Robust bias estimation for Kaplan–Meier survival estimator with jackknifing

摘要

For studying or reducing the bias of functionals of the Kaplan-Meier survivalestimator, the jackknifing approach of Stute and Wang (1994) is natural. Wehave studied the behavior of the jackknife estimate of bias under differentconfigurations of the censoring level, sample size, and the censoring andsurvival time distributions. The empirical research reveals some new findingsabout robust calculation of the bias, particularly for higher censoring levels.We have extended their jackknifing approach to cover the case where the largestobservation is censored, using the imputation methods for the largestobservations proposed in Khan and Shaw (2013b). This modification to theexisting formula reduces the number of conditions for creating jackknife biasestimates to one from the original two, and also avoids the problem that theKaplan--Meier estimator can be badly underestimated by the existing jackknifeformula.