Resampling plays a versatile role in every branch of statistics. For example, it is used to compute variance estimates, to avoid the suboptimal normal distribution approximation in inference methods, or as bagging (bootstrap aggregating) in random forest algorithms. There exists an abundance of resampling methods, in particular, the classical, weighted, multiplier, and parametric bootstraps, and random permutation.

In survival analysis, the issue of incomplete data demands special attention when it comes to resampling. For instance, Akritas (1986, JASA) found that Efron’s suggestion (1981, JASA) to draw with replacement from the censored data points before recomputing the Kaplan-Meier estimator allows the construction of asymptotically valid confidence bands for the survival function. And also that, in contrast, Reid’s approach (1981, Biometrika) to independently resample from the Kaplan-Meier curve alters the covariance structure of the resampled Kaplan-Meier estimator; hence, it should not be used for constructing such confidence bands.

This talk will provide an incomplete overview of various resampling procedures in different survival analytic applications, address some requirements and intuitions behind these procedures, and also discuss some computational aspects.