Bootstrap Kaplan-Meier Estimates
Bootstraps Kaplan-Meier estimate of the probability of survival to at
least a fixed time (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)
S |
a |
q |
quantile of survival time, default is 0.5 for median |
B |
number of bootstrap repetitions (default=500) |
times |
time vector (currently only a scalar is allowed) at which to compute
survival estimates. You may specify only one of |
pr |
set to |
bootkm uses Therneau's survfitKM function to efficiently
compute Kaplan-Meier estimates.
a vector containing B bootstrap estimates
updates .Random.seed, and, if pr=TRUE, prints progress
of simulations
Frank Harrell
Department of Biostatistics
Vanderbilt University School of Medicine
fh@fharrell.com
Akritas MG (1986): Bootstrapping the Kaplan-Meier estimator. JASA 81:1032–1038.
# 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 timesPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.