213 lines
8.5 KiB
Plaintext
213 lines
8.5 KiB
Plaintext
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R Under development (unstable) (2024-04-17 r86441) -- "Unsuffered Consequences"
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Copyright (C) 2024 The R Foundation for Statistical Computing
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Platform: aarch64-unknown-linux-gnu
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R is free software and comes with ABSOLUTELY NO WARRANTY.
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You are welcome to redistribute it under certain conditions.
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Type 'license()' or 'licence()' for distribution details.
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R is a collaborative project with many contributors.
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Type 'contributors()' for more information and
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'citation()' on how to cite R or R packages in publications.
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Type 'demo()' for some demos, 'help()' for on-line help, or
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'help.start()' for an HTML browser interface to help.
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Type 'q()' to quit R.
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> library(survival)
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> # Test data set 1 for Fine-Gray regression
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> fdata <- data.frame(time =c(1,2,3,4,4,4,5,5,6,8,8, 9,10,12),
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+ status=factor(c(1,2,0,1,0,0,2,1,0,0,2, 0,1 ,0), 0:2,
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+ c("cen", "type1", "type2")),
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+ x =c(5,4,3,1,2,1,1,2,2,4,6,1,2, 0),
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+ id = 1:14)
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> test1 <- finegray(Surv(time, status) ~., fdata, count="fgcount")
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> test2 <- finegray(Surv(time, status) ~x, fdata, etype="type2")
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>
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> # When creating the censoring time distribution remember that
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> # censors happen after deaths, so the distribution does not drop until
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> # time 3+, 4+, 6+, 8+ and 9+
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> csurv <- list(time=c(0, 3, 4, 6, 8, 9),
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+ p = cumprod(c(1, 11/12, 8/10, 5/6, 3/4, 2/3)))
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> #
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> # For estimation of event type 1, the first subject of event type
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> # 2 will have weights of curve$p over (0,3], (3,4], (4,6], (6,8], (8,9]
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> # and (9,12]. All that really matters is the weight at times 1, 4, 5,
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> # and 10, however, which are the points at which events of type 1 happen
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> #
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> # The next subject of event type 2 occurs at time 5, and will have a
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> # weight of (9,12] /(4,5] = (5*4*2)/(7*5*3) = 8/21 at time 10. The last
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> # censor at time 6 has a weight of 2/3 at time 10.
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>
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> all.equal(test1$id, c(1, 2,2,2,2, 3:6, 7, 7, 8:11, 11, 12:14))
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[1] TRUE
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> twt <- c(1, csurv$p[c(1,2,3,6)], 1,1,1, 1, 1, 5/12, 1,1,1,
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+ 1, 1/2, 1,1,1)
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> all.equal(test1$fgwt, twt)
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[1] TRUE
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> #extra obs will end at times found in csurv$time, or max(time)=12
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> all.equal(test1$fgstop[test1$fgcount>0], c(4,6,12, 12,12))
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[1] TRUE
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>
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> #
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> # Verify the data reproduces a multi-state curve
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> # censoring times may be different in the two setups so only
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> # compare at the event times
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> sfit <- survfit(Surv(time, status) ~1, fdata)
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> sfit1<- survfit(Surv(fgstart, fgstop, fgstatus) ~1, test1, weights=fgwt)
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> sfita<- sfit["type1"]
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> i1 <- sfita$n.event > 0
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> i2 <- sfit1$n.event > 0
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> all.equal(sfita$pstate[i1], 1- sfit1$surv[i2])
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[1] TRUE
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>
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> sfitb <- sfit["type2"]
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> sfit2 <- survfit(Surv(fgstart, fgstop, fgstatus) ~1, test2, weights=fgwt)
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> i1 <- sfitb$n.event > 0
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> i2 <- sfit2$n.event > 0
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> all.equal(sfitb$pstate[i1], 1- sfit2$surv[i2])
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[1] TRUE
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>
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> # Test strata. Make a single data set that has fdata for the first 19
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> # rows, then fdata with outcomes switched for the second 19. It should
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> # reprise test1 and test2 in a single call.
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> fdata2 <- rbind(fdata, fdata)
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> fdata2$group <- rep(1:2, each=nrow(fdata))
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> temp <- c(1,3,2)[as.numeric(fdata$status)]
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> fdata2$status[fdata2$group==2] <- factor(temp, 1:3, levels(fdata$status))
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> test3 <- finegray(Surv(time, status) ~ .+ strata(group), fdata2)
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> vtemp <- c("fgstart", "fgstop", "fgstatus", "fgwt")
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> all.equal(test3[1:19, vtemp], test1[,vtemp])
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[1] TRUE
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> all.equal(test3[20:38, vtemp], test2[,vtemp], check.attributes=FALSE)
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[1] TRUE
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>
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> #
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> # Test data set 2: use the larger MGUS data set
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> # Time is in months which leads to lots of ties
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> etime <- with(mgus2, ifelse(pstat==0, futime, ptime))
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> event <- with(mgus2, ifelse(pstat==0, 2*death, 1))
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> e2 <- factor(event, 0:2, c('censor', 'pcm', 'death'))
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> edata <- finegray(Surv(etime, e2) ~ sex + id, mgus2, etype="pcm")
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>
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> # Build G(t) = the KM of the censoring distribution
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> # An event at time x is not "at risk" for censoring at time x (Geskus 2011)
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> tt <- sort(unique(etime)) # all the times
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> ntime <- length(tt)
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> nrisk <- nevent <- double(ntime)
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> for (i in 1:ntime) {
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+ nrisk[i] <- sum((etime > tt[i] & event >0) | (etime >= tt[i] & event==0))
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+ nevent[i] <- sum(etime == tt[i] & event==0)
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+ }
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> G <- cumprod(1- nevent/nrisk)
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>
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> # The weight is defined as w(t)= G(t-)/G(s-) where s is the event time
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> # for a subject who experiences an endpoint other then the one of interest
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> type2 <- event[edata$id]==2 # the rows to be expanded
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> # These rows are copied over as is: endpoint 1 and censors
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> all(edata$fgstop[!type2] == etime[edata$id[!type2]])
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[1] TRUE
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> all(edata$fgstart[!type2] ==0)
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[1] TRUE
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> all(edata$fgwt[!type2] ==1)
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[1] TRUE
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>
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> tdata <- edata[type2,] #expanded rows
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> first <- match(tdata$id, tdata$id) #points to the first row for each subject
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> Gwt <- c(1, G)[match(tdata$fgstop, tt)] # G(t-)
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> all.equal(tdata$fgwt, Gwt/Gwt[first])
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[1] TRUE
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>
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> # Test data 3, left truncation.
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> # Ties are assumed to be ordered as event, censor, entry
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> # H(t) = truncation distribution, and is calculated on a reverse time scale
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> # Since there is only one row per subject every obs is a "start" event.
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> # Per equation 5 and 6 of Geskus both G and H are right continuous functions
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> # (the value at t- epsilon is different than the value at t).
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> fdata <- data.frame(time1 = c(0,0,0,3,2,0,0,1,0,7,5, 0, 0, 0),
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+ time2 = c(1,2,3,4,4,4,5,5,6,8,8, 9,10,12),
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+ status= c(1,2,0,1,0,0,2,1,0,0,2, 0, 1 ,0),
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+ x = c(5,4,3,1,2,1,1,2,2,4,6, 1, 2, 0),
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+ id = 1:14)
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> tt <- sort(unique(c(fdata$time1, fdata$time2)))
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> ntime <- length(tt)
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> Grisk <- Gevent <- double(ntime)
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> Hrisk <- Hevent <- double(ntime)
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> for (i in 1:ntime) {
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+ Grisk[i] <- with(fdata, sum((time2 > tt[i] & status >0 & time1 < tt[i]) |
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+ (time2 >= tt[i] & status ==0 & time1 < tt[i])))
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+ Gevent[i]<- with(fdata, sum(time2 == tt[i] & status==0))
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+ Hrisk[i] <- with(fdata, sum(time2 > tt[i] & time1 <= tt[i]))
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+ Hevent[i]<- with(fdata, sum(time1 == tt[i]))
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+ }
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> G <- cumprod(1- Gevent/pmax(1,Grisk))
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> G2 <- survfit(Surv(time1, time2 - .1*(status !=0), status==0) ~1, fdata)
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> all.equal(G2$surv[G2$n.event>0], G[Gevent>0])
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[1] TRUE
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>
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> H <- double(ntime)
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> # The loop below uses the definition of equation 6 in Geskus
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> for (i in 1:ntime)
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+ H[i] <- prod((1- Hevent/pmax(1, Hrisk))[-(i:1)])
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> H2 <- rev(cumprod(rev(1 - Hevent/pmax(1, Hrisk)))) #alternate form
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> H3 <- survfit(Surv(-time2, -time1, rep(1,14)) ~1, fdata) # alternate 3
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> # c(0,H) = H(t-), H2 = H(t-) already due to the time reversal
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> i2 <- sort(match(unique(fdata$time1), tt)) #time points in H3
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> all.equal(c(0, H), c(H2, 1))
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[1] TRUE
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> all.equal(H2[i2], rev(H3$surv))
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[1] TRUE
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>
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> fg <- finegray(Surv(time1, time2, factor(status, 0:2)) ~ x, id=id, fdata)
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> stat2 <- !is.na(match(fg$id, fdata$id[fdata$status==2])) #expanded ids
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> all(fg$fgwt[!stat2] ==1) #ordinary rows are left alone
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[1] TRUE
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> all(fg$fgstart[!stat2] == fdata$time1[fdata$status !=2])
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[1] TRUE
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> all(fg$fgstop[!stat2] == fdata$time2[fdata$status !=2])
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[1] TRUE
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>
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> tdata <- fg[stat2,]
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> index <- match(tdata$id, tdata$id) # points to the first row for each
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> Gwt <- c(1, G)[match(tdata$fgstop, tt)] # G(t-)
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> Hwt <- c(0, H)[match(tdata$fgstop, tt)] # H(t-)
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> all.equal(tdata$fgwt, Gwt*Hwt/(Gwt*Hwt)[index])
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[1] TRUE
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>
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> #
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> # Test data 4: mgus2 data on age scale
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> # The answer is incorrect due to roundoff, but consistent
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> #
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> start <- mgus2$age # age in years
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> end <- start + etime/12 #etime in months
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> tt <- sort(unique(c(start, end))) # all the times
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> ntime <- length(tt)
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> Grisk <- Gevent <- double(ntime)
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> Hrisk <- Hevent <- double(ntime)
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> for (i in 1:ntime) {
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+ Grisk[i] <- sum(((end > tt[i] & event >0) | (end >= tt[i] & event==0)) &
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+ (tt[i] > start))
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+ Gevent[i] <- sum(end == tt[i] & event==0)
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+ Hrisk[i] <- sum(start <= tt[i] & end > tt[i])
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+ Hevent[i] <- sum(start == tt[i])
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+ }
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> G <- cumprod(1 - Gevent/pmax(1, Grisk)) # pmax to avoid 0/0
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> H <- rev(cumprod(rev(1-Hevent/pmax(1,Hrisk))))
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> H <- c(H[-1], 1) #make it right continuous
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>
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> wdata <- finegray(Surv(start, end, e2) ~ ., id=id, mgus2, timefix=FALSE)
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> type2 <- event[wdata$id]==2 # the rows to be expanded
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> tdata <- wdata[type2,]
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> first <- match(tdata$id, tdata$id)
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>
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> Gwt <- c(1, G)[match(tdata$fgstop, tt)] # G(t-)
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> Hwt <- c(0, H)[match(tdata$fgstop, tt)] # H(t-)
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> all.equal(tdata$fgwt, (Gwt/Gwt[first]) * (Hwt / Hwt[first]))
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[1] TRUE
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>
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>
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> proc.time()
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user system elapsed
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0.486 0.024 0.507
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