Data Engineering and BI courses are free this week!
times variable) or the estimate of the q
quantile of the survival distribution (e.g., median survival time, the
default).
bootkm(S, q=0.5, B=500, times, pr=TRUE)Surv object for possibly right-censored survival time
q and
times, and if times is specified q is ignored.
FALSE to suppress printing the iteration number every
10 iterations
B bootstrap estimates
.Random.seed, and, if pr=TRUE, prints progress
of simulationsbootkm uses Therneau's survfitKM function to efficiently
compute Kaplan-Meier estimates.
survfit, Surv,
Survival.cph, Quantile.cph
# Compute 0.95 nonparametric confidence interval for the difference in
# median survival time between females and males (two-sample problem)
set.seed(1)
library(survival)
S <- Surv(runif(200)) # no censoring
sex <- c(rep('female',100),rep('male',100))
med.female <- bootkm(S[sex=='female',], B=100) # normally B=500
med.male <- bootkm(S[sex=='male',], B=100)
describe(med.female-med.male)
quantile(med.female-med.male, c(.025,.975), na.rm=TRUE)
# na.rm needed because some bootstrap estimates of median survival
# time may be missing when a bootstrap sample did not include the
# longer survival times
Run the code above in your browser using DataLab