106 lines
4.0 KiB
R
106 lines
4.0 KiB
R
library(survival)
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# start with the example used in chapter 2 of the book
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bdata <- data.frame(time = c(1, 2, 2, 3, 4, 4, 5, 5, 8, 8,
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9, 10,11, 12,14, 15, 16, 16, 18, 20),
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status = c(1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1,
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0, 0, 1, 0, 0, 1, 0, 1, 0))
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# First check: verify that the the RTTR reproduces the KM
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kfit <- survfit(Surv(time, status) ~1, bdata)
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bwt <- rttright(Surv(time, status) ~1, bdata, renorm= FALSE)
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cdf <- cumsum(bwt)/nrow(bdata) # weighted CDF
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cdf <- cdf[!duplicated(bdata$time, fromLast=TRUE)] # remove duplicates
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all.equal(kfit$surv, 1-cdf)
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# A covariate divides both survfit and rttr into disjoint groups, so repeat
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# the above check on subsets of the aml data
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afit <- survfit(Surv(time, status) ~x, aml)
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awt <- rttright(Surv(time, status) ~x, aml, renorm=TRUE)
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igroup <- as.numeric(aml$x)
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for (i in 1:2) {
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atemp <- awt[igroup ==i] # subset for this curve
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index <- order(aml$time[igroup ==i])
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acdf <- cumsum(atemp[index])
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acdf <- acdf[!duplicated(aml$time[igroup ==i], fromLast=TRUE)]
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print(all.equal(afit[i]$surv, 1-acdf))
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}
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###########
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# Alternate computation using inverse prob of censoring weights.
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# First shift the censorings to avoid ties: if there is a death and a censor
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# at time 10, say, the death was not at risk of censoring. Censoring weights
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# happen "later". This also results in a left-continuous curve.
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delta <- min(diff(sort(unique(bdata$time)))) /3
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offset <- ifelse(bdata$status==1, 0, delta)
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cfit <- survfit(Surv(time+ offset, 1-status) ~ 1, bdata)
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# interpolate
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indx <- findInterval(bdata$time, cfit$time)
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cwt <- ifelse(bdata$status==0, 0, 1/cfit$surv[indx])
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all.equal(bwt, cwt)
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# Multiple time points, this example is used in the vignette
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tdata <- data.frame(time= c(1,2,2,3,4,4,5,5,8,9),
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status= c(1,1,0,1,0,0,1,0,1,1))
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fit1 <- rttright(Surv(time, status) ~ 1, tdata, times=2:6, renorm=FALSE)
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fit2 <- rttright(Surv(time, status) ~ 1, tdata, times=2:6, renorm=TRUE)
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all.equal(fit1, 10*fit2)
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all.equal(fit1, cbind(7, c(7,7,0,8,8,8,8,8,8,8),
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c(7,7,0,8,8,8,8,8,8,8),
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c(7,7,0,8,0,0,12,12,12,12),
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c(7,7,0,8,0,0,12, 0, 18,18))/7, check.attributes=FALSE)
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# Now test with (start, stop] data, should get the same results
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b2 <- survSplit(Surv(time, status) ~ 1, bdata, cut= c(3,5, 7, 14),
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id = "subject")
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indx <- c(seq(1, 65, by=2), seq(64, 2, by= -2))
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b2 <- b2[indx,] # not in time within subject order (stronger test)
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b2wt <- rttright(Surv(tstart, time, status) ~1, b2, id=subject)
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indx2 <- order(b2$time)
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cdf2 <- cumsum(b2wt[indx2])
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cdf2 <- cdf2[!duplicated(b2$time[indx2], fromLast=TRUE)] # remove duplicates
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utime2 <- sort(unique(b2$time)) # will have an extra time 7
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utime1 <- sort(unique(bdata$time))
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all.equal(cdf2[match(utime1, utime2)], cdf)
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# Competing risks
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mdata <- mgus2
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mdata$etime <- with(mgus2, ifelse(pstat==1, ptime, futime))
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mdata$estat <- with(mgus2, ifelse(pstat==1, 1, 2*death))
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mdata$estat <- factor(mdata$estat, 0:2, c('censor', 'pcm', 'death'))
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mfit <- survfit(Surv(etime, estat) ~1, mdata, id=id, time0=FALSE)
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mwt1 <- rttright(Surv(etime, estat) ~1, mdata, id=id)
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morder <- order(mdata$etime)
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mdata2 <- mdata[morder,]
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mwt2 <- rttright(Surv(etime,estat) ~1, mdata2, id=id)
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all.equal(mwt1[morder], mwt2)
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keep <- !duplicated(mdata2$etime, fromLast=TRUE)
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csum1 <- cumsum(ifelse(mdata2$estat=="pcm", mwt2, 0))
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csum2 <- cumsum(ifelse(mdata2$estat=="death", mwt2, 0))
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all.equal(mfit$pstate[,2], csum1[keep])
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all.equal(mfit$pstate[,3], csum2[keep])
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# Case weights, at multiple times
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bwt <- rep(1:2, length=nrow(bdata))
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tm <- c(2, 6, 10, 15, 18)
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fit1 <- rttright(Surv(time, status) ~1, bdata, weights=bwt, times= tm)
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casefit <- survfit(Surv(time, status) ~ 1, bdata, weights= bwt)
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csum1 <- summary(casefit, censor=FALSE, times= tm)
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for (i in 1:length(tm)) {
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c1 <- sum(fit1[bdata$status==1 & bdata$time <= tm[i], i])
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print(all.equal(c1, 1-csum1$surv[i]))
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}
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