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Type 'q()' to quit R. > library(survival) > aeq <- function(x,y, ...) all.equal(as.vector(x), as.vector(y), ...) > # > # Test the multi-state version of the CI curve > # > tdata <- data.frame(id=c(1,1,1,1, 2,2,2, 3,3, 4,4,4,4, 5, 6, 6), + time1=c(0, 10,20,30, 0, 5, 15, 0, 20, 0, 6,18,34, 0, 0,15), + time2=c(10,20,30,40, 5, 15,25, 20, 22, 6,18,34,50,10,15,20), + status=c(1,1,1,1, 1,1,1, 1,0, 1,1,1,0,0,1,0), + event= letters[c(1,2,3,4, 2,4,3, 2,2, 3,1,2,2,1, 1,1)], + wt = c(2,2,2,2, 1,1,1, 3,3, 1,1,1,1, 2, 1,1), + stringsAsFactors=TRUE) > tdata$stat2 <- factor(tdata$status * as.numeric(tdata$event), + labels=c("censor", levels(tdata$event))) > > fit <- survfit(Surv(time1, time2, stat2) ~1, id=id, weight=wt, tdata, + influence=TRUE) > > # The exact figures for testci2. > # The subject data of id, weight, (transition time, transition) > > #1: 2 (10, 0->a) (20, a->b) (30, b->c) (40, c->d) no data after 40=censored > #2: 1 ( 5, 0->b) (15, b->d) (25, d->c) no data after 25 implies censored then > #3: 3 (20, 0->b) (22, censor) > #4: 1 ( 6, 0->c) (18, c->a) (34, a->b) (50, censor) > #5: 2 (10, censor) > #6: 1 (15, 0->a) (20, censor) > > # Each line below follows a subject through time as a (state, rdist weight) pair > # using the redistribute to the right algorithm. > # RDR algorithm: at each censoring (or last fu) a subject's weight is put into > # a "pool" for that state and their weight goes to zero. The pool is > # dynamically shared between all members of the state proportional to their > # original case weight, when someone leaves they take their portion of the > # pool to the new state. > > # Table of case weights and state, blank is weight of zero > # time 5 6 10 15 18 20 25 30 34 40 50 > # ----------------------------------------------------------------------- > # id, wt > # 1, 2 - - a a a b b c c d > # 2, 1 b b b d d d c > # 3, 3 - - - - - b > # 4, 1 - c c c a a a a b b b > # 5, 2 - - - > # 6, 1 - - - a a a > > # Pool weights > # 10 10+ 15 18 20 20+ 22+ 25 25+ 30 34 40 40+ > # - 0 2 3/2 3/2 0 > # a 0 0 1/2 1/2 1/4 5/4 5/4 5/4 5/4 5/4 > # b 0 0 0 0 7/4 7/4 19/4 19/4 19/4 5/4 5/4 5/4 > # c 0 0 0 0 0 1 23/4 23/4 > # d 0 0 0 0 0 23/4 31/4 > > # fit$pstate for time i and state j = total weight at that time/state in the > # above table (original weight + redistrib), divided by 10. > > # time 5 6 10 15 18 20 25 30 34 40 50 > truth <- matrix(c(0, 0, 2, 3, 4, 2, 1, 1, 0, 0, 0, + 1, 1, 1, 0, 0, 5, 2, 0, 1, 1, 1, + 0, 1, 1, 1, 0, 0, 1, 2, 2, 0, 0, + 0, 0, 0, 1, 1, 1, 0, 0, 0, 2, 0) + + c(0, 0, 0, .5, .5, 1/4, 5/4, 5/4, 0, 0, 0, + 0, 0, 0, 0, 0, 7/4, 19/4, 0, 5/4, 5/4, 5/4, + 0, 0, 0, 0, 0, 0, 0, 23/4, 23/4, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 23/4, 31/4), + ncol=4) > truth <- truth[c(1:6, 6:11),]/10 #the explicit censor at 22 > > #dimnames(truth) <- list(c(5, 6, 10, 15, 18, 20, 25, 30, 34, 40, 50), > # c('a', 'b', 'c', 'd') > aeq(truth, fit$pstate[,2:5]) [1] TRUE > > # Test the dfbetas > # It was a big surprise, but the epsilon where a finite difference approx to > # the derivative is most accurate is around 1e-7 = approx sqrt(precision). > # Smaller eps makes the approximate derivative worse. > # There is a now a formal test in mstate.R, not approximate. > > # compute the per observation influence first > n <- nrow(tdata) > U <- array(0, dim=c(n, dim(fit$pstate))) > eps <- sqrt(.Machine$double.eps) > n <- nrow(tdata) > for (i in 1:n) { + twt <- tdata$wt + twt[i] <- twt[i] + eps + tfit <- survfit(Surv(time1, time2, stat2) ~ 1, id=id, tdata, + weight=twt) + U[i,,] <- (tfit$pstate - fit$pstate)/eps #finite difference approx + } > dfbeta <- rowsum(tdata$wt*matrix(U,nrow=n), tdata$id) # per subject > dfbeta <- array(dfbeta, dim=c(6,12,5)) > aeq(dfbeta, fit$influence, tolerance= eps*10) [1] TRUE > > aeq(fit$std.err, sqrt(apply(fit$influence.pstate^2, 2:3, sum))) [1] TRUE > > if (FALSE) { + # a plot of the data that helped during creation of the example + plot(c(0,50), c(1,6), type='n', xlab='time', ylab='subject') + with(tdata, segments(time1, id, time2, id)) + with(tdata, text(time2, id, as.numeric(stat2)-1, cex=1.5, col=2)) + } > > if (FALSE) { + # The following lines test out 4 error messages in the routine + # + # Gap in follow-up time, id 2 + survfit(Surv(c(0,5,9,0,5,0), c(5,9,12, 4, 6, 3), factor(c(0,0,1,1,0,2))) ~1, + id=c(1,1,1,2,2,3)) + # mismatched weights + survfit(Surv(c(0,5,9,0,5,0), c(5,9,12, 5, 6, 3), factor(c(0,0,1,1,0,2))) ~1, + id=c(1,1,1,2,2,3), weights=c(1,1,2,1,1,4)) + # in two groups at once + survfit(Surv(c(0,5,9,0,5,0), c(5,9,12, 5, 6, 3), factor(c(0,0,1,1,0,2))) ~ + c(1,1,2,1,1,2), id=c(1,1,1,2,2,3)) + # state change that isn't a state change (went from 1 to 1) + survfit(Surv(c(0,5,9,0,5,0), c(5,9,12, 5, 6, 3), factor(c(0,1,1,1,0,2))) ~1, + id=c(1,1,1,2,2,3)) + } > > # Check the start.time option > # > # Later work showed this test has to be false. At time 0 everyone starts in > # state (s0), but by time 20 many have shifted to another. fit2 picks up at > # the right place, but because there is no istate varaible, fit2x starts > # everyone in (s0) at time 20. There is no way for survfit to know. > if (FALSE) { + fit2 <- survfit(Surv(time1, time2, stat2) ~1, id=id, weight=wt, tdata, + start.time=20) + data2 <- subset(tdata, time2>= 20) + fit2x <- survfit(Surv(time1, time2, stat2) ~1, id=id, weight=wt, data2) + + ii <- names(fit2)[!(names(fit2) %in% c("call", "start.time"))] + all.equal(unclass(fit2)[ii], unclass(fit2x)[ii]) + } > > proc.time() user system elapsed 0.89 0.04 0.93