jackknifeKME-package: Jackknife Estimates of Kaplan-Meier Estimators or Integrals

In jackknifeKME: Jackknife Estimates of Kaplan-Meier Estimators or Integrals

Description

Computing 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, 2015) 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.

Package:	jackknifeKME
Type:	Package
Version:	1.2
Date:	2015-10-23
License:	GPL-2
Depend:	imputeYn

Author(s)

Hasinur Rahaman Khan and Ewart Shaw Maintainer: Hasinur Rahaman Khan <[e-mail]>

References

Khan and Shaw (2015) imputeYn: Imputing the last largest censored observation/observations under weighted least squares. R package version 1.3, https://cran.r-project.org/package=imputeYn.

Khan and Shaw. (2013). On Dealing with Censored Largest Observations under Weighted Least Squares. CRiSM working paper, Department of Statistics, University of Warwick, UK, No. 13-07. Also available in http://arxiv.org/abs/1312.2533.

Khan and Shaw. (2015). Robust bias estimation for Kaplan-Meier Survival Estimator with Jackknifing. Journal of Statistical Theory and Practice, (published online; DOI:10.1080/15598608.2015.1062833). Also available in http://arxiv.org/abs/1312.4058.

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 (2015) package).
#For mean lifetime estimator.
data1<-data(n=100, p=4, r=0, b1=c(2,2,3,3), sig=1, Cper=0)
kme1<-jackknifeKME(data1$x,data1$y, data1$delta, method="condMean",estimator = 1)
kme1

Example output

Loading required package: imputeYn
Loading required package: quadprog
Loading required package: emplik
Loading required package: mvtnorm
Loading required package: survival
Loading required package: boot

Attaching package: 'boot'

The following object is masked from 'package:survival':

    aml


Attaching package: 'imputeYn'

The following object is masked from 'package:utils':

    data

$km.est
[1] 5.236043

$modkm.est
[1] 5.236043

$Jbias.kme
[1] 0

$Bcorr.Jkme
[1] 5.236043

$modJbias.kme
[1] 0

$Bcorr.modJkme
[1] 5.236043

attr(,"class")
[1] "jacknifeKME"

jackknifeKME documentation built on May 2, 2019, 5:27 a.m.