Estimates and C.I. of percentiles for a survival function

Question

In S-plus estimates of percentiles for a survival function can be obtained using the qkaplanMeier function (on the results of a call to kaplanMeier) like that:

kfit <-kaplanMeier(censor(TIME,STATUS)~1)
qkaplanMeier(kfit, c(.25, .5, .75))

How can I do this in R?. Those functions do not exist anymore. What if I also want the (asymptotic) confidence intervals for the percentiles? How can I get the mean for the survival time?

Answer 1

The CRAN task view on survival analysis says:

Kaplan-Meier: The survfit function from the survival package computes the Kaplan-Meier estimator for truncated and/or censored data. rms (replacement of the Design package) proposes a modified version of the survfit function. The prodlim package implements a fast algorithm and some features not included in survival. Various confidence intervals and confidence bands for the Kaplan-Meier estimator are implemented in the km.ci package. plot.Surv of package eha plots the Kaplan-Meier estimator. svykm in survey provides a weighted Kaplan-Meier estimator. nested.km in NestedCohort estimates the survival curve for each level of categorical variables with missing data. The kaplan-meier function in spatstat computes the Kaplan-Meier estimator from histogram data. The MAMSE package permits to compute a weighted Kaplan-Meier estimate. The KM function in package rhosp plots the survival function using a variant of the Kaplan-Meier estimator in a hospitalisation risk context. The survPresmooth package computes presmoothed estimates of the main quantities used for right-censored data, i.e., survival, hazard and density functions.

Answer 2

The bootkm() function in Hmisc provides bootstraped estimate of the probability of survival, as well as the estimate of the quantile of the survival distribution (through either describe or quantile applied onto the result of bootkm).