76 lines
3.2 KiB
R
76 lines
3.2 KiB
R
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library(survival)
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#
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# check of the Surv2 function
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#
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# Build a flat form of the mgus2 data set. Mix up the data set order, to test
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# out that part of the underlying code.
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set.seed(1953)
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m2 <- mgus2[sample(1:nrow(mgus2), nrow(mgus2),replace=FALSE),]
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temp1 <- data.frame(m2[,1:7], ftime=0)
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temp2 <- with(subset(m2, pstat==1),
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data.frame(id=id, ftime=ptime, event="progression"))
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# competing risks: use only the first of death and progression
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temp3 <- with(subset(m2, pstat==0),
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data.frame(id=id, ftime=futime,
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event=ifelse(death==0, "censor", "death")))
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mflat <- merge(temp1, rbind(temp2, temp3), all=TRUE)
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mflat$event <- factor(mflat$event, c("censor", "progression", "death"))
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sfit1 <- survfit(Surv2(ftime, event) ~ sex, mflat, id=id)
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# now compare it to the usual way
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etime <- with(mgus2, ifelse(pstat==1, ptime, futime))
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estat <- with(mgus2, ifelse(pstat==1, 1, 2*death))
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estat <- factor(estat, 0:2, c("censor", "progression", "death"))
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sfit2 <- survfit(Surv(etime, estat) ~ sex, mgus2)
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all.equal(sfit1$pstate, sfit2$pstate)
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# Cox model
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cfit1 <- coxph(Surv2(ftime, event) ~ sex + age, data=mflat, id=id)
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cfit2 <- coxph(Surv(etime, estat) ~ sex + age, data=mgus2, id=id)
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all.equal(cfit1[c("coefficients", "var", "loglik", "score")],
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cfit2[c("coefficients", "var", "loglik", "score")])
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# And using the explicit call to build a data set
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sdata <- Surv2data(Surv2(ftime, event) ~ ., data=mflat, id=id)
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cfit3 <- coxph(Surv2.y ~ sex + age, data=sdata, id=id)
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all.equal(cfit1[c("coefficients", "var", "loglik", "score")],
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cfit3[c("coefficients", "var", "loglik", "score")])
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# Create a data set with error = two events on the same day
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# A model with this data will generate an error.
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temp4 <- with(m2,
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data.frame(id=id, ftime=futime,
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event=ifelse(death==0, "censor", "death")))
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mflat2 <- merge(temp1, rbind(temp2, temp4), all=TRUE)
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mflat2$event <- factor(mflat2$event, c("censor", "prog", "death"))
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stemp <- survcheck(Surv2(ftime, event) ~ sex, data=mflat2, id=id)
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all.equal(stemp$duplicate$row, which(duplicated(mflat2[,c("id", "ftime")])))
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# Full 3 state model. We need to make progressions that are tied with
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# deaths be just a bit sooner.
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temp2b <- with(subset(m2, pstat==1),
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data.frame(id=id, ftime= ifelse(ptime==futime & death==1, ptime-.1, ptime),
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event="progression"))
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temp3b <- with(m2,
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data.frame(id=id, ftime=futime, event=ifelse(death==0, "censor", "death")))
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mflat3 <- merge(temp1, rbind(temp2b, temp3b), all=TRUE)
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mflat3$event <- factor(mflat3$event, c("censor", "progression", "death"))
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cfit4 <- coxph(Surv2(ftime, event) ~ sex + age + mspike, mflat3, id=id)
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# For a standard start-stop data set use tmerge
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m3 <- tmerge(m2[,1:7], subset(m2,,c(id, futime, death)), id=id,
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event= event(futime, 2*death))
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m3 <- tmerge(m3, temp2b, id=id, event= event(ftime))
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m3$event <- factor(m3$event, 0:2, c("censor", "progression", "death"))
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cfit5 <- coxph(Surv(tstart, tstop, event) ~ sex + age + mspike, m3, id=id)
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all.equal(cfit4[c("coefficients", "var", "loglik", "score")],
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cfit5[c("coefficients", "var", "loglik", "score")])
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