WKME {MAMSE}R Documentation

Kaplan-Meier Estimate

Description

Computes the weighted Kaplan-Meier estimate over some time points with optional confidence intervals.

Usage

WKME(x,ub,lb=0,time=NULL,boot=NULL,REP=1000)

Arguments

x A list of m samples. Each element is an n by 2 matrix whose second column is an indicator of whether the time in column 1 is observed (1) or censored (0).
lb,ub Lower and upper bounds of the integral of the MAMSE criterion.
time A vector of times at which to compute the Kaplan-Meier estimate.
boot When NULL, bootstrap confidence intervals are not generated. Otherwise must be a number in (0,1) corresponding to the coverage probability of the bootstrap intervals to be built.
REP When bootstrap is used, controls the number of pseudo-sample to generate.

Details

This function calculates the weighted Kaplan-Meier estimate and can provide pointwise bootstrap confidence intervals.

Value

List of elements:

x Sorted list of the times (observed and censored) from each samples
weight The size of the jump that the Kaplan-Meier estimate allocates to each time in x.
time Vector of time points where the function is evaluated.
kme The Kaplan-Meier estimate for Population 1 evaluated at time.
kmeCI Pointwise bootstrap confidence interval for kme.
wkme The weighted Kaplan-Meier estimate evaluated at time.
wkmeCI Pointwise bootstrap confidence interval for wkme.

References

J.-F. Plante (2007). Adaptive Likelihood Weights and Mixtures of Empirical Distributions. Unpublished doctoral dissertation, University of British Columbia.

J.-F. Plante (2009). About an adaptively weighted Kaplan-Meier estimate. Lifetime Data Analysis. Under revision.

See Also

MAMSE-package, WKME.

Examples

set.seed(2009)
x=list(
  cbind(rexp(20),sample(c(0,1),20,replace=TRUE)),
  cbind(rexp(50),sample(c(0,1),50,replace=TRUE)),
  cbind(rexp(200),sample(c(0,1),200,replace=TRUE))
)

allx=pmin(1,c(x[[1]][x[[1]][,2]==1,1],x[[2]][x[[2]][,2]==1,1],
   x[[3]][x[[3]][,2]==1,1]))
K=WKME(x,1,time=sort(unique(c(0,1,allx,allx-.0001))),boot=.9)

plot(K$time,K$wkme,type='l',col="blue",xlab="x",
   ylab="P(X<=x)",ylim=c(0,1))
lines(K$time,K$kme[,1],col="red")

lines(K$time,K$wkmeCI[1,],lty=2,col="blue")
lines(K$time,K$wkmeCI[2,],lty=2,col="blue")

lines(K$time,K$kmeCI[1,],lty=2,col="red")
lines(K$time,K$kmeCI[2,],lty=2,col="red")
legend(.1,.9,c("Weighted Kaplan-Meier","Kaplan-Meier"),
   col=c("blue","red"),lty=c(1,1))


[Package MAMSE version 0.1-1 Index]