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