R Under development (unstable) (2023-08-12 r84939 ucrt) -- "Unsuffered Consequences"
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> library(survival)
> 
> # start with the example used in chapter 2 of the book
> 
> bdata <- data.frame(time =   c(1, 2, 2, 3, 4, 4, 5, 5, 8, 8, 
+                                9, 10,11, 12,14, 15, 16, 16, 18, 20),
+                     status = c(1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1,
+                                0, 0, 1, 0, 0, 1, 0, 1, 0))
> 
> # First check: verify that the the RTTR reproduces the KM
> kfit <- survfit(Surv(time, status) ~1, bdata)
> bwt  <- rttright(Surv(time, status) ~1, bdata, renorm= FALSE)
> 
> cdf <- cumsum(bwt)/nrow(bdata)  # weighted CDF
> cdf <- cdf[!duplicated(bdata$time, fromLast=TRUE)]  # remove duplicates
> all.equal(kfit$surv, 1-cdf)
[1] TRUE
> 
> 
> # A covariate divides both survfit and rttr into disjoint groups, so repeat
> #  the above check on subsets of the aml data
> afit <- survfit(Surv(time, status) ~x, aml)
> awt <-  rttright(Surv(time, status) ~x, aml, renorm=TRUE)
> 
> igroup <- as.numeric(aml$x)
> for (i in 1:2) {
+     atemp <- awt[igroup ==i]   # subset for this curve
+     index <- order(aml$time[igroup ==i])
+     acdf <- cumsum(atemp[index])
+     acdf <- acdf[!duplicated(aml$time[igroup ==i], fromLast=TRUE)]
+     print(all.equal(afit[i]$surv, 1-acdf))
+ }
[1] TRUE
[1] TRUE
> 
> 
> ###########
> # Alternate computation using inverse prob of censoring weights.
> # First shift the censorings to avoid ties: if there is a death and a censor
> #   at time 10, say, the death was not at risk of censoring. Censoring weights
> #   happen "later".  This also results in a left-continuous curve.
> delta <- min(diff(sort(unique(bdata$time)))) /3
> offset <- ifelse(bdata$status==1, 0, delta)
> cfit <- survfit(Surv(time+ offset, 1-status) ~ 1, bdata)
> 
> # interpolate
> indx <- findInterval(bdata$time, cfit$time)
> cwt <- ifelse(bdata$status==0, 0, 1/cfit$surv[indx])
> all.equal(bwt, cwt) 
[1] TRUE
> 
> # Multiple time points, this example is used in the vignette
> tdata <- data.frame(time=   c(1,2,2,3,4,4,5,5,8,9),
+                     status= c(1,1,0,1,0,0,1,0,1,1))
> fit1 <- rttright(Surv(time, status) ~ 1, tdata, times=2:6, renorm=FALSE)
> fit2 <- rttright(Surv(time, status) ~ 1, tdata, times=2:6, renorm=TRUE)
> all.equal(fit1, 10*fit2)
[1] TRUE
> all.equal(fit1, cbind(7, c(7,7,0,8,8,8,8,8,8,8), 
+                          c(7,7,0,8,8,8,8,8,8,8), 
+                          c(7,7,0,8,0,0,12,12,12,12),
+                          c(7,7,0,8,0,0,12, 0, 18,18))/7, check.attributes=FALSE)
[1] TRUE
> 
> # Now test with (start, stop] data, should get the same results
> b2 <- survSplit(Surv(time, status) ~ 1, bdata, cut= c(3,5, 7, 14),
+                 id = "subject")
> indx <- c(seq(1, 65, by=2), seq(64, 2, by= -2))
> b2 <- b2[indx,]    # not in time within subject order (stronger test)
> 
> b2wt <- rttright(Surv(tstart, time, status) ~1, b2, id=subject)
> indx2 <- order(b2$time)
> cdf2 <- cumsum(b2wt[indx2])
> cdf2 <- cdf2[!duplicated(b2$time[indx2], fromLast=TRUE)] # remove duplicates
> utime2 <- sort(unique(b2$time))   # will have an extra time 7
> utime1 <- sort(unique(bdata$time))
> all.equal(cdf2[match(utime1, utime2)], cdf)
[1] TRUE
> 
> 
> # Competing risks
> mdata <- mgus2
> mdata$etime <- with(mgus2, ifelse(pstat==1, ptime, futime))
> mdata$estat <- with(mgus2, ifelse(pstat==1, 1, 2*death))
> mdata$estat <- factor(mdata$estat, 0:2, c('censor', 'pcm', 'death'))
> mfit <- survfit(Surv(etime, estat) ~1, mdata, id=id)
> mwt1 <- rttright(Surv(etime, estat) ~1, mdata, id=id)
> 
> morder <- order(mdata$etime)
> mdata2 <- mdata[morder,]
> mwt2   <- rttright(Surv(etime,estat) ~1, mdata2, id=id)
> all.equal(mwt1[morder], mwt2)
[1] TRUE
> 
> keep <- !duplicated(mdata2$etime, fromLast=TRUE)
> csum1 <- cumsum(ifelse(mdata2$estat=="pcm", mwt2, 0))
> csum2 <- cumsum(ifelse(mdata2$estat=="death", mwt2, 0))
> 
> all.equal(mfit$pstate[,2], csum1[keep])
[1] TRUE
> all.equal(mfit$pstate[,3], csum2[keep])
[1] TRUE
> 
> # Case weights, at multiple times
> bwt <- rep(1:2, length=nrow(bdata))
> tm <- c(2, 6, 10, 15, 18)
> fit1 <- rttright(Surv(time, status) ~1, bdata, weights=bwt, times= tm)
> casefit <- survfit(Surv(time, status) ~ 1, bdata, weights= bwt)
> csum1 <- summary(casefit, censor=FALSE, times= tm)
> for (i in 1:length(tm)) {
+     c1 <- sum(fit1[bdata$status==1 & bdata$time <= tm[i], i])
+     print(all.equal(c1, 1-csum1$surv[i]))
+ }
[1] TRUE
[1] TRUE
[1] TRUE
[1] TRUE
[1] TRUE
> 
> 
> ###
> # Delayed entry, tiny data set
> #  Still to be done
> delay <- data.frame(t0=c(0,0,0,0,3,0),
+                     t1=1:6,
+                     status=c(1,0,1,0,0,1),
+                     id=1:6)
> # dwt <- rttright(Surv(t0, t1, status) ~ 1, delay, id=id, times=0:5 + .9)
> 
> 
> 
> proc.time()
   user  system elapsed 
   1.03    0.09    1.10