78 lines
3.3 KiB
R
78 lines
3.3 KiB
R
library(survival)
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aeq <- function(x, y, ...) all.equal(as.vector(x), as.vector(y), ...)
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# Check that a multi-state model, correctly set up, gives the same
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# solution as a time-dependent covariate.
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# This is a stronger test than mstrata: there the covariate which was mapped
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# into a state was constant, here it is time-dependent.
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#
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# First build the TD data set from pbcseq, with a categorical bilirubin
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pbc1 <- pbcseq
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pbc1$bili4 <- cut(pbc1$bili, c(0,1, 2,4, 100),
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c("normal", "1-2x", "2-4x", ">4"))
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ptemp <- subset(pbc1, !duplicated(id)) # first row of each
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pbc2 <- tmerge(ptemp[, c("id", "age", "sex")], ptemp, id,
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death= event(futime, status==2))
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pbc2 <- tmerge(pbc2, pbc1, id=id, bili = tdc(day, bili),
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bili4 = tdc(day, bili4), bstat = event(day, as.numeric(bili4)))
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btemp <- with(pbc2, ifelse(death, 5, bstat))
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# a row with the same starting and ending bili4 level is not an event
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b2 <- ifelse(((as.numeric(pbc2$bili4)) == btemp), 0, btemp)
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pbc2$bstat <- factor(b2, 0:5,
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c("censor", "normal", "1-2x", "2-4x", ">4", "death"))
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check1 <- survcheck(Surv(tstart, tstop, bstat) ~ 1, istate= bili4,
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id = id, data=pbc2)
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check1$transitions
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all.equal(as.character(pbc2$bili4), as.character(check1$istate))
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# the above verifies that I created the data set correctly
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# Standard coxph fit with a time dependent bili4 variable.
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fit1 <- coxph(Surv(tstart, tstop, death) ~ age + bili4, pbc2)
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# An additive multi-state fit, where bili4 is a state
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# The three forms below should all give identical models
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fit2 <- coxph(list(Surv(tstart, tstop, bstat) ~ 1,
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c(1:4):5 ~ age / common + shared), id= id, istate=bili4,
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data=pbc2)
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fit2b <- coxph(list(Surv(tstart, tstop, bstat) ~ 1,
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1:5 + 2:5 + 3:5 + 4:5 ~ age / common + shared),
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id= id, istate=bili4, data=pbc2)
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fit2c <- coxph(list(Surv(tstart, tstop, bstat) ~ 1,
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0:5 ~ age / common + shared),
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id= id, istate=bili4, data=pbc2)
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# Make sure the names are correct and the coefficients match
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aeq(coef(fit1), coef(fit2))
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aeq(names(coef(fit2)), c("age", "ph(2:5/1:5)", "ph(3:5/1:5)", "ph(4:5/1:5)"))
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all.equal(coef(fit2), coef(fit2b))
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all.equal(coef(fit2), coef(fit2c))
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# Now a model with a separate age effect for each bilirubin group
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fit3 <- coxph(Surv(tstart, tstop, death) ~ age*bili4, pbc2)
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fit3b <- coxph(Surv(tstart, tstop, death) ~ bili4/age, pbc2)
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fit4 <- coxph(list(Surv(tstart, tstop, bstat) ~ 1,
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c(1:4):5 ~ age / shared), id= id, istate=bili4,
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data=pbc2)
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all.equal(fit3$loglik, fit3b$loglik)
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all.equal(fit3$loglik, fit4$loglik)
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# The coefficients are quite different due to different codings for dummy vars
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# Unpack the interaction, first 4 coefs will be the age effect within each
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# bilirubin group
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temp <- c(coef(fit3)[1] + c(0, coef(fit3)[5:7]), coef(fit3)[2:4])
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names(temp)[1:4] <- c("age1", "age2", "age3", "age4")
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aeq(temp, coef(fit3b)[c(4:7, 1:3)])
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aeq(temp, coef(fit4))
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# Third, a model with separate baseline hazards for each bili group
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fit5 <- coxph(Surv(tstart, tstop, death) ~ strata(bili4)/age, pbc2,
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cluster=id)
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fit6 <- coxph(list(Surv(tstart, tstop, bstat) ~ 1, 0:5 ~ age),
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id=id, istate=bili4, pbc2)
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aeq(coef(fit5), coef(fit6))
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aeq(fit5$var, fit6$var)
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aeq(fit5$naive.var, fit6$naive.var)
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