133 lines
4.6 KiB
Plaintext
133 lines
4.6 KiB
Plaintext
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R Under development (unstable) (2021-04-20 r80202) -- "Unsuffered Consequences"
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Copyright (C) 2021 The R Foundation for Statistical Computing
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Platform: x86_64-pc-linux-gnu (64-bit)
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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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> #
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> # Tests for multi-state Cox models
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> #
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> library(survival)
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>
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> aeq <- function(x,y, ...) all.equal(as.vector(x), as.vector(y), ...)
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>
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> # There are a few subjects with progression and death on the same day. In the
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> # usual multi-state data set only one will count.
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> data1 <- mgus2
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> data1$etime <- with(data1, ifelse(pstat==1, ptime, futime))
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> data1$event <- factor(ifelse(data1$pstat==1, 1, 2L*data1$death),
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+ 0:2, c("censor", "PCM", "death"))
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>
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> # direct data set with 2 rows per subject, much like mstate package would do
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> data2 <- mgus2[rep(1:nrow(mgus2) ,2), c("id", "age", "sex", "mspike")]
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> data2$time <- rep(data1$etime, 2)
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> data2$status <- 1* c(data1$event=="PCM", data1$event=="death")
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> data2$type <- rep(c(2:3), each=nrow(mgus2))
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>
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> fit1 <- coxph(Surv(etime, event) ~ age + sex + mspike, data1, id=id, x=TRUE,
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+ robust=FALSE)
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> fit1a <- coxph(Surv(etime, event=="PCM") ~ age + sex + mspike, data1)
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> fit1b <- coxph(Surv(etime, event=='death') ~ age + sex + mspike, data1)
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> fit1c <- coxph(Surv(time, status) ~ strata(type)/(age + sex+ mspike),
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+ data2, x=TRUE)
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>
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> aeq(fit1$loglik, fit1a$loglik + fit1b$loglik)
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[1] TRUE
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> aeq(fit1$coef, c(fit1a$coef, fit1b$coef))
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[1] TRUE
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> aeq(fit1$var[1:3, 1:3], fit1a$var)
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[1] TRUE
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> aeq(fit1$var[4:6, 4:6], fit1b$var)
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[1] TRUE
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> aeq(fit1$coef[c(1,4,2,5,3,6)], fit1c$coef)
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[1] TRUE
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>
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> # force a common age effect across all states
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> fit2 <- coxph(list(Surv(etime, event) ~ sex,
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+ 1:0 ~ age / common),
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+ data1, id=id)
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>
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> data2 <- rbind(cbind(data1, status= (data1$event=="PCM"), etype=1),
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+ cbind(data1, status= (data1$event=='death'), etype=2))
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> fit2a <- coxph(Surv(etime, status) ~ age + strata(etype)/sex, data2)
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>
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> aeq(coef(fit2), coef(fit2a)[c(2,1,3)]) # not in the same order
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[1] TRUE
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> aeq(fit2$loglik, fit2a$loglik)
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[1] TRUE
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>
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> #same fit in more complex ways
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> data1$entry <- "Entry"
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> fit2b <- coxph(list(Surv(etime, event) ~ sex,
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+ "Entry":"PCM" + "Entry":"death" ~ age / common),
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+ istate=entry, data1, id=id)
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> fit2c <- coxph(list(Surv(etime, event) ~ sex,
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+ "Entry":state(c("PCM", "death")) ~ age / common),
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+ istate=entry, data1, id=id)
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>
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> aeq(fit2b$loglik, fit2$loglik)
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[1] TRUE
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> aeq(fit2c$coef, fit2$coef)
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[1] TRUE
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>
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> # mspike size as a covariate for PCM only
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> # first, 4 different ways to write the same
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> fit3 <- coxph(list(Surv(etime, event) ~ age + sex,
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+ 1:state("PCM") ~ mspike),
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+ data1, id=id)
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> fit3b <- coxph(list(Surv(etime, event) ~ age + sex,
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+ 1:"PCM" ~ mspike),
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+ data1, id=id)
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> fit3c <- coxph(list(Surv(etime, event) ~ age + sex,
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+ 1:c("PCM") ~ mspike),
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+ data1, id=id)
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> fit3d <- coxph(list(Surv(etime, event) ~ age + sex + mspike,
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+ 1:3 ~ -mspike), data1, id=id)
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>
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> aeq(fit3b$coef, fit3$coef)
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[1] TRUE
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> aeq(fit3c$coef, fit3$coef)
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[1] TRUE
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> aeq(fit3d$coef, fit3$coef)
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[1] TRUE
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>
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> data3 <- data2
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> data3$mspike[data3$etype==2] <- 0
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> fit3a <- coxph(Surv(etime, status) ~ strata(etype)/(age + sex + mspike), data3)
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> aeq(fit3$loglik, fit3a$loglik)
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[1] TRUE
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> aeq(fit3$coef, fit3a$coef[c(1,3,5,2,4)])
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[1] TRUE
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>
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> # models with strata
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> test1 <- coxph(Surv(etime, event=="PCM") ~ age + mspike + strata(sex), data1)
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> test2 <- coxph(Surv(etime, event=="death") ~ age + strata(sex), data1)
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>
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> sfit1 <- coxph(list(Surv(etime, event) ~ age + strata(sex),
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+ 1:state("PCM") ~ mspike),
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+ data1, id=id)
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> aeq(coef(sfit1), c(coef(test1), coef(test2)))
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[1] TRUE
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>
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> test3 <- coxph(Surv(etime, event=="death") ~ age +sex, data1)
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> sfit2 <- coxph(list(Surv(etime, event) ~ age + sex,
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+ 1:2 ~ mspike + strata(sex) - sex), data1, id=id)
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> aeq(coef(sfit2), c(coef(test1), coef(test3)))
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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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1.749 0.125 1.900
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