jackknifeKME (version 1.0)

jackknifeKME-package: Jackknife estimates of Kaplan-Meier estimators or integrals.

Description

The package is for computing both the original and modified jackknife estimates of Kaplan-Meier estimators.

Details

For computing bias of Kaplan-Meier survival estimators the jackknifing (Stute and Wang, 1994) is a natural choice among the researchers because it reduces bias substantially. The package provides the original (Stute and Wang, 1994) and the modified (Khan and Shaw, 2012b) jackknife estimates for Kaplan-Meier estimators and their corresponding variances. The package also compute bias corrected jackknife estimates for Kaplan-Meier estimators under both approaches. ll{ Package: jackknifeKME Type: Package Version: 1.0 Date: 2013-02-22 License: GPL-2 Depend: imputeYn (>= 1.1) }

References

Khan, M. H. R. and Shaw, J. E. H. (2012a). On dealing with censored largest observations under weighted least squares (Preprint). Khan, M. H. R. and Shaw, J. E. H. (2012b). Robust bias estimation for Kaplan-Meier survival estimator with jackknifing (Preprint). Stute, W. and Wang, J. (1994). The jackknife estimate of a Kaplan-Meier integral. Biometrika 81, 602-606. Stute, W. (1993). Consistent estimation under random censorship when covariables are available. Journal of Multivariate Analysis, 45, 89-103.

Examples

#For full data typically used for AFT models (using imputeYn package).
#For mean lifetime estimator.
data<-data(n=100, p=4, r=0, b1=c(2,2,3,3), sig=1, Cper=0)
kme1<-jackknifeKME(data$x,data$y, data$delta, method="condMean",estimator = 1)
kme1