bootkm

0th

Percentile

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).

Keywords
nonparametric, survival
Usage
bootkm(S, q=0.5, B=500, times, pr=TRUE)
Arguments
S

a Surv object for possibly right-censored survival time

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 q and times, and if times is specified q is ignored.

pr

set to FALSE to suppress printing the iteration number every 10 iterations

Details

bootkm uses Therneau's survfitKM function to efficiently compute Kaplan-Meier estimates.

Value

a vector containing B bootstrap estimates

Side Effects

updates .Random.seed, and, if pr=TRUE, prints progress of simulations

References

Akritas MG (1986): Bootstrapping the Kaplan-Meier estimator. JASA 81:1032--1038.

survfit, Surv, Survival.cph, Quantile.cph

• bootkm
Examples
# NOT RUN {
# 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
# }

Documentation reproduced from package Hmisc, version 4.3-0, License: GPL (>= 2)

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