library(survival4)
# Example 3.9 of Davison and Hinkley (1997) does a bootstrap on some
# remission times for patients with a type of leukaemia. The patients
# were divided into those who received maintenance chemotherapy and
# those who did not. Here we are interested in the median remission
# time for the two groups.
aml.fun <- function(data) {
surv <- survfit(Surv(time, cens)~group, data=data)
out <- NULL
st <- 1
for (s in 1:length(surv$strata)) {
inds <- st:(st+surv$strata[s]-1)
md <- min(surv$time[inds[1-surv$surv[inds]>=0.5]])
st <- st+surv$strata[s]
out <- c(out,md)
}
}
data(aml)
aml.case <- censboot(aml,aml.fun,R=499,strata=aml$group)
# Now we will look at the same statistic using the conditional
# bootstrap and the weird bootstrap. For the conditional bootstrap
# the survival distribution is stratified but the censoring
# distribution is not.
aml.s1 <- survfit(Surv(time,cens)~group, data=aml)
aml.s2 <- survfit(Surv(time-0.001*cens,1-cens)~1, data=aml)
aml.cond <- censboot(aml,aml.fun,R=499,strata=aml$group,
F.surv=aml.s1,G.surv=aml.s2,sim="cond")
# For the weird bootstrap we must redefine our function slightly since
# the data will not contain the group number.
aml.fun1 <- function(data,str) {
surv <- survfit(Surv(data[,1],data[,2])~str)
out <- NULL
st <- 1
for (s in 1:length(surv$strata)) {
inds <- st:(st+surv$strata[s]-1)
md <- min(surv$time[inds[1-surv$surv[inds]>=0.5]])
st <- st+surv$strata[s]
out <- c(out,md)
}
}
aml.wei <- censboot(cbind(aml$time,aml$cens),aml.fun1,R=499,
strata=aml$group, F.surv=aml.s1,sim="weird")
# Now for an example where a cox regression model has been fitted
# the data we will look at the melanoma data of Example 7.6 from
# Davison and Hinkley (1997). The fitted model assumes that there
# is a different survival distribution for the ulcerated and
# non-ulcerated groups but that the thickness of the tumour has a
# common effect. We will also assume that the censoring distribution
# is different in different age groups. The statistic of interest
# is the linear predictor. This is returned as the values at a
# number of equally spaced points in the range of interest.
data(melanoma)
library(splines)
library(modreg) # for smooth.spline
mel.cox <- coxph(Surv(time,status==1)~ns(thickness,df=4)+strata(ulcer),
data=melanoma)
mel.surv <- survfit(mel.cox)
agec <- cut(melanoma$age,c(0,39,49,59,69,100))
mel.cens <- survfit(Surv(time-0.001*(status==1),status!=1)~
strata(agec),data=melanoma)
mel.fun <- function(d) {
t1 <- ns(d$thickness,df=4)
cox <- coxph(Surv(d$time,d$status==1) ~ t1+strata(d$ulcer))
eta <- unique(cox$linear.predictors)
u <- unique(d$thickness)
sp <- smooth.spline(u,eta,df=20)
th <- seq(from=0.25,to=10,by=0.25)
predict.smooth.spline(sp,th)$y
}
mel.str<-cbind(melanoma$ulcer,agec)
# this is slow!
mel.mod <- censboot(melanoma,mel.fun,R=999,F.surv=mel.surv,
G.surv=mel.cens,cox=mel.cox,strata=mel.str,sim="model")
proc.time()
# To plot the original predictor and a 95\% pointwise envelope for it
mel.env <- envelope(mel.mod)$point
plot(seq(0.25,10,by=0.25),mel.env[1,], ylim=c(-2,2),
xlab="thickness (mm)", ylab="linear predictor",type="n")
lines(seq(0.25,10,by=0.25),mel.env[1,],lty=2)
lines(seq(0.25,10,by=0.25),mel.env[2,],lty=2)
lines(seq(0.25,10,by=0.25),mel.mod$t0,lty=1)Run the code above in your browser using DataLab