] ( 0.0, 8.0+] ( 8.0, 397.0+] ( 0.0, 60.0+] [145] ( 60.0, 110.0] ( 0.0, 31.0+] ( 31.0, 370.0+] ( 0.0, 139.0+] [149] (139.0, 207.0] ( 0.0, 160.0+] (160.0, 186.0] ( 0.0, 340.0] [153] ( 0.0, 310.0+] (310.0, 340.0+] ( 0.0, 28.0+] ( 28.0, 265.0+] [157] ( 0.0, 4.0+] ( 4.0, 165.0] ( 0.0, 2.0+] ( 2.0, 16.0] [161] ( 0.0, 13.0+] ( 13.0, 180.0+] ( 0.0, 21.0+] ( 21.0, 131.0+] [165] ( 0.0, 96.0+] ( 96.0, 109.0+] ( 0.0, 21.0] ( 0.0, 38.0+] [169] ( 38.0, 39.0+] ( 0.0, 31.0+] ( 0.0, 11.0+] ( 0.0, 6.0] > > > > cleanEx() > nameEx("aareg") > ### * aareg > > flush(stderr()); flush(stdout()) > > ### Name: aareg > ### Title: Aalen's additive regression model for censored data > ### Aliases: aareg > ### Keywords: survival > > ### ** Examples > > # Fit a model to the lung cancer data set > lfit <- aareg(Surv(time, status) ~ age + sex + ph.ecog, data=lung, + nmin=1) > ## Not run: > ##D lfit > ##D Call: > ##D aareg(formula = Surv(time, status) ~ age + sex + ph.ecog, data = lung, nmin = 1 > ##D ) > ##D > ##D n=227 (1 observations deleted due to missing values) > ##D 138 out of 138 unique event times used > ##D > ##D slope coef se(coef) z p > ##D Intercept 5.26e-03 5.99e-03 4.74e-03 1.26 0.207000 > ##D age 4.26e-05 7.02e-05 7.23e-05 0.97 0.332000 > ##D sex -3.29e-03 -4.02e-03 1.22e-03 -3.30 0.000976 > ##D ph.ecog 3.14e-03 3.80e-03 1.03e-03 3.70 0.000214 > ##D > ##D Chisq=26.73 on 3 df, p=6.7e-06; test weights=aalen > ##D > ##D plot(lfit[4], ylim=c(-4,4)) # Draw a plot of the function for ph.ecog > ## End(Not run) > lfit2 <- aareg(Surv(time, status) ~ age + sex + ph.ecog, data=lung, + nmin=1, taper=1:10) > ## Not run: lines(lfit2[4], col=2) # Nearly the same, until the last point > > # A fit to the mulitple-infection data set of children with > # Chronic Granuomatous Disease. See section 8.5 of Therneau and Grambsch. > fita2 <- aareg(Surv(tstart, tstop, status) ~ treat + age + inherit + + steroids + cluster(id), data=cgd) > ## Not run: > ##D n= 203 > ##D 69 out of 70 unique event times used > ##D > ##D slope coef se(coef) robust se z p > ##D Intercept 0.004670 0.017800 0.002780 0.003910 4.55 5.30e-06 > ##D treatrIFN-g -0.002520 -0.010100 0.002290 0.003020 -3.36 7.87e-04 > ##D age -0.000101 -0.000317 0.000115 0.000117 -2.70 6.84e-03 > ##D inheritautosomal 0.001330 0.003830 0.002800 0.002420 1.58 1.14e-01 > ##D steroids 0.004620 0.013200 0.010600 0.009700 1.36 1.73e-01 > ##D > ##D Chisq=16.74 on 4 df, p=0.0022; test weights=aalen > ## End(Not run) > > > > cleanEx() > nameEx("aggregate.survfit") > ### * aggregate.survfit > > flush(stderr()); flush(stdout()) > > ### Name: aggregate.survfit > ### Title: Average survival curves > ### Aliases: aggregate.survfit > ### Keywords: survival > > ### ** Examples > > cfit <- coxph(Surv(futime, death) ~ sex + age*hgb, data=mgus2) > # marginal effect of sex, after adjusting for the others > dummy <- rbind(mgus2, mgus2) > dummy$sex <- rep(c("F", "M"), each=nrow(mgus2)) # population data set > dummy <- na.omit(dummy) # don't count missing hgb in our "population > csurv <- survfit(cfit, newdata=dummy) > dim(csurv) # 2 * 1384 survival curves data 2676 > csurv2 <- aggregate(csurv, dummy$sex) > > > > cleanEx() > nameEx("anova.coxph") > ### * anova.coxph > > flush(stderr()); flush(stdout()) > > ### Name: anova.coxph > ### Title: Analysis of Deviance for a Cox model. > ### Aliases: anova.coxph anova.coxphlist > ### Keywords: models regression survival > > ### ** Examples > > fit <- coxph(Surv(futime, fustat) ~ resid.ds *rx + ecog.ps, data = ovarian) > anova(fit) Analysis of Deviance Table Cox model: response is Surv(futime, fustat) Terms added sequentially (first to last) loglik Chisq Df Pr(>|Chi|) NULL -34.985 resid.ds -33.105 3.7594 1 0.05251 . rx -32.269 1.6733 1 0.19582 ecog.ps -31.970 0.5980 1 0.43934 resid.ds:rx -30.946 2.0469 1 0.15251 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > fit2 <- coxph(Surv(futime, fustat) ~ resid.ds +rx + ecog.ps, data=ovarian) > anova(fit2,fit) Analysis of Deviance Table Cox model: response is Surv(futime, fustat) Model 1: ~ resid.ds + rx + ecog.ps Model 2: ~ resid.ds * rx + ecog.ps loglik Chisq Df P(>|Chi|) 1 -31.970 2 -30.946 2.0469 1 0.1525 > > > > cleanEx() > nameEx("attrassign") > ### * attrassign > > flush(stderr()); flush(stdout()) > > ### Name: attrassign > ### Title: Create new-style "assign" attribute > ### Aliases: attrassign.default attrassign attrassign.lm > ### Keywords: models > > ### ** Examples > > formula <- Surv(time,status)~factor(ph.ecog) > tt <- terms(formula) > mf <- model.frame(tt,data=lung) > mm <- model.matrix(tt,mf) > ## a few rows of data > mm[1:3,] (Intercept) factor(ph.ecog)1 factor(ph.ecog)2 factor(ph.ecog)3 1 1 1 0 0 2 1 0 0 0 3 1 0 0 0 > ## old-style assign attribute > attr(mm,"assign") [1] 0 1 1 1 > ## alternate style assign attribute > attrassign(mm,tt) $`(Intercept)` [1] 1 $`factor(ph.ecog)` [1] 2 3 4 > > > > cleanEx() > nameEx("blogit") > ### * blogit > > flush(stderr()); flush(stdout()) > > ### Name: blogit > ### Title: Bounded link functions > ### Aliases: blogit bcloglog bprobit blog > ### Keywords: survival > > ### ** Examples > > py <- pseudo(survfit(Surv(time, status) ~1, lung), time=730) #2 year survival > range(py) [1] -0.335248 1.693831 > pfit <- glm(py ~ ph.ecog, data=lung, family=gaussian(link=blogit())) > # For each +1 change in performance score, the odds of 2 year survival > # are multiplied by 1/2 = exp of the coefficient. > > > > cleanEx() > nameEx("cch") > ### * cch > > flush(stderr()); flush(stdout()) > > ### Name: cch > ### Title: Fits proportional hazards regression model to case-cohort data > ### Aliases: cch > ### Keywords: survival > > ### ** Examples > > ## The complete Wilms Tumor Data > ## (Breslow and Chatterjee, Applied Statistics, 1999) > ## subcohort selected by simple random sampling. > ## > > subcoh <- nwtco$in.subcohort > selccoh <- with(nwtco, rel==1|subcoh==1) > ccoh.data <- nwtco[selccoh,] > ccoh.data$subcohort <- subcoh[selccoh] > ## central-lab histology > ccoh.data$histol <- factor(ccoh.data$histol,labels=c("FH","UH")) > ## tumour stage > ccoh.data$stage <- factor(ccoh.data$stage,labels=c("I","II","III","IV")) > ccoh.data$age <- ccoh.data$age/12 # Age in years > > ## > ## Standard case-cohort analysis: simple random subcohort > ## > > fit.ccP <- cch(Surv(edrel, rel) ~ stage + histol + age, data =ccoh.data, + subcoh = ~subcohort, id=~seqno, cohort.size=4028) > > > fit.ccP Case-cohort analysis,x$method, Prentice with subcohort of 668 from cohort of 4028 Call: cch(formula = Surv(edrel, rel) ~ stage + histol + age, data = ccoh.data, subcoh = ~subcohort, id = ~seqno, cohort.size = 4028) Coefficients: Value SE Z p stageII 0.73457084 0.16849620 4.359569 1.303187e-05 stageIII 0.59708356 0.17345094 3.442377 5.766257e-04 stageIV 1.38413197 0.20481982 6.757803 1.400990e-11 histolUH 1.49806307 0.15970515 9.380180 0.000000e+00 age 0.04326787 0.02373086 1.823274 6.826184e-02 > > fit.ccSP <- cch(Surv(edrel, rel) ~ stage + histol + age, data =ccoh.data, + subcoh = ~subcohort, id=~seqno, cohort.size=4028, method="SelfPren") > > summary(fit.ccSP) Case-cohort analysis,x$method, SelfPrentice with subcohort of 668 from cohort of 4028 Call: cch(formula = Surv(edrel, rel) ~ stage + histol + age, data = ccoh.data, subcoh = ~subcohort, id = ~seqno, cohort.size = 4028, method = "SelfPren") Coefficients: Coef HR (95% CI) p stageII 0.736 2.088 1.501 2.905 0.000 stageIII 0.597 1.818 1.294 2.553 0.001 stageIV 1.392 4.021 2.692 6.008 0.000 histolUH 1.506 4.507 3.295 6.163 0.000 age 0.043 1.044 0.997 1.094 0.069 > > ## > ## (post-)stratified on instit > ## > stratsizes<-table(nwtco$instit) > fit.BI<- cch(Surv(edrel, rel) ~ stage + histol + age, data =ccoh.data, + subcoh = ~subcohort, id=~seqno, stratum=~instit, cohort.size=stratsizes, + method="I.Borgan") > > summary(fit.BI) Exposure-stratified case-cohort analysis, I.Borgan method. 1 2 subcohort 952 202 cohort 3622 406 Call: cch(formula = Surv(edrel, rel) ~ stage + histol + age, data = ccoh.data, subcoh = ~subcohort, id = ~seqno, stratum = ~instit, cohort.size = stratsizes, method = "I.Borgan") Coefficients: Coef HR (95% CI) p stageII 0.737 2.090 1.501 2.909 0.000 stageIII 0.602 1.825 1.301 2.561 0.000 stageIV 1.395 4.036 2.702 6.029 0.000 histolUH 1.522 4.580 3.450 6.080 0.000 age 0.043 1.044 0.996 1.093 0.072 > > > > cleanEx() > nameEx("cipoisson") > ### * cipoisson > > flush(stderr()); flush(stdout()) > > ### Name: cipoisson > ### Title: Confidence limits for the Poisson > ### Aliases: cipoisson > > ### ** Examples > > cipoisson(4) # 95% confidence limit lower upper 1.089865 10.241589 > # lower upper > # 1.089865 10.24153 > ppois(4, 10.24153) #chance of seeing 4 or fewer events with large rate [1] 0.02500096 > # [1] 0.02500096 > 1-ppois(3, 1.08986) #chance of seeing 4 or more, with a small rate [1] 0.02499961 > # [1] 0.02499961 > > > > > cleanEx() > nameEx("clogit") > ### * clogit > > flush(stderr()); flush(stdout()) > > ### Name: clogit > ### Title: Conditional logistic regression > ### Aliases: clogit > ### Keywords: survival models > > ### ** Examples > > ## Not run: clogit(case ~ spontaneous + induced + strata(stratum), data=infert) > > # A multinomial response recoded to use clogit > # The revised data set has one copy per possible outcome level, with new > # variable tocc = target occupation for this copy, and case = whether > # that is the actual outcome for each subject. > # See the reference below for the data. > resp <- levels(logan$occupation) > n <- nrow(logan) > indx <- rep(1:n, length(resp)) > logan2 <- data.frame(logan[indx,], + id = indx, + tocc = factor(rep(resp, each=n))) > logan2$case <- (logan2$occupation == logan2$tocc) > clogit(case ~ tocc + tocc:education + strata(id), logan2) Call: clogit(case ~ tocc + tocc:education + strata(id), logan2) coef exp(coef) se(coef) z p toccfarm -1.8964629 0.1500986 1.3807822 -1.373 0.16961 toccoperatives 1.1667502 3.2115388 0.5656465 2.063 0.03914 toccprofessional -8.1005492 0.0003034 0.6987244 -11.593 < 2e-16 toccsales -5.0292297 0.0065438 0.7700862 -6.531 6.54e-11 tocccraftsmen:education -0.3322842 0.7172835 0.0568682 -5.843 5.13e-09 toccfarm:education -0.3702858 0.6905370 0.1164100 -3.181 0.00147 toccoperatives:education -0.4222188 0.6555906 0.0584328 -7.226 4.98e-13 toccprofessional:education 0.2782469 1.3208122 0.0510212 5.454 4.94e-08 toccsales:education NA NA 0.0000000 NA NA Likelihood ratio test=665.5 on 8 df, p=< 2.2e-16 n= 4190, number of events= 838 > > > > cleanEx() > nameEx("cluster") > ### * cluster > > flush(stderr()); flush(stdout()) > > ### Name: cluster > ### Title: Identify clusters. > ### Aliases: cluster > ### Keywords: survival > > ### ** Examples > > marginal.model <- coxph(Surv(time, status) ~ rx, data= rats, cluster=litter, + subset=(sex=='f')) > frailty.model <- coxph(Surv(time, status) ~ rx + frailty(litter), rats, + subset=(sex=='f')) > > > > cleanEx() > nameEx("concordance") > ### * concordance > > flush(stderr()); flush(stdout()) > > ### Name: concordance > ### Title: Compute the concordance statistic for data or a model > ### Aliases: concordance concordance.coxph concordance.formula > ### concordance.lm concordance.survreg > ### Keywords: survival > > ### ** Examples > > fit1 <- coxph(Surv(ptime, pstat) ~ age + sex + mspike, mgus2) > concordance(fit1, timewt="n") Call: concordance.coxph(object = fit1, timewt = "n") n= 1373 Concordance= 0.6577 se= 0.03057 concordant discordant tied.x tied.y tied.xy 54083 28129 79 31 0 > > # logistic regression > fit2 <- glm(pstat ~ age + sex + mspike, binomial, data= mgus2) > concordance(fit2) # equal to the AUC Call: concordance.lm(object = fit2) n= 1373 Concordance= 0.6861 se= 0.02592 concordant discordant tied.x tied.y tied.xy 99195 45360 115 796404 804 > > > > cleanEx() > nameEx("cox.zph") > ### * cox.zph > > flush(stderr()); flush(stdout()) > > ### Name: cox.zph > ### Title: Test the Proportional Hazards Assumption of a Cox Regression > ### Aliases: cox.zph [.cox.zph print.cox.zph > ### Keywords: survival > > ### ** Examples > > fit <- coxph(Surv(futime, fustat) ~ age + ecog.ps, + data=ovarian) > temp <- cox.zph(fit) > print(temp) # display the results chisq df p age 0.698 1 0.40 ecog.ps 2.371 1 0.12 GLOBAL 3.633 2 0.16 > plot(temp) # plot curves > > > > cleanEx() > nameEx("coxph") > ### * coxph > > flush(stderr()); flush(stdout()) > > ### Name: coxph > ### Title: Fit Proportional Hazards Regression Model > ### Aliases: coxph print.coxph.null print.coxph.penal coxph.penalty > ### coxph.getdata summary.coxph.penal > ### Keywords: survival > > ### ** Examples > > # Create the simplest test data set > test1 <- list(time=c(4,3,1,1,2,2,3), + status=c(1,1,1,0,1,1,0), + x=c(0,2,1,1,1,0,0), + sex=c(0,0,0,0,1,1,1)) > # Fit a stratified model > coxph(Surv(time, status) ~ x + strata(sex), test1) Call: coxph(formula = Surv(time, status) ~ x + strata(sex), data = test1) coef exp(coef) se(coef) z p x 0.8023 2.2307 0.8224 0.976 0.329 Likelihood ratio test=1.09 on 1 df, p=0.2971 n= 7, number of events= 5 > # Create a simple data set for a time-dependent model > test2 <- list(start=c(1,2,5,2,1,7,3,4,8,8), + stop=c(2,3,6,7,8,9,9,9,14,17), + event=c(1,1,1,1,1,1,1,0,0,0), + x=c(1,0,0,1,0,1,1,1,0,0)) > summary(coxph(Surv(start, stop, event) ~ x, test2)) Call: coxph(formula = Surv(start, stop, event) ~ x, data = test2) n= 10, number of events= 7 coef exp(coef) se(coef) z Pr(>|z|) x -0.02111 0.97912 0.79518 -0.027 0.979 exp(coef) exp(-coef) lower .95 upper .95 x 0.9791 1.021 0.2061 4.653 Concordance= 0.526 (se = 0.129 ) Likelihood ratio test= 0 on 1 df, p=1 Wald test = 0 on 1 df, p=1 Score (logrank) test = 0 on 1 df, p=1 > > # > # Create a simple data set for a time-dependent model > # > test2 <- list(start=c(1, 2, 5, 2, 1, 7, 3, 4, 8, 8), + stop =c(2, 3, 6, 7, 8, 9, 9, 9,14,17), + event=c(1, 1, 1, 1, 1, 1, 1, 0, 0, 0), + x =c(1, 0, 0, 1, 0, 1, 1, 1, 0, 0) ) > > > summary( coxph( Surv(start, stop, event) ~ x, test2)) Call: coxph(formula = Surv(start, stop, event) ~ x, data = test2) n= 10, number of events= 7 coef exp(coef) se(coef) z Pr(>|z|) x -0.02111 0.97912 0.79518 -0.027 0.979 exp(coef) exp(-coef) lower .95 upper .95 x 0.9791 1.021 0.2061 4.653 Concordance= 0.526 (se = 0.129 ) Likelihood ratio test= 0 on 1 df, p=1 Wald test = 0 on 1 df, p=1 Score (logrank) test = 0 on 1 df, p=1 > > # Fit a stratified model, clustered on patients > > bladder1 <- bladder[bladder$enum < 5, ] > coxph(Surv(stop, event) ~ (rx + size + number) * strata(enum), + cluster = id, bladder1) Call: coxph(formula = Surv(stop, event) ~ (rx + size + number) * strata(enum), data = bladder1, cluster = id) coef exp(coef) se(coef) robust se z p rx -0.52598 0.59097 0.31583 0.31524 -1.669 0.09521 size 0.06961 1.07209 0.10156 0.08863 0.785 0.43220 number 0.23818 1.26894 0.07588 0.07459 3.193 0.00141 rx:strata(enum)enum=2 -0.10633 0.89913 0.50424 0.33396 -0.318 0.75019 rx:strata(enum)enum=3 -0.17251 0.84155 0.55780 0.39868 -0.433 0.66523 rx:strata(enum)enum=4 -0.10945 0.89632 0.65730 0.50636 -0.216 0.82886 size:strata(enum)enum=2 -0.14737 0.86298 0.16803 0.11409 -1.292 0.19646 size:strata(enum)enum=3 -0.28345 0.75318 0.20894 0.15220 -1.862 0.06255 size:strata(enum)enum=4 -0.27607 0.75876 0.25222 0.18904 -1.460 0.14418 number:strata(enum)enum=2 -0.10125 0.90370 0.11904 0.11759 -0.861 0.38920 number:strata(enum)enum=3 -0.06467 0.93738 0.12925 0.12035 -0.537 0.59101 number:strata(enum)enum=4 0.09429 1.09888 0.14594 0.11973 0.788 0.43097 Likelihood ratio test=30.09 on 12 df, p=0.002708 n= 340, number of events= 112 > > # Fit a time transform model using current age > coxph(Surv(time, status) ~ ph.ecog + tt(age), data=lung, + tt=function(x,t,...) pspline(x + t/365.25)) Call: coxph(formula = Surv(time, status) ~ ph.ecog + tt(age), data = lung, tt = function(x, t, ...) pspline(x + t/365.25)) coef se(coef) se2 Chisq DF p ph.ecog 0.4528 0.1178 0.1174 14.7704 1.00 0.00012 tt(age), linear 0.0112 0.0093 0.0093 1.4414 1.00 0.22991 tt(age), nonlin 2.6992 3.08 0.45431 Iterations: 4 outer, 10 Newton-Raphson Theta= 0.796 Degrees of freedom for terms= 1.0 4.1 Likelihood ratio test=22.5 on 5.07 df, p=5e-04 n= 227, number of events= 164 (1 observation deleted due to missingness) > > > > cleanEx() > nameEx("coxph.detail") > ### * coxph.detail > > flush(stderr()); flush(stdout()) > > ### Name: coxph.detail > ### Title: Details of a Cox Model Fit > ### Aliases: coxph.detail > ### Keywords: survival > > ### ** Examples > > fit <- coxph(Surv(futime,fustat) ~ age + rx + ecog.ps, ovarian, x=TRUE) > fitd <- coxph.detail(fit) > # There is one Schoenfeld residual for each unique death. It is a > # vector (covariates for the subject who died) - (weighted mean covariate > # vector at that time). The weighted mean is defined over the subjects > # still at risk, with exp(X beta) as the weight. > > events <- fit$y[,2]==1 > etime <- fit$y[events,1] #the event times --- may have duplicates > indx <- match(etime, fitd$time) > schoen <- fit$x[events,] - fitd$means[indx,] > > > > cleanEx() > nameEx("diabetic") > ### * diabetic > > flush(stderr()); flush(stdout()) > > ### Name: diabetic > ### Title: Ddiabetic retinopathy > ### Aliases: diabetic > ### Keywords: datasets survival > > ### ** Examples > > # juvenile diabetes is defined as and age less than 20 > juvenile <- 1*(diabetic$age < 20) > coxph(Surv(time, status) ~ trt + juvenile, cluster= id, + data= diabetic) Call: coxph(formula = Surv(time, status) ~ trt + juvenile, data = diabetic, cluster = id) coef exp(coef) se(coef) robust se z p trt -0.77893 0.45890 0.16893 0.14851 -5.245 1.56e-07 juvenile -0.05388 0.94754 0.16211 0.17864 -0.302 0.763 Likelihood ratio test=22.48 on 2 df, p=1.312e-05 n= 394, number of events= 155 > > > > cleanEx() > nameEx("dsurvreg") > ### * dsurvreg > > flush(stderr()); flush(stdout()) > > ### Name: dsurvreg > ### Title: Distributions available in survreg. > ### Aliases: dsurvreg psurvreg qsurvreg rsurvreg > ### Keywords: distribution > > ### ** Examples > > # List of distributions available > names(survreg.distributions) [1] "extreme" "logistic" "gaussian" "weibull" "exponential" [6] "rayleigh" "loggaussian" "lognormal" "loglogistic" "t" > ## Not run: > ##D [1] "extreme" "logistic" "gaussian" "weibull" "exponential" > ##D [6] "rayleigh" "loggaussian" "lognormal" "loglogistic" "t" > ## End(Not run) > # Compare results > all.equal(dsurvreg(1:10, 2, 5, dist='lognormal'), dlnorm(1:10, 2, 5)) [1] TRUE > > # Hazard function for a Weibull distribution > x <- seq(.1, 3, length=30) > haz <- dsurvreg(x, 2, 3)/ (1-psurvreg(x, 2, 3)) > ## Not run: > ##D plot(x, haz, log='xy', ylab="Hazard") #line with slope (1/scale -1) > ## End(Not run) > > > > cleanEx() > nameEx("finegray") > ### * finegray > > flush(stderr()); flush(stdout()) > > ### Name: finegray > ### Title: Create data for a Fine-Gray model > ### Aliases: finegray > ### Keywords: survival > > ### ** Examples > > # Treat time to death and plasma cell malignancy as competing risks > etime <- with(mgus2, ifelse(pstat==0, futime, ptime)) > event <- with(mgus2, ifelse(pstat==0, 2*death, 1)) > event <- factor(event, 0:2, labels=c("censor", "pcm", "death")) > > # FG model for PCM > pdata <- finegray(Surv(etime, event) ~ ., data=mgus2) > fgfit <- coxph(Surv(fgstart, fgstop, fgstatus) ~ age + sex, + weight=fgwt, data=pdata) > > # Compute the weights separately by sex > adata <- finegray(Surv(etime, event) ~ . + strata(sex), + data=mgus2, na.action=na.pass) > > > > cleanEx() > nameEx("flchain") > ### * flchain > > flush(stderr()); flush(stdout()) > > ### Name: flchain > ### Title: Assay of serum free light chain for 7874 subjects. > ### Aliases: flchain > ### Keywords: datasets > > ### ** Examples > > data(flchain) > age.grp <- cut(flchain$age, c(49,54, 59,64, 69,74,79, 89, 110), + labels= paste(c(50,55,60,65,70,75,80,90), + c(54,59,64,69,74,79,89,109), sep='-')) > table(flchain$sex, age.grp) age.grp 50-54 55-59 60-64 65-69 70-74 75-79 80-89 90-109 F 881 766 625 589 541 408 459 81 M 796 714 591 524 405 269 202 23 > > > > cleanEx() > nameEx("frailty") > ### * frailty > > flush(stderr()); flush(stdout()) > > ### Name: frailty > ### Title: Random effects terms > ### Aliases: frailty frailty.gamma frailty.gaussian frailty.t > ### Keywords: survival > > ### ** Examples > > # Random institutional effect > coxph(Surv(time, status) ~ age + frailty(inst, df=4), lung) Call: coxph(formula = Surv(time, status) ~ age + frailty(inst, df = 4), data = lung) coef se(coef) se2 Chisq DF p age 0.01937 0.00933 0.00925 4.31149 1.00 0.038 frailty(inst, df = 4) 3.33459 3.99 0.501 Iterations: 3 outer, 10 Newton-Raphson Variance of random effect= 0.038 I-likelihood = -743.6 Degrees of freedom for terms= 1 4 Likelihood ratio test=9.96 on 4.97 df, p=0.08 n= 227, number of events= 164 (1 observation deleted due to missingness) > > # Litter effects for the rats data > rfit2a <- coxph(Surv(time, status) ~ rx + + frailty.gaussian(litter, df=13, sparse=FALSE), rats, + subset= (sex=='f')) > rfit2b <- coxph(Surv(time, status) ~ rx + + frailty.gaussian(litter, df=13, sparse=TRUE), rats, + subset= (sex=='f')) > > > > cleanEx() > nameEx("is.ratetable") > ### * is.ratetable > > flush(stderr()); flush(stdout()) > > ### Name: is.ratetable > ### Title: Verify that an object is of class ratetable. > ### Aliases: is.ratetable Math.ratetable Ops.ratetable > ### Keywords: survival > > ### ** Examples > > is.ratetable(survexp.us) # True [1] TRUE > is.ratetable(lung) # False [1] FALSE > > > > cleanEx() > nameEx("kidney") > ### * kidney > > flush(stderr()); flush(stdout()) > > ### Name: kidney > ### Title: Kidney catheter data > ### Aliases: kidney > ### Keywords: survival > > ### ** Examples > > kfit <- coxph(Surv(time, status)~ age + sex + disease + frailty(id), kidney) > kfit0 <- coxph(Surv(time, status)~ age + sex + disease, kidney) > kfitm1 <- coxph(Surv(time,status) ~ age + sex + disease + + frailty(id, dist='gauss'), kidney) > > > > cleanEx() > nameEx("levels.Surv") > ### * levels.Surv > > flush(stderr()); flush(stdout()) > > ### Name: levels.Surv > ### Title: Return the states of a multi-state Surv object > ### Aliases: levels.Surv > ### Keywords: survival > > ### ** Examples > > y1 <- Surv(c(1,5, 9, 17,21, 30), + factor(c(0, 1, 2,1,0,2), 0:2, c("censored", "progression", "death"))) > levels(y1) [1] "progression" "death" > > y2 <- Surv(1:6, rep(0:1, 3)) > y2 [1] 1+ 2 3+ 4 5+ 6 > levels(y2) NULL > > > > cleanEx() > nameEx("lines.survfit") > ### * lines.survfit > > flush(stderr()); flush(stdout()) > > ### Name: lines.survfit > ### Title: Add Lines or Points to a Survival Plot > ### Aliases: lines.survfit points.survfit lines.survexp > ### Keywords: survival > > ### ** Examples > > fit <- survfit(Surv(time, status==2) ~ sex, pbc,subset=1:312) > plot(fit, mark.time=FALSE, xscale=365.25, + xlab='Years', ylab='Survival') > lines(fit[1], lwd=2) #darken the first curve and add marks > > > # Add expected survival curves for the two groups, > # based on the US census data > # The data set does not have entry date, use the midpoint of the study > efit <- survexp(~sex, data=pbc, times= (0:24)*182, ratetable=survexp.us, + rmap=list(sex=sex, age=age*365.35, year=as.Date('1979/01/01'))) > temp <- lines(efit, lty=2, lwd=2:1) > text(temp, c("Male", "Female"), adj= -.1) #labels just past the ends > title(main="Primary Biliary Cirrhosis, Observed and Expected") > > > > > cleanEx() > nameEx("mgus") > ### * mgus > > flush(stderr()); flush(stdout()) > > ### Name: mgus > ### Title: Monoclonal gammopathy data > ### Aliases: mgus mgus1 > ### Keywords: datasets survival > > ### ** Examples > > # Create the competing risk curves for time to first of death or PCM > sfit <- survfit(Surv(start, stop, event) ~ sex, mgus1, id=id, + subset=(enum==1)) > print(sfit) # the order of printout is the order in which they plot Call: survfit(formula = Surv(start, stop, event) ~ sex, data = mgus1, subset = (enum == 1), id = id) n nevent rmean* sex=female, (s0) 104 0 5762.379 sex=male, (s0) 137 0 4543.293 sex=female, pcm 104 33 2881.500 sex=male, pcm 137 31 2478.026 sex=female, death 104 63 5681.121 sex=male, death 137 100 7303.682 *restricted mean time in state (max time = 14325 ) > > plot(sfit, xscale=365.25, lty=c(2,2,1,1), col=c(1,2,1,2), + xlab="Years after MGUS detection", ylab="Proportion") > legend(0, .8, c("Death/male", "Death/female", "PCM/male", "PCM/female"), + lty=c(1,1,2,2), col=c(2,1,2,1), bty='n') > > title("Curves for the first of plasma cell malignancy or death") > # The plot shows that males have a higher death rate than females (no > # surprise) but their rates of conversion to PCM are essentially the same. > > > > cleanEx() > nameEx("model.matrix.coxph") > ### * model.matrix.coxph > > flush(stderr()); flush(stdout()) > > ### Name: model.matrix.coxph > ### Title: Model.matrix method for coxph models > ### Aliases: model.matrix.coxph > ### Keywords: survival > > ### ** Examples > > fit1 <- coxph(Surv(time, status) ~ age + factor(ph.ecog), data=lung) > xfit <- model.matrix(fit1) > > fit2 <- coxph(Surv(time, status) ~ age + factor(ph.ecog), data=lung, + x=TRUE) > all.equal(model.matrix(fit1), fit2$x) [1] TRUE > > > > cleanEx() > nameEx("myeloid") > ### * myeloid > > flush(stderr()); flush(stdout()) > > ### Name: myeloid > ### Title: Acute myeloid leukemia > ### Aliases: myeloid > ### Keywords: datasets > > ### ** Examples > > coxph(Surv(futime, death) ~ trt, data=myeloid) Call: coxph(formula = Surv(futime, death) ~ trt, data = myeloid) coef exp(coef) se(coef) z p trtB -0.3457 0.7077 0.1122 -3.081 0.00206 Likelihood ratio test=9.52 on 1 df, p=0.002029 n= 646, number of events= 320 > # See the mstate vignette for a more complete analysis > > > > cleanEx() > nameEx("myeloma") > ### * myeloma > > flush(stderr()); flush(stdout()) > > ### Name: myeloma > ### Title: Survival times of patients with multiple myeloma > ### Aliases: myeloma > ### Keywords: datasets > > ### ** Examples > > # Incorrect survival curve, which ignores left truncation > fit1 <- survfit(Surv(futime, death) ~ 1, myeloma) > # Correct curve > fit2 <- survfit(Surv(entry, futime, death) ~1, myeloma) > > > > cleanEx() > nameEx("neardate") > ### * neardate > > flush(stderr()); flush(stdout()) > > ### Name: neardate > ### Title: Find the index of the closest value in data set 2, for each > ### entry in data set one. > ### Aliases: neardate > ### Keywords: manip utilities > > ### ** Examples > > data1 <- data.frame(id = 1:10, + entry.dt = as.Date(paste("2011", 1:10, "5", sep='-'))) > temp1 <- c(1,4,5,1,3,6,9, 2,7,8,12,4,6,7,10,12,3) > data2 <- data.frame(id = c(1,1,1,2,2,4,4,5,5,5,6,8,8,9,10,10,12), + lab.dt = as.Date(paste("2011", temp1, "1", sep='-')), + chol = round(runif(17, 130, 280))) > > #first cholesterol on or after enrollment > indx1 <- neardate(data1$id, data2$id, data1$entry.dt, data2$lab.dt) > data2[indx1, "chol"] [1] 186 160 NA 265 224 161 NA NA NA 205 > > # Closest one, either before or after. > # > indx2 <- neardate(data1$id, data2$id, data1$entry.dt, data2$lab.dt, + best="prior") > ifelse(is.na(indx1), indx2, # none after, take before + ifelse(is.na(indx2), indx1, #none before + ifelse(abs(data2$lab.dt[indx2]- data1$entry.dt) < + abs(data2$lab.dt[indx1]- data1$entry.dt), indx2, indx1))) [1] 1 5 NA 6 9 11 NA 13 14 15 > > # closest date before or after, but no more than 21 days prior to index > indx2 <- ifelse((data1$entry.dt - data2$lab.dt[indx2]) >21, NA, indx2) > ifelse(is.na(indx1), indx2, # none after, take before + ifelse(is.na(indx2), indx1, #none before + ifelse(abs(data2$lab.dt[indx2]- data1$entry.dt) < + abs(data2$lab.dt[indx1]- data1$entry.dt), indx2, indx1))) [1] 1 5 NA 6 9 11 NA NA NA 15 > > > > cleanEx() > nameEx("nsk") > ### * nsk > > flush(stderr()); flush(stdout()) > > ### Name: nsk > ### Title: Natural splines with knot heights as the basis. > ### Aliases: nsk > ### Keywords: smooth > > ### ** Examples > > # make some dummy data > tdata <- data.frame(x= lung$age, y = 10*log(lung$age-35) + rnorm(228, 0, 2)) > fit1 <- lm(y ~ -1 + nsk(x, df=4, intercept=TRUE) , data=tdata) > fit2 <- lm(y ~ nsk(x, df=3), data=tdata) > > # the knots (same for both fits) > knots <- unlist(attributes(fit1$model[[2]])[c('Boundary.knots', 'knots')]) > knots Boundary.knots.5% Boundary.knots.95% knots.33.33333% knots.66.66667% 45.35 75.00 59.00 67.00 > unname(coef(fit1)) # predictions at the knot points [1] 22.23565 32.18784 34.82994 36.93020 > > unname(coef(fit1)[-1] - coef(fit1)[1]) # differences: yhat[2:4] - yhat[1] [1] 9.952189 12.594290 14.694549 > unname(coef(fit2)) [1] 22.235650 9.952189 12.594290 14.694549 > > ## Not run: > ##D plot(y ~ x, data=tdata) > ##D points(sort(knots), coef(fit1), col=2, pch=19) > ##D coef(fit)[1] + c(0, coef(fit)[-1]) > ## End(Not run) > > > > cleanEx() > nameEx("nwtco") > ### * nwtco > > flush(stderr()); flush(stdout()) > > ### Name: nwtco > ### Title: Data from the National Wilm's Tumor Study > ### Aliases: nwtco > ### Keywords: datasets > > ### ** Examples > > with(nwtco, table(instit,histol)) histol instit 1 2 1 3493 129 2 76 330 > anova(coxph(Surv(edrel,rel)~histol+instit,data=nwtco)) Analysis of Deviance Table Cox model: response is Surv(edrel, rel) Terms added sequentially (first to last) loglik Chisq Df Pr(>|Chi|) NULL -4666.3 histol -4532.5 267.6667 1 < 2e-16 *** instit -4531.0 3.0397 1 0.08125 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(coxph(Surv(edrel,rel)~instit+histol,data=nwtco)) Analysis of Deviance Table Cox model: response is Surv(edrel, rel) Terms added sequentially (first to last) loglik Chisq Df Pr(>|Chi|) NULL -4666.3 instit -4577.5 177.714 1 < 2.2e-16 *** histol -4531.0 92.992 1 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > > > cleanEx() > nameEx("pbcseq") > ### * pbcseq > > flush(stderr()); flush(stdout()) > > ### Name: pbcseq > ### Title: Mayo Clinic Primary Biliary Cirrhosis, sequential data > ### Aliases: pbcseq > ### Keywords: datasets > > ### ** Examples > > # Create the start-stop-event triplet needed for coxph > first <- with(pbcseq, c(TRUE, diff(id) !=0)) #first id for each subject > last <- c(first[-1], TRUE) #last id > > time1 <- with(pbcseq, ifelse(first, 0, day)) > time2 <- with(pbcseq, ifelse(last, futime, c(day[-1], 0))) > event <- with(pbcseq, ifelse(last, status, 0)) > > fit1 <- coxph(Surv(time1, time2, event) ~ age + sex + log(bili), pbcseq) Warning in Surv(time1, time2, event) : Invalid status value, converted to NA > > > > cleanEx() > nameEx("plot.cox.zph") > ### * plot.cox.zph > > flush(stderr()); flush(stdout()) > > ### Name: plot.cox.zph > ### Title: Graphical Test of Proportional Hazards > ### Aliases: plot.cox.zph > ### Keywords: survival > > ### ** Examples > > vfit <- coxph(Surv(time,status) ~ trt + factor(celltype) + + karno + age, data=veteran, x=TRUE) > temp <- cox.zph(vfit) > plot(temp, var=3) # Look at Karnofsy score, old way of doing plot > plot(temp[3]) # New way with subscripting > abline(0, 0, lty=3) > # Add the linear fit as well > abline(lm(temp$y[,3] ~ temp$x)$coefficients, lty=4, col=3) > title(main="VA Lung Study") > > > > cleanEx() > nameEx("plot.survfit") > ### * plot.survfit > > flush(stderr()); flush(stdout()) > > ### Name: plot.survfit > ### Title: Plot method for 'survfit' objects > ### Aliases: plot.survfit > ### Keywords: survival hplot > > ### ** Examples > > leukemia.surv <- survfit(Surv(time, status) ~ x, data = aml) > plot(leukemia.surv, lty = 2:3) > legend(100, .9, c("Maintenance", "No Maintenance"), lty = 2:3) > title("Kaplan-Meier Curves\nfor AML Maintenance Study") > lsurv2 <- survfit(Surv(time, status) ~ x, aml, type='fleming') > plot(lsurv2, lty=2:3, fun="cumhaz", + xlab="Months", ylab="Cumulative Hazard") > > > > cleanEx() > nameEx("predict.coxph") > ### * predict.coxph > > flush(stderr()); flush(stdout()) > > ### Name: predict.coxph > ### Title: Predictions for a Cox model > ### Aliases: predict.coxph predict.coxph.penal > ### Keywords: survival > > ### ** Examples > > options(na.action=na.exclude) # retain NA in predictions > fit <- coxph(Surv(time, status) ~ age + ph.ecog + strata(inst), lung) > #lung data set has status coded as 1/2 > mresid <- (lung$status-1) - predict(fit, type='expected') #Martingale resid > predict(fit,type="lp") 1 2 3 4 5 6 0.215495605 -0.423532231 -0.559265038 0.183469551 -0.539432878 0.248095483 7 8 9 10 11 12 0.406461814 0.489169379 -0.047448917 0.327284344 0.040389888 0.550315552 13 14 15 16 17 18 -0.115925255 NA 0.055807340 0.110906025 0.050567124 0.493760215 19 20 21 22 23 24 0.557645717 -0.004245606 -0.127236322 -0.621260082 -0.319524466 -0.575882288 25 26 27 28 29 30 -0.345688084 0.202851214 -0.428371074 1.313400384 -0.021210624 0.761244928 31 32 33 34 35 36 0.191540147 0.749933860 0.180240469 0.459827013 0.672213041 0.625512121 37 38 39 40 41 42 0.565173220 0.085767683 0.761244928 0.076972823 0.330513426 0.511791514 43 44 45 46 47 48 -0.439682141 0.660901974 -0.164699618 0.496950353 -0.381077937 0.091073865 49 50 51 52 53 54 -0.354839644 -0.175654221 0.192873470 -0.447487689 -0.450985298 -0.562055013 55 56 57 58 59 60 0.063012023 -0.516810744 -0.297203343 0.474684682 0.034518529 0.076972823 61 62 63 64 65 66 0.678283893 -0.045992266 0.176731471 -0.149858457 0.158940268 0.718790633 67 68 69 70 71 72 0.539004484 -0.308514410 -0.543216443 0.153500561 -0.479261384 -0.078592144 73 74 75 76 77 78 0.946919127 -0.073531430 -0.049489875 0.214162281 -0.641232484 0.029078821 79 80 81 82 83 84 -0.276488357 -0.392389004 -0.439682141 0.001411510 -0.410013004 -0.151289480 85 86 87 88 89 90 -0.292311495 0.198744830 -0.039921414 -0.530162769 -0.123010230 0.738622793 91 92 93 94 95 96 -0.743642023 0.050567124 0.285269157 0.108857156 -0.437633273 0.796634781 97 98 99 100 101 102 0.158940268 0.214162281 -0.161169524 -0.400910096 -0.562055013 0.176122695 103 104 105 106 107 108 0.012722577 0.108256292 0.617817211 0.157606945 -0.189452466 0.110906025 109 110 111 112 113 114 -0.026867740 0.797968104 -0.411394980 -0.149248522 0.369011703 -0.344354760 115 116 117 118 119 120 0.006456686 0.783867062 0.503880355 0.693378524 0.527693417 0.244122624 121 122 123 124 125 126 -0.464038972 0.449575370 0.158940268 0.500480446 -0.426322206 0.005322855 127 128 129 130 131 132 -0.368298829 0.134984810 0.652115157 -0.617153698 0.131479291 -0.190511890 133 134 135 136 137 138 -0.643882217 0.001411510 -0.460255408 0.666972826 0.067118407 0.583884010 139 140 141 142 143 144 -0.036137850 -0.399002948 0.747892903 0.215495605 0.630552446 0.088283890 145 146 147 148 149 150 -0.240346995 -0.200763533 -0.558074111 -0.179200822 -0.232577411 -0.524505653 151 152 153 154 155 156 0.171077519 -0.633704981 -0.331136545 -0.190511890 0.477441161 NA 157 158 159 160 161 162 -0.031097524 0.736573925 0.123673743 -0.013515715 -0.585704233 -0.038186718 163 164 165 166 167 168 0.466547245 0.108256292 -0.209943887 -0.716429053 -0.206413793 -0.699828778 169 170 171 172 173 174 0.085634157 -0.424865554 0.069277914 -0.441093652 0.107445646 -0.874783994 175 176 177 178 179 180 -0.047448917 0.046655779 0.557645717 0.001411510 -0.047448917 -0.667994646 181 182 183 184 185 186 -0.513194586 -0.776965291 -0.614629447 0.019390401 -0.583220496 -0.651086900 187 188 189 190 191 192 0.859584155 -0.536642904 0.063145548 -0.712882451 0.024398388 0.369338475 193 194 195 196 197 198 -0.023370131 0.076972823 0.061878192 -0.368310218 -0.003231734 0.074931865 199 200 201 202 203 204 -0.629921417 -0.037164935 0.063145548 0.084500326 -0.574393166 -0.627131442 205 206 207 208 209 210 -0.658814293 0.302547317 -0.410314015 0.516017606 0.131487202 -0.302547317 211 212 213 214 215 216 -0.539432878 0.153500561 0.119700884 0.409991908 -0.149858457 -0.149858457 217 218 219 220 221 222 -0.156943432 0.781826105 0.477858312 -0.452404719 0.016633922 -0.081992053 223 224 225 226 227 228 0.212705630 0.224016697 -0.750726998 0.703662506 0.142189494 -0.085165683 > predict(fit,type="expected") 1 2 3 4 5 6 7 0.74602570 0.57892506 1.28411487 0.65144995 2.53474317 2.59935704 0.94925558 8 9 10 11 12 13 14 1.07812821 0.63137435 0.55866807 0.31809979 1.96068120 2.96879741 NA 15 16 17 18 19 20 21 2.14464916 0.39248100 1.01652225 2.53985878 0.23734050 0.15454932 0.41781121 22 23 24 25 26 27 28 0.03725873 1.07425239 0.73304358 0.71922541 1.96068538 0.91425760 0.50868712 29 30 31 32 33 34 35 1.07651355 0.10727131 1.64348011 0.22335391 1.34246079 0.18355514 0.25427967 36 37 38 39 40 41 42 0.57948554 3.87217595 1.42062915 0.50341133 2.84274107 1.90670187 0.39302876 43 44 45 46 47 48 49 1.67374788 0.56009982 1.95081502 0.39930277 0.62185372 1.18384892 1.08920268 50 51 52 53 54 55 56 1.36922169 2.72429090 0.31557423 0.04821232 0.41960993 3.07164840 0.12000994 57 58 59 60 61 62 63 0.07406041 0.17908976 1.74520134 1.10195998 1.47697029 0.54523697 0.51461493 64 65 66 67 68 69 70 0.14292300 0.18117365 0.20227027 0.70028855 1.00636733 0.31133532 0.64126839 71 72 73 74 75 76 77 0.96177399 0.46743320 0.53451717 0.16345589 0.86294287 1.44797843 1.06953116 78 79 80 81 82 83 84 1.19014609 0.03668315 0.33061179 1.90397464 0.08944145 0.20857044 0.28585781 85 86 87 88 89 90 91 1.15723874 0.87295638 1.19851949 0.14216346 1.37338069 0.92021616 1.05096221 92 93 94 95 96 97 98 0.27465006 0.47403241 0.26750987 1.01622540 0.08901343 0.32456045 0.93961618 99 100 101 102 103 104 105 0.85179714 0.14362313 0.89733451 1.74403467 0.70225748 0.15754565 0.36065915 106 107 108 109 110 111 112 0.41227011 0.29089093 0.02759911 2.54485283 1.57705739 0.02915789 0.51482474 113 114 115 116 117 118 119 1.51254632 0.24392791 1.95773713 0.16855572 0.69132758 2.65613080 1.04014324 120 121 122 123 124 125 126 0.89157179 0.40187641 0.23829273 1.56065440 0.17535194 1.02778525 0.18442460 127 128 129 130 131 132 133 0.08051722 0.20596405 1.70473379 0.86354367 0.72017118 0.27146814 0.48487446 134 135 136 137 138 139 140 1.10114414 0.51567846 1.46035831 0.93950468 1.54314328 1.12143879 0.60372302 141 142 143 144 145 146 147 1.46022571 0.88081136 0.66047105 0.18347489 0.51981101 0.28761918 0.50825077 148 149 150 151 152 153 154 0.15268490 0.06671446 0.32571666 0.39746179 0.39772440 0.38939509 0.20940447 155 156 157 158 159 160 161 0.62171971 NA 0.34080256 0.46159657 0.47539058 1.00662370 0.21472196 162 163 164 165 166 167 168 0.54619593 0.50111574 0.24481910 0.51248548 0.19954882 0.25566706 0.78817717 169 170 171 172 173 174 175 0.44798249 0.43113659 0.44847984 1.48341994 0.46620310 0.37028208 0.86812344 176 177 178 179 180 181 182 0.43844817 0.94494334 0.25935783 0.37625255 0.20649507 0.25048304 0.37569346 183 184 185 186 187 188 189 0.40334526 0.39324727 0.36799524 0.39552828 1.77501387 0.24422514 0.38021709 190 191 192 193 194 195 196 0.21501843 0.51818689 0.08032921 0.22774986 0.71502728 0.36774267 0.39500663 197 198 199 200 201 202 203 0.38445105 0.97727710 0.43520510 0.16869554 0.17219830 0.05878035 0.21716448 204 205 206 207 208 209 210 0.18384556 0.18192355 0.64682101 0.35975276 0.70106697 1.03414013 0.35317899 211 212 213 214 215 216 217 0.42921059 0.47944086 0.40234009 0.25017393 0.04470913 0.27054309 0.22137404 218 219 220 221 222 223 224 1.18698635 0.50681607 0.11190719 0.11327702 0.28954125 0.33611081 0.74776723 225 226 227 228 0.12225025 0.00000000 0.35218786 0.10231300 > predict(fit,type="risk",se.fit=TRUE) $fit 1 2 3 4 5 6 7 8 1.2404765 0.6547301 0.5716290 1.2013784 0.5830788 1.2815823 1.5014958 1.6309609 9 10 11 12 13 14 15 16 0.9536592 1.3871959 1.0412167 1.7338000 0.8905418 NA 1.0573939 1.1172899 17 18 19 20 21 22 23 24 1.0518675 1.6384656 1.7465558 0.9957634 0.8805256 0.5372670 0.7264944 0.5622086 25 26 27 28 29 30 31 32 0.7077332 1.2248902 0.6515696 3.7187976 0.9790127 2.1409399 1.2111135 2.1168600 33 34 35 36 37 38 39 40 1.1975053 1.5838000 1.9585669 1.8692030 1.7597526 1.0895532 2.1409399 1.0800127 41 42 43 44 45 46 47 48 1.3916825 1.6682773 0.6442412 1.9365383 0.8481484 1.6437009 0.6831246 1.0953499 49 50 51 52 53 54 55 56 0.7012859 0.8389080 1.2127293 0.6392321 0.6370002 0.5700364 1.0650396 0.5964197 57 58 59 60 61 62 63 64 0.7428929 1.6075072 1.0351212 1.0800127 1.9704933 0.9550493 1.1933106 0.8608298 65 66 67 68 69 70 71 72 1.1722679 2.0519501 1.7142994 0.7345374 0.5808769 1.1659084 0.6192406 0.9244169 73 74 75 76 77 78 79 80 2.5777557 0.9291069 0.9517148 1.2388237 0.5266429 1.0295057 0.7584424 0.6754413 81 82 83 84 85 86 87 88 0.6442412 1.0014125 0.6636416 0.8595988 0.7465360 1.2198707 0.9608649 0.5885092 89 90 91 92 93 94 95 96 0.8842546 2.0930510 0.4753794 1.0518675 1.3301200 1.1150031 0.6455625 2.2180641 97 98 99 100 101 102 103 104 1.1722679 1.2388237 0.8511478 0.6697103 0.5700364 1.1925844 1.0128039 1.1143333 105 106 107 108 109 110 111 112 1.8548748 1.1707060 0.8274120 1.1172899 0.9734900 2.2210235 0.6627251 0.8613550 113 114 115 116 117 118 119 120 1.4463045 0.7086775 1.0064776 2.1899245 1.6551313 2.0004627 1.6950181 1.2765009 121 122 123 124 125 126 127 128 0.6287391 1.5676464 1.1722679 1.6495136 0.6529059 1.0053370 0.6919104 1.1445194 129 130 131 132 133 134 135 136 1.9195968 0.5394778 1.1405143 0.8265359 0.5252493 1.0014125 0.6311224 1.9483304 137 138 139 140 141 142 143 144 1.0694221 1.7929889 0.9645073 0.6709887 2.1125440 1.2404765 1.8786481 1.0922982 145 146 147 148 149 150 151 152 0.7863550 0.8181059 0.5723102 0.8359380 0.7924884 0.5918479 1.1865827 0.5306222 153 154 155 156 157 158 159 160 0.7181071 0.8265359 1.6119444 NA 0.9693810 2.0887670 1.1316466 0.9865752 161 162 163 164 165 166 167 168 0.5567137 0.9625332 1.5944793 1.1143333 0.8106297 0.4884935 0.8134964 0.4966703 169 170 171 172 173 174 175 176 1.0894077 0.6538577 1.0717340 0.6433325 1.1134303 0.4169521 0.9536592 1.0477613 177 178 179 180 181 182 183 184 1.7465558 1.0014125 0.9536592 0.5127358 0.5985803 0.4597993 0.5408413 1.0195796 185 186 187 188 189 190 191 192 0.5580981 0.5214787 2.3621782 0.5847079 1.0651819 0.4902291 1.0246985 1.4467772 193 194 195 196 197 198 199 200 0.9769008 1.0800127 1.0638328 0.6919025 0.9967735 1.0778107 0.5326337 0.9635172 201 202 203 204 205 206 207 208 1.0651819 1.0881732 0.5630464 0.5341218 0.5174645 1.3533017 0.6634419 1.6753425 209 210 211 212 213 214 215 216 1.1405233 0.7389335 0.5830788 1.1659084 1.1271597 1.5068056 0.8608298 0.8608298 217 218 219 220 221 222 223 224 0.8547524 2.1854595 1.6126170 0.6360967 1.0167730 0.9212793 1.2370205 1.2510919 225 226 227 228 0.4720233 2.0211416 1.1527951 0.9183601 $se.fit 1 2 3 4 5 6 0.094027169 0.096340319 0.096185061 0.110144705 0.091221886 0.124003567 7 8 9 10 11 12 0.106470052 0.135893441 0.104263809 0.115204660 0.048057506 0.157626321 13 14 15 16 17 18 0.058398830 NA 0.078593550 0.044525715 0.047523899 0.139753275 19 20 21 22 23 24 0.246130195 0.051683778 0.050651208 0.106747848 0.121191090 0.095563151 25 26 27 28 29 30 0.135232494 0.077970827 0.084316589 0.541641696 0.047411370 0.244541270 31 32 33 34 35 36 0.067316853 0.236761412 0.222247496 0.143779967 0.246770836 0.214866749 37 38 39 40 41 42 0.186808694 0.027994134 0.244541270 0.017746688 0.094899948 0.150429986 43 44 45 46 47 48 0.082038635 0.251128992 0.071539989 0.172653479 0.157627962 0.046664065 49 50 51 52 53 54 0.203630081 0.147427688 0.071868116 0.087051165 0.126710133 0.091078334 55 56 57 58 59 60 0.030346404 0.094111921 0.072518580 0.232795318 0.092391388 0.017746688 61 62 63 64 65 66 0.207337260 0.162712161 0.126511646 0.038549743 0.042876315 0.234595146 67 68 69 70 71 72 0.151669341 0.068462840 0.112880428 0.068678027 0.124246473 0.184637680 73 74 75 76 77 78 0.325442016 0.174862073 0.090441588 0.089040153 0.108376599 0.057550307 79 80 81 82 83 84 0.188633743 0.150191651 0.082038635 0.027564795 0.181878087 0.172125872 85 86 87 88 89 90 0.142365056 0.114741553 0.035859182 0.096819023 0.132484179 0.229864932 91 92 93 94 95 96 0.120689668 0.047523899 0.070339929 0.055381362 0.123547581 0.253870138 97 98 99 100 101 102 0.042876315 0.089040153 0.035190905 0.106227011 0.091078334 0.091298269 103 104 105 106 107 108 0.017787711 0.028641480 0.194430169 0.039989624 0.075782969 0.044525715 109 110 111 112 113 114 0.071209628 0.254965259 0.163546509 0.185211877 0.241649528 0.139074790 115 116 117 118 119 120 0.076796420 0.262556790 0.348185429 0.211911041 0.146845572 0.149423594 121 122 123 124 125 126 0.150969692 0.156065943 0.042876315 0.142648758 0.129688202 0.004890619 127 128 129 130 131 132 0.113985419 0.031310085 0.248637733 0.121183075 0.041502912 0.067248608 133 134 135 136 137 138 0.115359144 0.027564795 0.112511267 0.200585657 0.069255092 0.201817172 139 140 141 142 143 144 0.094786456 0.075667327 0.240338975 0.094027169 0.216098624 0.024974398 145 146 147 148 149 150 0.066191299 0.084423319 0.167625233 0.058808327 0.221289168 0.105873833 151 152 153 154 155 156 0.140449741 0.098993713 0.063583542 0.067248608 0.230942129 NA 157 158 159 160 161 162 0.067558237 0.245408761 0.032338223 0.075589234 0.101745759 0.174851413 163 164 165 166 167 168 0.125897325 0.028641480 0.048065722 0.111659253 0.045260623 0.125085448 169 170 171 172 173 174 0.020095538 0.093808006 0.037378627 0.093118562 0.031761359 0.135544076 175 176 177 178 179 180 0.104263809 0.016586035 0.246130195 0.027564795 0.104263809 0.174088607 181 182 183 184 185 186 0.109727836 0.166211707 0.139230772 0.017941579 0.106388490 0.137198131 187 188 189 190 191 192 0.304795981 0.089505183 0.043311645 0.114439474 0.131445121 0.192173147 193 194 195 196 197 198 0.144436340 0.017746688 0.058484070 0.121193159 0.002956631 0.025613128 199 200 201 202 203 204 0.104623286 0.033429233 0.043311645 0.080773833 0.103942128 0.124008736 205 206 207 208 209 210 0.118294076 0.078206752 0.080505144 0.235804861 0.079727031 0.057789591 211 212 213 214 215 216 0.091221886 0.068678027 0.029421496 0.124248857 0.038549743 0.038549743 217 218 219 220 221 222 0.158976598 0.269332667 0.130275218 0.089792820 0.015369862 0.085131550 223 224 225 226 227 228 0.148494109 0.160862263 0.138362860 0.225740927 0.057778343 0.074788433 > predict(fit,type="terms",se.fit=TRUE) $fit age ph.ecog 1 0.130878057 0.03032716 2 0.063011653 -0.54083428 3 -0.072721154 -0.54083428 4 -0.061410086 0.03032716 5 -0.027476885 -0.54083428 6 0.130878057 0.03032716 7 0.063011653 0.60148859 8 0.096944855 0.60148859 9 -0.106654355 0.03032716 10 -0.016165817 0.60148859 11 -0.061410086 0.03032716 12 0.063011653 0.60148859 13 0.063011653 0.03032716 14 NA NA 15 -0.061410086 0.03032716 16 0.051700586 0.03032716 17 0.085633788 0.03032716 18 0.006456317 0.60148859 19 -0.072721154 0.60148859 20 -0.061410086 0.03032716 21 0.051700586 0.03032716 22 -0.151898625 -0.54083428 23 -0.140587557 0.03032716 24 -0.050099019 -0.54083428 25 0.108255923 -0.54083428 26 0.085633788 0.03032716 27 -0.027476885 -0.54083428 28 0.085633788 1.17265002 29 -0.106654355 0.03032716 30 0.130878057 0.60148859 31 0.074322721 0.03032716 32 0.119566990 0.60148859 33 -0.163209692 0.60148859 34 -0.027476885 0.60148859 35 -0.016165817 0.60148859 36 -0.004854750 0.60148859 37 0.029078452 0.60148859 38 0.040389519 0.03032716 39 0.130878057 0.60148859 40 0.017767384 0.03032716 41 0.085633788 0.03032716 42 0.119566990 0.60148859 43 -0.038787952 -0.54083428 44 -0.027476885 0.60148859 45 0.063011653 0.03032716 46 0.153500192 0.60148859 47 0.130878057 -0.54083428 48 0.006456317 0.03032716 49 0.130878057 -0.54083428 50 -0.140587557 0.03032716 51 0.108255923 0.03032716 52 0.006456317 -0.54083428 53 0.063011653 -0.54083428 54 -0.050099019 -0.54083428 55 -0.038787952 0.03032716 56 -0.004854750 -0.54083428 57 0.029078452 -0.54083428 58 -0.061410086 0.60148859 59 -0.050099019 0.03032716 60 0.017767384 0.03032716 61 0.142189124 0.60148859 62 -0.163209692 0.03032716 63 0.119566990 0.03032716 64 0.029078452 0.03032716 65 0.074322721 0.03032716 66 0.063011653 0.60148859 67 0.051700586 0.60148859 68 0.017767384 -0.54083428 69 0.063011653 -0.54083428 70 0.051700586 0.03032716 71 0.006456317 -0.54083428 72 -0.163209692 0.03032716 73 0.130878057 0.60148859 74 -0.253698230 0.03032716 75 -0.106654355 0.03032716 76 0.096944855 0.03032716 77 -0.129276490 -0.54083428 78 -0.072721154 0.03032716 79 0.210055528 -0.54083428 80 0.119566990 -0.54083428 81 -0.038787952 -0.54083428 82 -0.084032221 0.03032716 83 -0.231076095 0.03032716 84 -0.208453961 0.03032716 85 -0.208453961 0.03032716 86 0.096944855 0.03032716 87 -0.004854750 0.03032716 88 -0.016165817 -0.54083428 89 -0.208453961 0.03032716 90 0.108255923 0.60148859 91 0.006456317 -0.54083428 92 0.085633788 0.03032716 93 0.040389519 0.03032716 94 -0.061410086 0.03032716 95 0.074322721 -0.54083428 96 0.108255923 0.60148859 97 0.074322721 0.03032716 98 0.096944855 0.03032716 99 0.017767384 0.03032716 100 0.085633788 -0.54083428 101 -0.050099019 -0.54083428 102 0.074322721 0.03032716 103 -0.072721154 0.03032716 104 0.006456317 0.03032716 105 -0.038787952 0.60148859 106 0.040389519 0.03032716 107 -0.095343288 0.03032716 108 0.051700586 0.03032716 109 -0.084032221 0.03032716 110 0.142189124 0.60148859 111 0.074322721 -0.54083428 112 -0.208453961 0.03032716 113 0.198744461 0.03032716 114 0.142189124 -0.54083428 115 -0.095343288 0.03032716 116 0.153500192 0.60148859 117 -0.151898625 0.60148859 118 0.063011653 0.60148859 119 0.040389519 0.60148859 120 0.198744461 0.03032716 121 0.142189124 -0.54083428 122 -0.027476885 0.60148859 123 0.074322721 0.03032716 124 0.108255923 0.60148859 125 0.085633788 -0.54083428 126 0.040389519 0.03032716 127 -0.140587557 0.03032716 128 0.017767384 0.03032716 129 0.164811259 0.60148859 130 -0.163209692 -0.54083428 131 -0.038787952 0.03032716 132 -0.106654355 0.03032716 133 -0.174520759 -0.54083428 134 -0.084032221 0.03032716 135 0.051700586 -0.54083428 136 0.130878057 0.60148859 137 -0.050099019 0.03032716 138 -0.072721154 0.60148859 139 -0.095343288 0.03032716 140 -0.072721154 -0.54083428 141 0.119566990 0.60148859 142 0.130878057 0.03032716 143 0.153500192 0.60148859 144 0.029078452 0.03032716 145 -0.061410086 0.03032716 146 -0.106654355 0.03032716 147 0.096944855 -0.54083428 148 -0.095343288 0.03032716 149 0.221366595 -0.54083428 150 -0.038787952 -0.54083428 151 0.085633788 0.03032716 152 -0.027476885 -0.54083428 153 -0.004854750 -0.54083428 154 -0.106654355 0.03032716 155 -0.084032221 0.60148859 156 NA NA 157 0.063011653 0.03032716 158 -0.004854750 0.60148859 159 0.006456317 0.03032716 160 -0.072721154 0.03032716 161 -0.004854750 -0.54083428 162 -0.208453961 0.03032716 163 0.074322721 0.60148859 164 0.006456317 0.03032716 165 0.017767384 0.03032716 166 -0.061410086 -0.54083428 167 -0.027476885 0.03032716 168 -0.185831826 -0.54083428 169 -0.016165817 0.03032716 170 0.029078452 -0.54083428 171 -0.016165817 0.03032716 172 -0.050099019 -0.54083428 173 -0.072721154 0.03032716 174 -0.219765028 -0.54083428 175 -0.106654355 0.03032716 176 -0.038787952 0.03032716 177 -0.072721154 0.60148859 178 -0.084032221 0.03032716 179 -0.106654355 0.03032716 180 0.130878057 -0.54083428 181 -0.027476885 -0.54083428 182 -0.265009297 -0.54083428 183 0.040389519 -0.54083428 184 0.029078452 0.03032716 185 -0.129276490 -0.54083428 186 -0.197142894 -0.54083428 187 0.108255923 0.60148859 188 -0.050099019 -0.54083428 189 0.017767384 0.03032716 190 -0.106654355 -0.54083428 191 0.108255923 0.03032716 192 -0.117965423 0.60148859 193 -0.140587557 0.03032716 194 0.017767384 0.03032716 195 0.096944855 0.03032716 196 0.085633788 -0.54083428 197 0.006456317 0.03032716 198 0.017767384 0.03032716 199 -0.117965423 -0.54083428 200 -0.027476885 0.03032716 201 0.017767384 0.03032716 202 0.119566990 0.03032716 203 0.006456317 -0.54083428 204 -0.140587557 -0.54083428 205 0.006456317 -0.54083428 206 -0.004854750 0.60148859 207 -0.084032221 -0.54083428 208 -0.140587557 0.60148859 209 0.074322721 0.03032716 210 -0.038787952 0.03032716 211 -0.027476885 -0.54083428 212 0.051700586 0.03032716 213 0.074322721 0.03032716 214 0.017767384 0.60148859 215 0.029078452 0.03032716 216 0.029078452 0.03032716 217 -0.242387163 0.03032716 218 0.153500192 0.60148859 219 0.085633788 0.60148859 220 -0.061410086 -0.54083428 221 0.051700586 0.03032716 222 0.096944855 0.03032716 223 0.153500192 0.03032716 224 0.164811259 0.03032716 225 -0.265009297 -0.54083428 226 0.142189124 0.60148859 227 0.040389519 0.03032716 228 -0.050099019 0.03032716 $se.fit age ph.ecog 1 0.119930635 0.007395102 2 0.057740983 0.131879319 3 0.066638322 0.131879319 4 0.056273380 0.007395102 5 0.025178554 0.131879319 6 0.119930635 0.007395102 7 0.057740983 0.146669523 8 0.088835809 0.146669523 9 0.097733148 0.007395102 10 0.014813612 0.146669523 11 0.056273380 0.007395102 12 0.057740983 0.146669523 13 0.057740983 0.007395102 14 NA NA 15 0.056273380 0.007395102 16 0.047376041 0.007395102 17 0.078470867 0.007395102 18 0.005916272 0.146669523 19 0.066638322 0.146669523 20 0.056273380 0.007395102 21 0.047376041 0.007395102 22 0.139192917 0.131879319 23 0.128827975 0.007395102 24 0.045908438 0.131879319 25 0.099200751 0.131879319 26 0.078470867 0.007395102 27 0.025178554 0.131879319 28 0.078470867 0.285943945 29 0.097733148 0.007395102 30 0.119930635 0.146669523 31 0.068105925 0.007395102 32 0.109565693 0.146669523 33 0.149557859 0.146669523 34 0.025178554 0.146669523 35 0.014813612 0.146669523 36 0.004448670 0.146669523 37 0.026646156 0.146669523 38 0.037011098 0.007395102 39 0.119930635 0.146669523 40 0.016281214 0.007395102 41 0.078470867 0.007395102 42 0.109565693 0.146669523 43 0.035543496 0.131879319 44 0.025178554 0.146669523 45 0.057740983 0.007395102 46 0.140660519 0.146669523 47 0.119930635 0.131879319 48 0.005916272 0.007395102 49 0.119930635 0.131879319 50 0.128827975 0.007395102 51 0.099200751 0.007395102 52 0.005916272 0.131879319 53 0.057740983 0.131879319 54 0.045908438 0.131879319 55 0.035543496 0.007395102 56 0.004448670 0.131879319 57 0.026646156 0.131879319 58 0.056273380 0.146669523 59 0.045908438 0.007395102 60 0.016281214 0.007395102 61 0.130295577 0.146669523 62 0.149557859 0.007395102 63 0.109565693 0.007395102 64 0.026646156 0.007395102 65 0.068105925 0.007395102 66 0.057740983 0.146669523 67 0.047376041 0.146669523 68 0.016281214 0.131879319 69 0.057740983 0.131879319 70 0.047376041 0.007395102 71 0.005916272 0.131879319 72 0.149557859 0.007395102 73 0.119930635 0.146669523 74 0.232477395 0.007395102 75 0.097733148 0.007395102 76 0.088835809 0.007395102 77 0.118463033 0.131879319 78 0.066638322 0.007395102 79 0.192485229 0.131879319 80 0.109565693 0.131879319 81 0.035543496 0.131879319 82 0.077003264 0.007395102 83 0.211747511 0.007395102 84 0.191017627 0.007395102 85 0.191017627 0.007395102 86 0.088835809 0.007395102 87 0.004448670 0.007395102 88 0.014813612 0.131879319 89 0.191017627 0.007395102 90 0.099200751 0.146669523 91 0.005916272 0.131879319 92 0.078470867 0.007395102 93 0.037011098 0.007395102 94 0.056273380 0.007395102 95 0.068105925 0.131879319 96 0.099200751 0.146669523 97 0.068105925 0.007395102 98 0.088835809 0.007395102 99 0.016281214 0.007395102 100 0.078470867 0.131879319 101 0.045908438 0.131879319 102 0.068105925 0.007395102 103 0.066638322 0.007395102 104 0.005916272 0.007395102 105 0.035543496 0.146669523 106 0.037011098 0.007395102 107 0.087368206 0.007395102 108 0.047376041 0.007395102 109 0.077003264 0.007395102 110 0.130295577 0.146669523 111 0.068105925 0.131879319 112 0.191017627 0.007395102 113 0.182120287 0.007395102 114 0.130295577 0.131879319 115 0.087368206 0.007395102 116 0.140660519 0.146669523 117 0.139192917 0.146669523 118 0.057740983 0.146669523 119 0.037011098 0.146669523 120 0.182120287 0.007395102 121 0.130295577 0.131879319 122 0.025178554 0.146669523 123 0.068105925 0.007395102 124 0.099200751 0.146669523 125 0.078470867 0.131879319 126 0.037011098 0.007395102 127 0.128827975 0.007395102 128 0.016281214 0.007395102 129 0.151025461 0.146669523 130 0.149557859 0.131879319 131 0.035543496 0.007395102 132 0.097733148 0.007395102 133 0.159922801 0.131879319 134 0.077003264 0.007395102 135 0.047376041 0.131879319 136 0.119930635 0.146669523 137 0.045908438 0.007395102 138 0.066638322 0.146669523 139 0.087368206 0.007395102 140 0.066638322 0.131879319 141 0.109565693 0.146669523 142 0.119930635 0.007395102 143 0.140660519 0.146669523 144 0.026646156 0.007395102 145 0.056273380 0.007395102 146 0.097733148 0.007395102 147 0.088835809 0.131879319 148 0.087368206 0.007395102 149 0.202850171 0.131879319 150 0.035543496 0.131879319 151 0.078470867 0.007395102 152 0.025178554 0.131879319 153 0.004448670 0.131879319 154 0.097733148 0.007395102 155 0.077003264 0.146669523 156 NA NA 157 0.057740983 0.007395102 158 0.004448670 0.146669523 159 0.005916272 0.007395102 160 0.066638322 0.007395102 161 0.004448670 0.131879319 162 0.191017627 0.007395102 163 0.068105925 0.146669523 164 0.005916272 0.007395102 165 0.016281214 0.007395102 166 0.056273380 0.131879319 167 0.025178554 0.007395102 168 0.170287743 0.131879319 169 0.014813612 0.007395102 170 0.026646156 0.131879319 171 0.014813612 0.007395102 172 0.045908438 0.131879319 173 0.066638322 0.007395102 174 0.201382569 0.131879319 175 0.097733148 0.007395102 176 0.035543496 0.007395102 177 0.066638322 0.146669523 178 0.077003264 0.007395102 179 0.097733148 0.007395102 180 0.119930635 0.131879319 181 0.025178554 0.131879319 182 0.242842337 0.131879319 183 0.037011098 0.131879319 184 0.026646156 0.007395102 185 0.118463033 0.131879319 186 0.180652685 0.131879319 187 0.099200751 0.146669523 188 0.045908438 0.131879319 189 0.016281214 0.007395102 190 0.097733148 0.131879319 191 0.099200751 0.007395102 192 0.108098090 0.146669523 193 0.128827975 0.007395102 194 0.016281214 0.007395102 195 0.088835809 0.007395102 196 0.078470867 0.131879319 197 0.005916272 0.007395102 198 0.016281214 0.007395102 199 0.108098090 0.131879319 200 0.025178554 0.007395102 201 0.016281214 0.007395102 202 0.109565693 0.007395102 203 0.005916272 0.131879319 204 0.128827975 0.131879319 205 0.005916272 0.131879319 206 0.004448670 0.146669523 207 0.077003264 0.131879319 208 0.128827975 0.146669523 209 0.068105925 0.007395102 210 0.035543496 0.007395102 211 0.025178554 0.131879319 212 0.047376041 0.007395102 213 0.068105925 0.007395102 214 0.016281214 0.146669523 215 0.026646156 0.007395102 216 0.026646156 0.007395102 217 0.222112453 0.007395102 218 0.140660519 0.146669523 219 0.078470867 0.146669523 220 0.056273380 0.131879319 221 0.047376041 0.007395102 222 0.088835809 0.007395102 223 0.140660519 0.007395102 224 0.151025461 0.007395102 225 0.242842337 0.131879319 226 0.130295577 0.146669523 227 0.037011098 0.007395102 228 0.045908438 0.007395102 > > # For someone who demands reference='zero' > pzero <- function(fit) + predict(fit, reference="sample") + sum(coef(fit) * fit$means, na.rm=TRUE) > > > > cleanEx() > nameEx("predict.survreg") > ### * predict.survreg > > flush(stderr()); flush(stdout()) > > ### Name: predict.survreg > ### Title: Predicted Values for a 'survreg' Object > ### Aliases: predict.survreg predict.survreg.penal > ### Keywords: survival > > ### ** Examples > > # Draw figure 1 from Escobar and Meeker, 1992. > fit <- survreg(Surv(time,status) ~ age + I(age^2), data=stanford2, + dist='lognormal') > with(stanford2, plot(age, time, xlab='Age', ylab='Days', + xlim=c(0,65), ylim=c(.1, 10^5), log='y', type='n')) > with(stanford2, points(age, time, pch=c(2,4)[status+1], cex=.7)) > pred <- predict(fit, newdata=list(age=1:65), type='quantile', + p=c(.1, .5, .9)) > matlines(1:65, pred, lty=c(2,1,2), col=1) > > # Predicted Weibull survival curve for a lung cancer subject with > # ECOG score of 2 > lfit <- survreg(Surv(time, status) ~ ph.ecog, data=lung) > pct <- 1:98/100 # The 100th percentile of predicted survival is at +infinity > ptime <- predict(lfit, newdata=data.frame(ph.ecog=2), type='quantile', + p=pct, se=TRUE) > matplot(cbind(ptime$fit, ptime$fit + 2*ptime$se.fit, + ptime$fit - 2*ptime$se.fit)/30.5, 1-pct, + xlab="Months", ylab="Survival", type='l', lty=c(1,2,2), col=1) > > > > cleanEx() > nameEx("pseudo") > ### * pseudo > > flush(stderr()); flush(stdout()) > > ### Name: pseudo > ### Title: Pseudo values for survival. > ### Aliases: pseudo > ### Keywords: survival > > ### ** Examples > > fit1 <- survfit(Surv(time, status) ~ 1, data=lung) > yhat <- pseudo(fit1, times=c(365, 730)) > dim(yhat) [1] 228 2 > lfit <- lm(yhat[,1] ~ ph.ecog + age + sex, data=lung) > > > > cleanEx() > nameEx("pspline") > ### * pspline > > flush(stderr()); flush(stdout()) > > ### Name: pspline > ### Title: Smoothing splines using a pspline basis > ### Aliases: pspline psplineinverse > ### Keywords: survival > > ### ** Examples > > lfit6 <- survreg(Surv(time, status)~pspline(age, df=2), lung) > plot(lung$age, predict(lfit6), xlab='Age', ylab="Spline prediction") > title("Cancer Data") > fit0 <- coxph(Surv(time, status) ~ ph.ecog + age, lung) > fit1 <- coxph(Surv(time, status) ~ ph.ecog + pspline(age,3), lung) > fit3 <- coxph(Surv(time, status) ~ ph.ecog + pspline(age,8), lung) > fit0 Call: coxph(formula = Surv(time, status) ~ ph.ecog + age, data = lung) coef exp(coef) se(coef) z p ph.ecog 0.443485 1.558128 0.115831 3.829 0.000129 age 0.011281 1.011345 0.009319 1.211 0.226082 Likelihood ratio test=19.06 on 2 df, p=7.279e-05 n= 227, number of events= 164 (1 observation deleted due to missingness) > fit1 Call: coxph(formula = Surv(time, status) ~ ph.ecog + pspline(age, 3), data = lung) coef se(coef) se2 Chisq DF p ph.ecog 0.44802 0.11707 0.11678 14.64453 1.00 0.00013 pspline(age, 3), linear 0.01126 0.00928 0.00928 1.47231 1.00 0.22498 pspline(age, 3), nonlin 2.07924 2.08 0.37143 Iterations: 4 outer, 12 Newton-Raphson Theta= 0.861 Degrees of freedom for terms= 1.0 3.1 Likelihood ratio test=21.9 on 4.08 df, p=2e-04 n= 227, number of events= 164 (1 observation deleted due to missingness) > fit3 Call: coxph(formula = Surv(time, status) ~ ph.ecog + pspline(age, 8), data = lung) coef se(coef) se2 Chisq DF p ph.ecog 0.47640 0.12024 0.11925 15.69732 1.00 7.4e-05 pspline(age, 8), linear 0.01172 0.00923 0.00923 1.61161 1.00 0.20 pspline(age, 8), nonlin 6.93188 6.99 0.43 Iterations: 5 outer, 15 Newton-Raphson Theta= 0.691 Degrees of freedom for terms= 1 8 Likelihood ratio test=27.6 on 8.97 df, p=0.001 n= 227, number of events= 164 (1 observation deleted due to missingness) > > > > cleanEx() > nameEx("pyears") > ### * pyears > > flush(stderr()); flush(stdout()) > > ### Name: pyears > ### Title: Person Years > ### Aliases: pyears > ### Keywords: survival > > ### ** Examples > > # Look at progression rates jointly by calendar date and age > # > temp.yr <- tcut(mgus$dxyr, 55:92, labels=as.character(55:91)) > temp.age <- tcut(mgus$age, 34:101, labels=as.character(34:100)) > ptime <- ifelse(is.na(mgus$pctime), mgus$futime, mgus$pctime) > pstat <- ifelse(is.na(mgus$pctime), 0, 1) > pfit <- pyears(Surv(ptime/365.25, pstat) ~ temp.yr + temp.age + sex, mgus, + data.frame=TRUE) > # Turn the factor back into numerics for regression > tdata <- pfit$data > tdata$age <- as.numeric(as.character(tdata$temp.age)) > tdata$year<- as.numeric(as.character(tdata$temp.yr)) > fit1 <- glm(event ~ year + age+ sex +offset(log(pyears)), + data=tdata, family=poisson) > ## Not run: > ##D # fit a gam model > ##D gfit.m <- gam(y ~ s(age) + s(year) + offset(log(time)), > ##D family = poisson, data = tdata) > ## End(Not run) > > # Example #2 Create the hearta data frame: > hearta <- by(heart, heart$id, + function(x)x[x$stop == max(x$stop),]) > hearta <- do.call("rbind", hearta) > # Produce pyears table of death rates on the surgical arm > # The first is by age at randomization, the second by current age > fit1 <- pyears(Surv(stop/365.25, event) ~ cut(age + 48, c(0,50,60,70,100)) + + surgery, data = hearta, scale = 1) > fit2 <- pyears(Surv(stop/365.25, event) ~ tcut(age + 48, c(0,50,60,70,100)) + + surgery, data = hearta, scale = 1) > fit1$event/fit1$pyears #death rates on the surgery and non-surg arm surgery cut(age + 48, c(0, 50, 60, 70, 100)) 0 1 (0,50] 0.7615378 0.3036881 (50,60] 2.0068681 0.9979508 (60,70] 5.1083916 NaN (70,100] NaN NaN > > fit2$event/fit2$pyears #death rates on the surgery and non-surg arm surgery tcut(age + 48, c(0, 50, 60, 70, 100)) 0 1 0+ thru 50 0.8013285 0.2636994 50+ thru 60 1.6119238 0.6564817 60+ thru 70 3.9701087 NaN 70+ thru 100 NaN NaN > > > > cleanEx() > nameEx("quantile.survfit") > ### * quantile.survfit > > flush(stderr()); flush(stdout()) > > ### Name: quantile.survfit > ### Title: Quantiles from a survfit object > ### Aliases: quantile.survfit quantile.survfitms > ### Keywords: survival > > ### ** Examples > > fit <- survfit(Surv(time, status) ~ ph.ecog, data=lung) > quantile(fit) $quantile 25 50 75 ph.ecog=0 285 394 655 ph.ecog=1 181 306 550 ph.ecog=2 105 199 351 ph.ecog=3 118 118 118 $lower 25 50 75 ph.ecog=0 189 348 558 ph.ecog=1 156 268 460 ph.ecog=2 61 156 285 ph.ecog=3 NA NA NA $upper 25 50 75 ph.ecog=0 350 574 NA ph.ecog=1 223 429 689 ph.ecog=2 163 288 654 ph.ecog=3 NA NA NA > > cfit <- coxph(Surv(time, status) ~ age + strata(ph.ecog), data=lung) > csurv<- survfit(cfit, newdata=data.frame(age=c(40, 60, 80)), + conf.type ="none") > temp <- quantile(csurv, 1:5/10) > temp[2,3,] # quantiles for second level of ph.ecog, age=80 10 20 30 40 50 92 144 181 218 270 > quantile(csurv[2,3], 1:5/10) # quantiles of a single curve, same result 10 20 30 40 50 92 144 181 218 270 > > > > cleanEx() > nameEx("reliability") > ### * reliability > > flush(stderr()); flush(stdout()) > > ### Name: reliability > ### Title: Reliability data sets > ### Aliases: reliability capacitor cracks genfan ifluid imotor turbine > ### valveSeat > ### Keywords: datasets > > ### ** Examples > > survreg(Surv(time, status) ~ temperature + voltage, capacitor) Call: survreg(formula = Surv(time, status) ~ temperature + voltage, data = capacitor) Coefficients: (Intercept) temperature voltage 13.40701688 -0.02890466 -0.00591082 Scale= 0.3638092 Loglik(model)= -244.2 Loglik(intercept only)= -254.5 Chisq= 20.57 on 2 degrees of freedom, p= 3.41e-05 n= 64 > > > > cleanEx() > nameEx("residuals.coxph") > ### * residuals.coxph > > flush(stderr()); flush(stdout()) > > ### Name: residuals.coxph > ### Title: Calculate Residuals for a 'coxph' Fit > ### Aliases: residuals.coxph.penal residuals.coxph.null residuals.coxph > ### residuals.coxphms > ### Keywords: survival > > ### ** Examples > > > fit <- coxph(Surv(start, stop, event) ~ (age + surgery)* transplant, + data=heart) > mresid <- resid(fit, collapse=heart$id) > > > > cleanEx() > nameEx("residuals.survfit") > ### * residuals.survfit > > flush(stderr()); flush(stdout()) > > ### Name: residuals.survfit > ### Title: IJ residuals from a survfit object. > ### Aliases: residuals.survfit > > ### ** Examples > > fit <- survfit(Surv(time, status) ~ x, aml) > resid(fit, times=c(24, 48), type="RMTS") [,1] [,2] [1,] -1.0836777 -2.076652893 [2,] -0.7200413 -1.713016529 [3,] 0.2004132 0.421074380 [4,] -0.3237345 -1.468414256 [5,] 0.1876291 -0.957050620 [6,] 0.2899019 0.965676653 [7,] 0.2899019 -0.359777893 [8,] 0.2899019 0.008403926 [9,] 0.2899019 1.726585744 [10,] 0.2899019 1.726585744 [11,] 0.2899019 1.726585744 [12,] -1.0057870 -1.475694444 [13,] -1.0057870 -1.475694444 [14,] -0.7557870 -1.225694444 [15,] -0.7557870 -1.225694444 [16,] -0.4224537 -0.892361111 [17,] 0.5636574 0.899305556 [18,] 0.4826389 -0.121527778 [19,] 0.5798611 0.267361111 [20,] 0.5798611 0.559027778 [21,] 0.5798611 0.850694444 [22,] 0.5798611 1.822916667 [23,] 0.5798611 2.017361111 > > > > cleanEx() > nameEx("residuals.survreg") > ### * residuals.survreg > > flush(stderr()); flush(stdout()) > > ### Name: residuals.survreg > ### Title: Compute Residuals for 'survreg' Objects > ### Aliases: residuals.survreg residuals.survreg.penal > ### Keywords: survival > > ### ** Examples > > fit <- survreg(Surv(futime, death) ~ age + sex, mgus2) > summary(fit) # age and sex are both important Call: survreg(formula = Surv(futime, death) ~ age + sex, data = mgus2) Value Std. Error z p (Intercept) 8.85979 0.23842 37.16 < 2e-16 age -0.05360 0.00312 -17.19 < 2e-16 sexM -0.31874 0.06357 -5.01 5.3e-07 Log(scale) -0.02840 0.02787 -1.02 0.31 Scale= 0.972 Weibull distribution Loglik(model)= -5528.3 Loglik(intercept only)= -5699 Chisq= 341.42 on 2 degrees of freedom, p= 7.3e-75 Number of Newton-Raphson Iterations: 5 n= 1384 > > rr <- residuals(fit, type='matrix') > sum(rr[,1]) - with(mgus2, sum(log(futime[death==1]))) # loglik [1] -5528.267 > > plot(mgus2$age, rr[,2], col= (1+mgus2$death)) # ldresp > > > > cleanEx() > nameEx("retinopathy") > ### * retinopathy > > flush(stderr()); flush(stdout()) > > ### Name: retinopathy > ### Title: Diabetic Retinopathy > ### Aliases: retinopathy > ### Keywords: datasets > > ### ** Examples > > coxph(Surv(futime, status) ~ type + trt, cluster= id, retinopathy) Call: coxph(formula = Surv(futime, status) ~ type + trt, data = retinopathy, cluster = id) coef exp(coef) se(coef) robust se z p typeadult 0.05388 1.05536 0.16211 0.17864 0.302 0.763 trt -0.77893 0.45890 0.16893 0.14851 -5.245 1.56e-07 Likelihood ratio test=22.48 on 2 df, p=1.312e-05 n= 394, number of events= 155 > > > > cleanEx() > nameEx("rhDNase") > ### * rhDNase > > flush(stderr()); flush(stdout()) > > ### Name: rhDNase > ### Title: rhDNASE data set > ### Aliases: rhDNase > ### Keywords: datasets > > ### ** Examples > > # Build the start-stop data set for analysis, and > # replicate line 2 of table 8.13 > first <- subset(rhDNase, !duplicated(id)) #first row for each subject > dnase <- tmerge(first, first, id=id, tstop=as.numeric(end.dt -entry.dt)) > > # Subjects whose fu ended during the 6 day window are the reason for > # this next line > temp.end <- with(rhDNase, pmin(ivstop+6, end.dt-entry.dt)) > dnase <- tmerge(dnase, rhDNase, id=id, + infect=event(ivstart), + end= event(temp.end)) > # toss out the non-at-risk intervals, and extra variables > # 3 subjects had an event on their last day of fu, infect=1 and end=1 > dnase <- subset(dnase, (infect==1 | end==0), c(id:trt, fev:infect)) > agfit <- coxph(Surv(tstart, tstop, infect) ~ trt + fev, cluster=id, + data=dnase) > > > > cleanEx() > nameEx("ridge") > ### * ridge > > flush(stderr()); flush(stdout()) > > ### Name: ridge > ### Title: Ridge regression > ### Aliases: ridge > ### Keywords: survival > > ### ** Examples > > > coxph(Surv(futime, fustat) ~ rx + ridge(age, ecog.ps, theta=1), + ovarian) Call: coxph(formula = Surv(futime, fustat) ~ rx + ridge(age, ecog.ps, theta = 1), data = ovarian) coef se(coef) se2 Chisq DF p rx -0.8564 0.6161 0.6156 1.9323 1 0.1645 ridge(age) 0.1229 0.0385 0.0354 10.2127 1 0.0014 ridge(ecog.ps) 0.1093 0.5734 0.5484 0.0363 1 0.8488 Iterations: 1 outer, 5 Newton-Raphson Degrees of freedom for terms= 1.0 1.8 Likelihood ratio test=15.6 on 2.76 df, p=0.001 n= 26, number of events= 12 > > lfit0 <- survreg(Surv(time, status) ~1, lung) > lfit1 <- survreg(Surv(time, status) ~ age + ridge(ph.ecog, theta=5), lung) > lfit2 <- survreg(Surv(time, status) ~ sex + ridge(age, ph.ecog, theta=1), lung) > lfit3 <- survreg(Surv(time, status) ~ sex + age + ph.ecog, lung) > > > > > cleanEx() > nameEx("rotterdam") > ### * rotterdam > > flush(stderr()); flush(stdout()) > > ### Name: rotterdam > ### Title: Breast cancer data set used in Royston and Altman (2013) > ### Aliases: rotterdam > ### Keywords: datasets survival > > ### ** Examples > > status <- pmax(rotterdam$recur, rotterdam$death) > rfstime <- with(rotterdam, ifelse(recur==1, rtime, dtime)) > fit1 <- coxph(Surv(rfstime, status) ~ pspline(age) + meno + size + + pspline(nodes) + er, + data=rotterdam, subset = (nodes > 0)) > # Royston and Altman used fractional polynomials for the nonlinear terms > > > > cleanEx() > nameEx("royston") > ### * royston > > flush(stderr()); flush(stdout()) > > ### Name: royston > ### Title: Compute Royston's D for a Cox model > ### Aliases: royston > ### Keywords: survival > > ### ** Examples > > # An example used in Royston and Sauerbrei > pbc2 <- na.omit(pbc) # no missing values > cfit <- coxph(Surv(time, status==2) ~ age + log(bili) + edema + albumin + + stage + copper, data=pbc2, ties="breslow") > royston(cfit) D se(D) R.D R.KO R.N C.GH 2.6917766 0.2273352 0.6336693 0.5554885 0.4626961 0.7735923 > > > > cleanEx() > nameEx("rttright") > ### * rttright > > flush(stderr()); flush(stdout()) > > ### Name: rttright > ### Title: Compute redistribute-to-the-right weights > ### Aliases: rttright > ### Keywords: survival > > ### ** Examples > > afit <- survfit(Surv(time, status) ~1, data=aml) > rwt <- rttright(Surv(time, status) ~1, data=aml) > > index <- order(aml$time) > cdf <- cumsum(rwt[index]) # weighted CDF > cdf <- cdf[!duplicated(aml$time[index], fromLast=TRUE)] # remove duplicates > cbind(time=afit$time, KM= afit$surv, RTTR= 1-cdf) time KM RTTR [1,] 5 0.91304348 -1.000000 [2,] 8 0.82608696 -3.000000 [3,] 9 0.78260870 -4.000000 [4,] 12 0.73913043 -5.000000 [5,] 13 0.69565217 -6.000000 [6,] 16 0.69565217 -6.000000 [7,] 18 0.64596273 -7.142857 [8,] 23 0.54658385 -9.428571 [9,] 27 0.49689441 -10.571429 [10,] 28 0.49689441 -10.571429 [11,] 30 0.44168392 -11.841270 [12,] 31 0.38647343 -13.111111 [13,] 33 0.33126294 -14.380952 [14,] 34 0.27605245 -15.650794 [15,] 43 0.22084196 -16.920635 [16,] 45 0.16563147 -18.190476 [17,] 48 0.08281573 -20.095238 [18,] 161 0.08281573 -20.095238 > > > > cleanEx() > nameEx("solder") > ### * solder > > flush(stderr()); flush(stdout()) > > ### Name: solder > ### Title: Data from a soldering experiment > ### Aliases: solder > ### Keywords: datasets > > ### ** Examples > > # The balanced subset used by Chambers and Hastie > # contains the first 180 of each mask and deletes mask A6. > index <- 1 + (1:nrow(solder)) - match(solder$Mask, solder$Mask) > solder.balance <- droplevels(subset(solder, Mask != "A6" & index <= 180)) > > > > cleanEx() > nameEx("statefig") > ### * statefig > > flush(stderr()); flush(stdout()) > > ### Name: statefig > ### Title: Draw a state space figure. > ### Aliases: statefig > ### Keywords: survival hplot > > ### ** Examples > > # Draw a simple competing risks figure > states <- c("Entry", "Complete response", "Relapse", "Death") > connect <- matrix(0, 4, 4, dimnames=list(states, states)) > connect[1, -1] <- c(1.1, 1, 0.9) > statefig(c(1, 3), connect) > > > > cleanEx() > nameEx("strata") > ### * strata > > flush(stderr()); flush(stdout()) > > ### Name: strata > ### Title: Identify Stratification Variables > ### Aliases: strata > ### Keywords: survival > > ### ** Examples > > a <- factor(rep(1:3,4), labels=c("low", "medium", "high")) > b <- factor(rep(1:4,3)) > levels(strata(b)) [1] "1" "2" "3" "4" > levels(strata(a,b,shortlabel=TRUE)) [1] "low, 1" "low, 2" "low, 3" "low, 4" "medium, 1" "medium, 2" [7] "medium, 3" "medium, 4" "high, 1" "high, 2" "high, 3" "high, 4" > > coxph(Surv(futime, fustat) ~ age + strata(rx), data=ovarian) Call: coxph(formula = Surv(futime, fustat) ~ age + strata(rx), data = ovarian) coef exp(coef) se(coef) z p age 0.13735 1.14723 0.04741 2.897 0.00376 Likelihood ratio test=12.69 on 1 df, p=0.0003678 n= 26, number of events= 12 > > > > cleanEx() > nameEx("summary.aareg") > ### * summary.aareg > > flush(stderr()); flush(stdout()) > > ### Name: summary.aareg > ### Title: Summarize an aareg fit > ### Aliases: summary.aareg > ### Keywords: survival > > ### ** Examples > > afit <- aareg(Surv(time, status) ~ age + sex + ph.ecog, data=lung, + dfbeta=TRUE) > summary(afit) $table slope coef se(coef) robust se z Intercept 5.048983e-03 5.868616e-03 4.739162e-03 4.771021e-03 1.230055 age 4.005089e-05 7.149015e-05 7.228889e-05 6.996847e-05 1.021748 sex -3.164485e-03 -4.030555e-03 1.217949e-03 1.227954e-03 -3.282333 ph.ecog 3.009913e-03 3.673470e-03 1.016785e-03 1.015845e-03 3.616171 p Intercept 0.2186766165 age 0.3069001400 sex 0.0010295209 ph.ecog 0.0002989931 $test [1] "aalen" $test.statistic Intercept age sex ph.ecog 1.901744 108.155068 -19.531696 33.158152 $test.var b0 b0 2.358499 -151.80330 -3.715147 2.157013 -151.803299 11960.36511 16.697824 -277.142084 -3.715147 16.69782 34.834385 -5.183630 2.157013 -277.14208 -5.183630 84.233750 $test.var2 [,1] [,2] [,3] [,4] [1,] 2.390315 -149.43604 -4.237668 1.406427 [2,] -149.436042 11204.85060 69.078254 -169.293859 [3,] -4.237668 69.07825 35.409086 -12.726907 [4,] 1.406427 -169.29386 -12.726907 84.078078 $chisq [,1] [1,] 22.84047 $n [1] 227 136 138 attr(,"class") [1] "summary.aareg" > ## Not run: > ##D slope test se(test) robust se z p > ##D Intercept 5.05e-03 1.9 1.54 1.55 1.23 0.219000 > ##D age 4.01e-05 108.0 109.00 106.00 1.02 0.307000 > ##D sex -3.16e-03 -19.5 5.90 5.95 -3.28 0.001030 > ##D ph.ecog 3.01e-03 33.2 9.18 9.17 3.62 0.000299 > ##D > ##D Chisq=22.84 on 3 df, p=4.4e-05; test weights=aalen > ## End(Not run) > > summary(afit, maxtime=600) $table slope coef se(coef) robust se z Intercept 4.157249e-03 6.669391e-03 4.616582e-03 0.0045848352 1.454663 age 2.820613e-05 5.738087e-05 7.069894e-05 0.0000669899 0.856560 sex -2.536835e-03 -4.304800e-03 1.172585e-03 0.0011773976 -3.656199 ph.ecog 2.472413e-03 3.540475e-03 9.986578e-04 0.0009721356 3.641956 p Intercept 0.1457625229 age 0.3916881027 sex 0.0002559827 ph.ecog 0.0002705746 $test [1] "aalen" $test.statistic Intercept age sex ph.ecog 2.134219 85.797235 -20.601360 31.593788 $test.var Intercept age sex ph.ecog Intercept 2.182462 -141.28105 -3.398501 2.207493 age -141.281046 11174.75712 16.439611 -271.147721 sex -3.398501 16.43961 31.490109 -5.423722 ph.ecog 2.207493 -271.14772 -5.423722 79.417053 $test.var2 [,1] [,2] [,3] [,4] [1,] 2.152549 -133.2752 -4.043482 1.231431 [2,] -133.275184 10033.0045 70.870904 -180.875471 [3,] -4.043482 70.8709 31.749153 -7.222745 [4,] 1.231431 -180.8755 -7.222745 75.254781 $chisq [,1] [1,] 27.08125 $n [1] 227 121 138 attr(,"class") [1] "summary.aareg" > ## Not run: > ##D slope test se(test) robust se z p > ##D Intercept 4.16e-03 2.13 1.48 1.47 1.450 0.146000 > ##D age 2.82e-05 85.80 106.00 100.00 0.857 0.392000 > ##D sex -2.54e-03 -20.60 5.61 5.63 -3.660 0.000256 > ##D ph.ecog 2.47e-03 31.60 8.91 8.67 3.640 0.000271 > ##D > ##D Chisq=27.08 on 3 df, p=5.7e-06; test weights=aalen > ## End(Not run) > > > cleanEx() > nameEx("summary.coxph") > ### * summary.coxph > > flush(stderr()); flush(stdout()) > > ### Name: summary.coxph > ### Title: Summary method for Cox models > ### Aliases: summary.coxph > ### Keywords: survival > > ### ** Examples > > fit <- coxph(Surv(time, status) ~ age + sex, lung) > summary(fit) Call: coxph(formula = Surv(time, status) ~ age + sex, data = lung) n= 228, number of events= 165 coef exp(coef) se(coef) z Pr(>|z|) age 0.017045 1.017191 0.009223 1.848 0.06459 . sex -0.513219 0.598566 0.167458 -3.065 0.00218 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 exp(coef) exp(-coef) lower .95 upper .95 age 1.0172 0.9831 0.9990 1.0357 sex 0.5986 1.6707 0.4311 0.8311 Concordance= 0.603 (se = 0.025 ) Likelihood ratio test= 14.12 on 2 df, p=9e-04 Wald test = 13.47 on 2 df, p=0.001 Score (logrank) test = 13.72 on 2 df, p=0.001 > > > > cleanEx() > nameEx("summary.survfit") > ### * summary.survfit > > flush(stderr()); flush(stdout()) > > ### Name: summary.survfit > ### Title: Summary of a Survival Curve > ### Aliases: summary.survfit > ### Keywords: survival > > ### ** Examples > > summary( survfit( Surv(futime, fustat)~1, data=ovarian)) Call: survfit(formula = Surv(futime, fustat) ~ 1, data = ovarian) time n.risk n.event survival std.err lower 95% CI upper 95% CI 59 26 1 0.962 0.0377 0.890 1.000 115 25 1 0.923 0.0523 0.826 1.000 156 24 1 0.885 0.0627 0.770 1.000 268 23 1 0.846 0.0708 0.718 0.997 329 22 1 0.808 0.0773 0.670 0.974 353 21 1 0.769 0.0826 0.623 0.949 365 20 1 0.731 0.0870 0.579 0.923 431 17 1 0.688 0.0919 0.529 0.894 464 15 1 0.642 0.0965 0.478 0.862 475 14 1 0.596 0.0999 0.429 0.828 563 12 1 0.546 0.1032 0.377 0.791 638 11 1 0.497 0.1051 0.328 0.752 > summary( survfit( Surv(futime, fustat)~rx, data=ovarian)) Call: survfit(formula = Surv(futime, fustat) ~ rx, data = ovarian) rx=1 time n.risk n.event survival std.err lower 95% CI upper 95% CI 59 13 1 0.923 0.0739 0.789 1.000 115 12 1 0.846 0.1001 0.671 1.000 156 11 1 0.769 0.1169 0.571 1.000 268 10 1 0.692 0.1280 0.482 0.995 329 9 1 0.615 0.1349 0.400 0.946 431 8 1 0.538 0.1383 0.326 0.891 638 5 1 0.431 0.1467 0.221 0.840 rx=2 time n.risk n.event survival std.err lower 95% CI upper 95% CI 353 13 1 0.923 0.0739 0.789 1.000 365 12 1 0.846 0.1001 0.671 1.000 464 9 1 0.752 0.1256 0.542 1.000 475 8 1 0.658 0.1407 0.433 1.000 563 7 1 0.564 0.1488 0.336 0.946 > > > > cleanEx() > nameEx("survSplit") > ### * survSplit > > flush(stderr()); flush(stdout()) > > ### Name: survSplit > ### Title: Split a survival data set at specified times > ### Aliases: survSplit > ### Keywords: survival utilities > > ### ** Examples > > fit1 <- coxph(Surv(time, status) ~ karno + age + trt, veteran) > plot(cox.zph(fit1)[1]) > # a cox.zph plot of the data suggests that the effect of Karnofsky score > # begins to diminish by 60 days and has faded away by 120 days. > # Fit a model with separate coefficients for the three intervals. > # > vet2 <- survSplit(Surv(time, status) ~., veteran, + cut=c(60, 120), episode ="timegroup") > fit2 <- coxph(Surv(tstart, time, status) ~ karno* strata(timegroup) + + age + trt, data= vet2) > c(overall= coef(fit1)[1], + t0_60 = coef(fit2)[1], + t60_120= sum(coef(fit2)[c(1,4)]), + t120 = sum(coef(fit2)[c(1,5)])) overall.karno t0_60.karno t60_120 t120 -0.034443897 -0.049176157 -0.011031558 -0.007629841 > > > > cleanEx() > nameEx("survdiff") > ### * survdiff > > flush(stderr()); flush(stdout()) > > ### Name: survdiff > ### Title: Test Survival Curve Differences > ### Aliases: survdiff print.survdiff > ### Keywords: survival > > ### ** Examples > > ## Two-sample test > survdiff(Surv(futime, fustat) ~ rx,data=ovarian) Call: survdiff(formula = Surv(futime, fustat) ~ rx, data = ovarian) N Observed Expected (O-E)^2/E (O-E)^2/V rx=1 13 7 5.23 0.596 1.06 rx=2 13 5 6.77 0.461 1.06 Chisq= 1.1 on 1 degrees of freedom, p= 0.3 > > ## Stratified 7-sample test > > survdiff(Surv(time, status) ~ pat.karno + strata(inst), data=lung) Call: survdiff(formula = Surv(time, status) ~ pat.karno + strata(inst), data = lung) n=224, 4 observations deleted due to missingness. N Observed Expected (O-E)^2/E (O-E)^2/V pat.karno=30 2 1 0.692 0.13720 0.15752 pat.karno=40 2 1 1.099 0.00889 0.00973 pat.karno=50 4 4 1.166 6.88314 7.45359 pat.karno=60 30 27 16.298 7.02790 9.57333 pat.karno=70 41 31 26.358 0.81742 1.14774 pat.karno=80 50 38 41.938 0.36978 0.60032 pat.karno=90 60 38 47.242 1.80800 3.23078 pat.karno=100 35 21 26.207 1.03451 1.44067 Chisq= 21.4 on 7 degrees of freedom, p= 0.003 > > ## Expected survival for heart transplant patients based on > ## US mortality tables > expect <- survexp(futime ~ 1, data=jasa, cohort=FALSE, + rmap= list(age=(accept.dt - birth.dt), sex=1, year=accept.dt), + ratetable=survexp.us) > ## actual survival is much worse (no surprise) > survdiff(Surv(jasa$futime, jasa$fustat) ~ offset(expect)) Call: survdiff(formula = Surv(jasa$futime, jasa$fustat) ~ offset(expect)) Observed Expected Z p 75.000 0.644 -92.681 0.000 > > > > cleanEx() > nameEx("survexp") > ### * survexp > > flush(stderr()); flush(stdout()) > > ### Name: survexp > ### Title: Compute Expected Survival > ### Aliases: survexp print.survexp > ### Keywords: survival > > ### ** Examples > > # > # Stanford heart transplant data > # We don't have sex in the data set, but know it to be nearly all males. > # Estimate of conditional survival > fit1 <- survexp(futime ~ 1, rmap=list(sex="male", year=accept.dt, + age=(accept.dt-birth.dt)), method='conditional', data=jasa) > summary(fit1, times=1:10*182.5, scale=365) #expected survival by 1/2 years Call: survexp(formula = futime ~ 1, data = jasa, rmap = list(sex = "male", year = accept.dt, age = (accept.dt - birth.dt)), method = "conditional") time n.risk survival 0.5 41 0.996 1.0 28 0.993 1.5 21 0.989 2.0 16 0.986 2.5 13 0.983 3.0 8 0.980 3.5 7 0.977 4.0 3 0.972 4.5 1 0.969 > > # Estimate of expected survival stratified by prior surgery > survexp(~ surgery, rmap= list(sex="male", year=accept.dt, + age=(accept.dt-birth.dt)), method='ederer', data=jasa, + times=1:10 * 182.5) Call: survexp(formula = ~surgery, data = jasa, rmap = list(sex = "male", year = accept.dt, age = (accept.dt - birth.dt)), times = 1:10 * 182.5, method = "ederer") age ranges from 8.8 to 64.4 years male: 103 female: 0 date of entry from 1967-09-13 to 1974-03-22 time nrisk1 nrisk2 surgery=0 surgery=1 182 87 16 0.996 0.996 365 87 16 0.991 0.993 548 87 16 0.987 0.989 730 87 16 0.982 0.985 912 87 16 0.978 0.981 1095 87 16 0.973 0.977 1278 87 16 0.968 0.973 1460 87 16 0.963 0.969 1642 87 16 0.958 0.964 1825 87 16 0.952 0.960 > > ## Compare the survival curves for the Mayo PBC data to Cox model fit > ## > pfit <-coxph(Surv(time,status>0) ~ trt + log(bili) + log(protime) + age + + platelet, data=pbc) > plot(survfit(Surv(time, status>0) ~ trt, data=pbc), mark.time=FALSE) > lines(survexp( ~ trt, ratetable=pfit, data=pbc), col='purple') > > > > cleanEx() > nameEx("survexp.us") > ### * survexp.us > > flush(stderr()); flush(stdout()) > > ### Name: ratetables > ### Title: Census Data Sets for the Expected Survival and Person Years > ### Functions > ### Aliases: survexp.us survexp.usr survexp.mn > ### Keywords: survival datasets > > ### ** Examples > > survexp.uswhite <- survexp.usr[,,"white",] > > > > cleanEx() > nameEx("survfit.formula") > ### * survfit.formula > > flush(stderr()); flush(stdout()) > > ### Name: survfit.formula > ### Title: Compute a Survival Curve for Censored Data > ### Aliases: survfit.formula [.survfit > ### Keywords: survival > > ### ** Examples > > #fit a Kaplan-Meier and plot it > fit <- survfit(Surv(time, status) ~ x, data = aml) > plot(fit, lty = 2:3) > legend(100, .8, c("Maintained", "Nonmaintained"), lty = 2:3) > > #fit a Cox proportional hazards model and plot the > #predicted survival for a 60 year old > fit <- coxph(Surv(futime, fustat) ~ age, data = ovarian) > plot(survfit(fit, newdata=data.frame(age=60)), + xscale=365.25, xlab = "Years", ylab="Survival") > > # Here is the data set from Turnbull > # There are no interval censored subjects, only left-censored (status=3), > # right-censored (status 0) and observed events (status 1) > # > # Time > # 1 2 3 4 > # Type of observation > # death 12 6 2 3 > # losses 3 2 0 3 > # late entry 2 4 2 5 > # > tdata <- data.frame(time =c(1,1,1,2,2,2,3,3,3,4,4,4), + status=rep(c(1,0,2),4), + n =c(12,3,2,6,2,4,2,0,2,3,3,5)) > fit <- survfit(Surv(time, time, status, type='interval') ~1, + data=tdata, weight=n) > > # > # Three curves for patients with monoclonal gammopathy. > # 1. KM of time to PCM, ignoring death (statistically incorrect) > # 2. Competing risk curves (also known as "cumulative incidence") > # 3. Multi-state, showing Pr(in each state, at time t) > # > fitKM <- survfit(Surv(stop, event=='pcm') ~1, data=mgus1, + subset=(start==0)) > fitCR <- survfit(Surv(stop, event) ~1, + data=mgus1, subset=(start==0)) > fitMS <- survfit(Surv(start, stop, event) ~ 1, id=id, data=mgus1) > ## Not run: > ##D # CR curves show the competing risks > ##D plot(fitCR, xscale=365.25, xmax=7300, mark.time=FALSE, > ##D col=2:3, xlab="Years post diagnosis of MGUS", > ##D ylab="P(state)") > ##D lines(fitKM, fun='event', xmax=7300, mark.time=FALSE, > ##D conf.int=FALSE) > ##D text(3652, .4, "Competing risk: death", col=3) > ##D text(5840, .15,"Competing risk: progression", col=2) > ##D text(5480, .30,"KM:prog") > ## End(Not run) > > > > cleanEx() > nameEx("survfit.matrix") > ### * survfit.matrix > > flush(stderr()); flush(stdout()) > > ### Name: survfit.matrix > ### Title: Create Aalen-Johansen estimates of multi-state survival from a > ### matrix of hazards. > ### Aliases: survfit.matrix > ### Keywords: survival > > ### ** Examples > > etime <- with(mgus2, ifelse(pstat==0, futime, ptime)) > event <- with(mgus2, ifelse(pstat==0, 2*death, 1)) > event <- factor(event, 0:2, labels=c("censor", "pcm", "death")) > > cfit1 <- coxph(Surv(etime, event=="pcm") ~ age + sex, mgus2) > cfit2 <- coxph(Surv(etime, event=="death") ~ age + sex, mgus2) > > # predicted competing risk curves for a 72 year old with mspike of 1.2 > # (median values), male and female. > # The survfit call is a bit faster without standard errors. > newdata <- expand.grid(sex=c("F", "M"), age=72, mspike=1.2) > > AJmat <- matrix(list(), 3,3) > AJmat[1,2] <- list(survfit(cfit1, newdata, std.err=FALSE)) > AJmat[1,3] <- list(survfit(cfit2, newdata, std.err=FALSE)) > csurv <- survfit(AJmat, p0 =c(entry=1, PCM=0, death=0)) > > > > cleanEx() > nameEx("survobrien") > ### * survobrien > > flush(stderr()); flush(stdout()) > > ### Name: survobrien > ### Title: O'Brien's Test for Association of a Single Variable with > ### Survival > ### Aliases: survobrien > ### Keywords: survival > > ### ** Examples > > xx <- survobrien(Surv(futime, fustat) ~ age + factor(rx) + I(ecog.ps), + data=ovarian) > coxph(Surv(time, status) ~ age + strata(.strata.), data=xx) Call: coxph(formula = Surv(time, status) ~ age + strata(.strata.), data = xx) coef exp(coef) se(coef) z p age 0.5681 1.7649 0.1816 3.128 0.00176 Likelihood ratio test=10.55 on 1 df, p=0.001165 n= 230, number of events= 12 > > > > cleanEx() > nameEx("survreg") > ### * survreg > > flush(stderr()); flush(stdout()) > > ### Name: survreg > ### Title: Regression for a Parametric Survival Model > ### Aliases: survreg model.frame.survreg labels.survreg print.survreg.penal > ### print.summary.survreg survReg anova.survreg anova.survreglist > ### Keywords: survival > > ### ** Examples > > # Fit an exponential model: the two fits are the same > survreg(Surv(futime, fustat) ~ ecog.ps + rx, ovarian, dist='weibull', + scale=1) Call: survreg(formula = Surv(futime, fustat) ~ ecog.ps + rx, data = ovarian, dist = "weibull", scale = 1) Coefficients: (Intercept) ecog.ps rx 6.9618376 -0.4331347 0.5815027 Scale fixed at 1 Loglik(model)= -97.2 Loglik(intercept only)= -98 Chisq= 1.67 on 2 degrees of freedom, p= 0.434 n= 26 > survreg(Surv(futime, fustat) ~ ecog.ps + rx, ovarian, + dist="exponential") Call: survreg(formula = Surv(futime, fustat) ~ ecog.ps + rx, data = ovarian, dist = "exponential") Coefficients: (Intercept) ecog.ps rx 6.9618376 -0.4331347 0.5815027 Scale fixed at 1 Loglik(model)= -97.2 Loglik(intercept only)= -98 Chisq= 1.67 on 2 degrees of freedom, p= 0.434 n= 26 > > # > # A model with different baseline survival shapes for two groups, i.e., > # two different scale parameters > survreg(Surv(time, status) ~ ph.ecog + age + strata(sex), lung) Call: survreg(formula = Surv(time, status) ~ ph.ecog + age + strata(sex), data = lung) Coefficients: (Intercept) ph.ecog age 6.73234505 -0.32443043 -0.00580889 Scale: sex=1 sex=2 0.7834211 0.6547830 Loglik(model)= -1137.3 Loglik(intercept only)= -1146.2 Chisq= 17.8 on 2 degrees of freedom, p= 0.000137 n=227 (1 observation deleted due to missingness) > > # There are multiple ways to parameterize a Weibull distribution. The survreg > # function embeds it in a general location-scale family, which is a > # different parameterization than the rweibull function, and often leads > # to confusion. > # survreg's scale = 1/(rweibull shape) > # survreg's intercept = log(rweibull scale) > # For the log-likelihood all parameterizations lead to the same value. > y <- rweibull(1000, shape=2, scale=5) > survreg(Surv(y)~1, dist="weibull") Call: survreg(formula = Surv(y) ~ 1, dist = "weibull") Coefficients: (Intercept) 1.604435 Scale= 0.4965001 Loglik(model)= -2199.4 Loglik(intercept only)= -2199.4 n= 1000 > > # Economists fit a model called `tobit regression', which is a standard > # linear regression with Gaussian errors, and left censored data. > tobinfit <- survreg(Surv(durable, durable>0, type='left') ~ age + quant, + data=tobin, dist='gaussian') > > > > cleanEx() > nameEx("survreg.distributions") > ### * survreg.distributions > > flush(stderr()); flush(stdout()) > > ### Name: survreg.distributions > ### Title: Parametric Survival Distributions > ### Aliases: survreg.distributions > ### Keywords: survival > > ### ** Examples > > # time transformation > survreg(Surv(time, status) ~ ph.ecog + sex, dist='weibull', data=lung) Call: survreg(formula = Surv(time, status) ~ ph.ecog + sex, data = lung, dist = "weibull") Coefficients: (Intercept) ph.ecog sex 5.8195907 -0.3557319 0.4013684 Scale= 0.7310495 Loglik(model)= -1133.1 Loglik(intercept only)= -1147.4 Chisq= 28.73 on 2 degrees of freedom, p= 5.76e-07 n=227 (1 observation deleted due to missingness) > # change the transformation to work in years > # intercept changes by log(365), everything else stays the same > my.weibull <- survreg.distributions$weibull > my.weibull$trans <- function(y) log(y/365) > my.weibull$itrans <- function(y) 365*exp(y) > survreg(Surv(time, status) ~ ph.ecog + sex, lung, dist=my.weibull) Call: survreg(formula = Surv(time, status) ~ ph.ecog + sex, data = lung, dist = my.weibull) Coefficients: (Intercept) ph.ecog sex -0.08030664 -0.35573188 0.40136844 Scale= 0.7310495 Loglik(model)= -1133.1 Loglik(intercept only)= -1147.4 Chisq= 28.73 on 2 degrees of freedom, p= 5.76e-07 n=227 (1 observation deleted due to missingness) > > # Weibull parametrisation > y<-rweibull(1000, shape=2, scale=5) > survreg(Surv(y)~1, dist="weibull") Call: survreg(formula = Surv(y) ~ 1, dist = "weibull") Coefficients: (Intercept) 1.604435 Scale= 0.4965001 Loglik(model)= -2199.4 Loglik(intercept only)= -2199.4 n= 1000 > # survreg scale parameter maps to 1/shape, linear predictor to log(scale) > > # Cauchy fit > mycauchy <- list(name='Cauchy', + init= function(x, weights, ...) + c(median(x), mad(x)), + density= function(x, parms) { + temp <- 1/(1 + x^2) + cbind(.5 + atan(x)/pi, .5+ atan(-x)/pi, + temp/pi, -2 *x*temp, 2*temp*(4*x^2*temp -1)) + }, + quantile= function(p, parms) tan((p-.5)*pi), + deviance= function(...) stop('deviance residuals not defined') + ) > survreg(Surv(log(time), status) ~ ph.ecog + sex, lung, dist=mycauchy) Call: survreg(formula = Surv(log(time), status) ~ ph.ecog + sex, data = lung, dist = mycauchy) Coefficients: (Intercept) ph.ecog sex 5.4517240 -0.3979387 0.4692383 Scale= 0.4788955 Loglik(model)= -274.6 Loglik(intercept only)= -294.9 Chisq= 40.75 on 2 degrees of freedom, p= 1.42e-09 n=227 (1 observation deleted due to missingness) > > > > cleanEx() > nameEx("survregDtest") > ### * survregDtest > > flush(stderr()); flush(stdout()) > > ### Name: survregDtest > ### Title: Verify a survreg distribution > ### Aliases: survregDtest > ### Keywords: survival > > ### ** Examples > > # An invalid distribution (it should have "init =" on line 2) > # surveg would give an error message > mycauchy <- list(name='Cauchy', + init<- function(x, weights, ...) + c(median(x), mad(x)), + density= function(x, parms) { + temp <- 1/(1 + x^2) + cbind(.5 + atan(temp)/pi, .5+ atan(-temp)/pi, + temp/pi, -2 *x*temp, 2*temp^2*(4*x^2*temp -1)) + }, + quantile= function(p, parms) tan((p-.5)*pi), + deviance= function(...) stop('deviance residuals not defined') + ) > > survregDtest(mycauchy, TRUE) [1] "Missing or invalid init function" > > > > cleanEx() > nameEx("tcut") > ### * tcut > > flush(stderr()); flush(stdout()) > > ### Name: tcut > ### Title: Factors for person-year calculations > ### Aliases: tcut [.tcut levels.tcut > ### Keywords: survival > > ### ** Examples > > mdy.date <- function(m,d,y) + as.Date(paste(ifelse(y<100, y+1900, y), m, d, sep='/')) > temp1 <- mdy.date(6,6,36) > temp2 <- mdy.date(6,6,55)# Now compare the results from person-years > # > temp.age <- tcut(temp2-temp1, floor(c(-1, (18:31 * 365.24))), + labels=c('0-18', paste(18:30, 19:31, sep='-'))) > temp.yr <- tcut(temp2, mdy.date(1,1,1954:1965), labels=1954:1964) > temp.time <- 3700 #total days of fu > py1 <- pyears(temp.time ~ temp.age + temp.yr, scale=1) #output in days > py1 Call: pyears(formula = temp.time ~ temp.age + temp.yr, scale = 1) Total number of person-years tabulated: 3497 Total number of person-years off table: 203 Observations in the data set: 1 > > > > cleanEx() > nameEx("tmerge") > ### * tmerge > > flush(stderr()); flush(stdout()) > > ### Name: tmerge > ### Title: Time based merge for survival data > ### Aliases: tmerge > ### Keywords: survival > > ### ** Examples > > # The pbc data set contains baseline data and follow-up status > # for a set of subjects with primary biliary cirrhosis, while the > # pbcseq data set contains repeated laboratory values for those > # subjects. > # The first data set contains data on 312 subjects in a clinical trial plus > # 106 that agreed to be followed off protocol, the second data set has data > # only on the trial subjects. > temp <- subset(pbc, id <= 312, select=c(id:sex, stage)) # baseline data > pbc2 <- tmerge(temp, temp, id=id, endpt = event(time, status)) > pbc2 <- tmerge(pbc2, pbcseq, id=id, ascites = tdc(day, ascites), + bili = tdc(day, bili), albumin = tdc(day, albumin), + protime = tdc(day, protime), alk.phos = tdc(day, alk.phos)) > > fit <- coxph(Surv(tstart, tstop, endpt==2) ~ protime + log(bili), data=pbc2) > > > > cleanEx() > nameEx("tobin") > ### * tobin > > flush(stderr()); flush(stdout()) > > ### Name: tobin > ### Title: Tobin's Tobit data > ### Aliases: tobin > ### Keywords: datasets > > ### ** Examples > > tfit <- survreg(Surv(durable, durable>0, type='left') ~age + quant, + data=tobin, dist='gaussian') > > predict(tfit,type="response") [1] -3.04968679 -4.31254182 -0.54163315 -0.25607164 -1.85017727 -2.40987803 [7] -3.50629220 -0.74041486 -4.05145594 -3.55880518 -0.32223237 -3.68044619 [13] -3.65997456 -2.63254564 0.22382063 0.02177674 -0.09571284 -3.17696755 [19] -0.61521215 -3.13913903 > > > > > cleanEx() > nameEx("transplant") > ### * transplant > > flush(stderr()); flush(stdout()) > > ### Name: transplant > ### Title: Liver transplant waiting list > ### Aliases: transplant > ### Keywords: datasets > > ### ** Examples > > #since event is a factor, survfit creates competing risk curves > pfit <- survfit(Surv(futime, event) ~ abo, transplant) > pfit[,2] #time to liver transplant, by blood type Call: survfit(formula = Surv(futime, event) ~ abo, data = transplant) n nevent rmean* abo=A, death 325 21 164.9734 abo=B, death 103 10 202.4902 abo=AB, death 41 3 137.8293 abo=O, death 346 32 182.1075 *restricted mean time in state (max time = 2055 ) > plot(pfit[,2], mark.time=FALSE, col=1:4, lwd=2, xmax=735, + xscale=30.5, xlab="Months", ylab="Fraction transplanted", + xaxt = 'n') > temp <- c(0, 6, 12, 18, 24) > axis(1, temp*30.5, temp) > legend(450, .35, levels(transplant$abo), lty=1, col=1:4, lwd=2) > > # competing risks for type O > plot(pfit[4,], xscale=30.5, xmax=735, col=1:3, lwd=2) > legend(450, .4, c("Death", "Transpant", "Withdrawal"), col=1:3, lwd=2) > > > > cleanEx() > nameEx("udca") > ### * udca > > flush(stderr()); flush(stdout()) > > ### Name: udca > ### Title: Data from a trial of usrodeoxycholic acid > ### Aliases: udca udca1 udca2 > ### Keywords: datasets > > ### ** Examples > > # values found in table 8.3 of the book > fit1 <- coxph(Surv(futime, status) ~ trt + log(bili) + stage, + cluster =id , data=udca1) > fit2 <- coxph(Surv(futime, status) ~ trt + log(bili) + stage + + strata(endpoint), cluster=id, data=udca2) > > > > > cleanEx() > nameEx("untangle.specials") > ### * untangle.specials > > flush(stderr()); flush(stdout()) > > ### Name: untangle.specials > ### Title: Help Process the 'specials' Argument of the 'terms' Function. > ### Aliases: untangle.specials > ### Keywords: survival > > ### ** Examples > > formula <- Surv(tt,ss) ~ x + z*strata(id) > tms <- terms(formula, specials="strata") > ## the specials attribute > attr(tms, "specials") $strata [1] 4 > ## main effects > untangle.specials(tms, "strata") $vars [1] "strata(id)" $tvar [1] 3 $terms [1] 3 > ## and interactions > untangle.specials(tms, "strata", order=1:2) $vars [1] "strata(id)" $tvar [1] 3 $terms [1] 3 4 > > > > cleanEx() > nameEx("uspop2") > ### * uspop2 > > flush(stderr()); flush(stdout()) > > ### Name: uspop2 > ### Title: Projected US Population > ### Aliases: uspop2 > ### Keywords: datasets > > ### ** Examples > > us50 <- uspop2[51:101,, "2000"] #US 2000 population, 50 and over > age <- as.integer(dimnames(us50)[[1]]) > smat <- model.matrix( ~ factor(floor(age/5)) -1) > ustot <- t(smat) %*% us50 #totals by 5 year age groups > temp <- c(50,55, 60, 65, 70, 75, 80, 85, 90, 95) > dimnames(ustot) <- list(c(paste(temp, temp+4, sep="-"), "100+"), + c("male", "female")) > > > > cleanEx() > nameEx("xtfrm.Surv") > ### * xtfrm.Surv > > flush(stderr()); flush(stdout()) > > ### Name: xtfrm.Surv > ### Title: Sorting order for Surv objects > ### Aliases: xtfrm.Surv sort.Surv order.Surv > ### Keywords: survival > > ### ** Examples > > test <- c(Surv(c(10, 9,9, 8,8,8,7,5,5,4), rep(1:0, 5)), Surv(6.2, NA)) > test [1] 10.0 9.0+ 9.0 8.0+ 8.0 8.0+ 7.0 5.0+ 5.0 4.0+ 6.2? > sort(test) [1] 4+ 5 5+ 7 8 8+ 8+ 9 9+ 10 > > > > cleanEx() > nameEx("yates") > ### * yates > > flush(stderr()); flush(stdout()) > > ### Name: yates > ### Title: Population prediction > ### Aliases: yates > ### Keywords: models survival > > ### ** Examples > > fit1 <- lm(skips ~ Solder*Opening + Mask, data = solder) > yates(fit1, ~Opening, population = "factorial") Opening pmm std test chisq df ss Pr L 3.2638 0.33460 global 573.4 2 15980 < 1e-08 M 3.5700 0.30480 S 12.3519 0.31251 > > fit2 <- coxph(Surv(time, status) ~ factor(ph.ecog)*sex + age, lung) > yates(fit2, ~ ph.ecog, predict="risk") # hazard ratio factor(ph.ecog) pmm std test chisq df Pr 0 0.94238 0.46334 factor(ph.ecog) NA NA NA 1 1.42677 0.75697 2 1.74848 3.80017 3 NA 74.01221 > > > > ### *