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options(na.action=na.exclude) # preserve missings
options(contrasts=c('contr.treatment', 'contr.poly')) #ensure constrast type
library(survival)
# Create a "counting process" version of the simplest test data set
#
test1 <- data.frame(time= c(9, 3,1,1,6,6,8),
status=c(1,NA,1,0,1,1,0),
x= c(0, 2,1,1,1,0,0))
test1b<- list(start= c(0, 3, 0, 0, 5, 0, 6,14, 0, 0, 10,20,30, 0),
stop = c(3,10, 10, 5,20, 6,14,20, 30, 10,20,30,40, 10),
status=c(0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0),
x= c(1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, NA),
id = c(3, 3, 4, 5, 5, 6, 6, 6, 7, 1, 1, 1, 1, 2))
aeq <- function(x,y) all.equal(as.vector(x), as.vector(y))
#
# Check out the various residuals under an Efron approximation
#
fit0 <- coxph(Surv(time, status)~ x, test1, iter=0)
fit <- coxph(Surv(time, status) ~x, test1)
fit0b <- coxph(Surv(start, stop, status) ~ x, test1b, iter=0)
fitb <- coxph(Surv(start, stop, status) ~x, test1b)
fitc <- coxph(Surv(time, status) ~ offset(fit$coef*x), test1)
fitd <- coxph(Surv(start, stop, status) ~ offset(fit$coef*x), test1b)
aeq(fit0b$coef, fit0$coef)
aeq(resid(fit0), resid(fit0b, collapse=test1b$id))
aeq(resid(fit), resid(fitb, collapse=test1b$id))
aeq(resid(fitc), resid(fitd, collapse=test1b$id))
aeq(resid(fitc), resid(fit))
aeq(resid(fit0, type='score'), resid(fit0b, type='score', collapse=test1b$id))
aeq(resid(fit, type='score'), resid(fitb, type='score', collapse=test1b$id))
aeq(resid(fit0, type='scho'), resid(fit0b, type='scho', collapse=test1b$id))
aeq(resid(fit, type='scho'), resid(fitb, type='scho', collapse=test1b$id))
# The two survivals will have different censoring times
# nrisk, nevent, surv, and std should be the same
temp1 <- survfit(fit, list(x=1), censor=FALSE)
temp2 <- survfit(fitb, list(x=1), censor=FALSE)
all.equal(unclass(temp1)[c(3,4,6,8)], unclass(temp2)[c(3,4,6,8)])
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