72 lines
2.8 KiB
R
72 lines
2.8 KiB
R
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#
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# Verify that using multiple states + proportional baselines
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# will mimic a factor covariate
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#
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library(survival)
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aeq <- function(x, y, ...) all.equal(as.vector(x), as.vector(y), ...)
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tdata <- subset(lung, ph.ecog < 3) # there is only one row with ph.ecog=3
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tdata$state <- factor(tdata$status, 1:2, c("censor", "death"))
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tdata$cstate<- factor(tdata$ph.ecog, 0:2, c("ph0", "ph1", "ph2"))
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tdata$id <- 1:nrow(tdata)
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survcheck(Surv(time, state) ~ 1, id=id, istate=cstate, tdata)
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# standard coxph fit, stratified by the ph0/1/2 groups
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fit1 <- coxph(Surv(time, status) ~ age + sex + factor(ph.ecog), tdata,
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ties="breslow")
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# multi-state fit, where ph0/1/2 are states with a shared hazard
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fit2 <- coxph(list(Surv(time, state) ~1,
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1:4 + 2:4 + 3:4~ age + sex/ common + shared),
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id=id, istate=cstate, data= tdata, ties="breslow")
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aeq(coef(fit1), coef(fit2)) # the names are quite different, values the same
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all.equal(fit1$loglik, fit2$loglik)
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# Three curves in the usual way: ph0, 1, or 2 for all time, common baseline
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csurv1 <- survfit(fit1, newdata=expand.grid(age=65, sex=1, ph.ecog=0:2))
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# Multistate: start in p0, p1, or p2 (the only place to go is death)
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csurv2a <- survfit(fit2, newdata= list(age=65, sex=1), p0=c(1,0,0,0))
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csurv2b <- survfit(fit2, newdata= list(age=65, sex=1), p0=c(0,1,0,0))
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csurv2c <- survfit(fit2, newdata= list(age=65, sex=1), p0=c(0,0,1,0))
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aeq(csurv1[1]$surv, csurv2a$pstate[,1,1])
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aeq(csurv1[2]$surv, csurv2b$pstate[,1,2])
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aeq(csurv1[3]$surv, csurv2c$pstate[,1,3])
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# Note that multi-state defaults to the Breslow, as it implements the Efron
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# only imperfectly.
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# part 2: predicted survival for a multistate model that has a strata
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mgus2$etime <- with(mgus2, ifelse(pstat==0, futime, ptime))
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temp <- with(mgus2, ifelse(pstat==0, 2*death, 1))
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mgus2$event <- factor(temp, 0:2, labels=c("censor", "pcm", "death"))
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dummy <- expand.grid(age=c(60, 80), mspike=1.2)
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cfit1 <- coxph(Surv(etime, event) ~ age + mspike +strata(sex), mgus2, id=id)
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csurv1 <- survfit(cfit1, newdata=dummy)
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cfit2 <- coxph(Surv(etime, event) ~ age + mspike, id=id,
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init= coef(cfit1), iter=0, data=mgus2, subset=(sex=='F'))
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csurv3 <- survfit(cfit2, newdata= expand.grid(age=c(60, 80), mspike=1.2))
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test <- c('n', 'time', 'n.risk', 'n.event', 'n.censor', 'pstate', 'cumhaz')
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all.equal(unclass(csurv1[1,,])[test], unclass(csurv3)[test])
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# Part 3: compare a shared baseline to identical baseline
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if (FALSE) {
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# not yet completed
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fit3 <- coxph(list(Surv(time, state) ~1,
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1:4 + 2:4 + 3:4~ age + sex/ common + 1),
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id=id, istate=cstate, data= tdata)
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fit4 <- coxph(list(Surv(time, state) ~1,
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1:4 + 2:4 + 3:4~ age + sex/ 1),
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id=id, istate=cstate, data= tdata)
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fit0 <- coxph(Surv(time, status) ~ age + sex, tdata, ties="breslow")
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survfit(fit3, newdata= list(age=65, sex=1))
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}
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