# bootkm

##### 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.

##### See Also

##### 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 4.3-1, License: GPL (>= 2)*