Direct Bootstrapping and Permuting of Observations fail for Aalen-Johansen Estimators

Abstract

This article provides rigorous proofs that neither Efron's bootstrap nor permutation techniques can be applied directly to the observations to construct consistent resampling tests for transition probability matrices of finite-state Markov processes. These methods modify the covariance functions of the limiting distributions of the involved Aalen-Johansen processes, even in the case of fully observable individuals. An example for the failure of these resampling methods is given by cumulative incidence functions in competing risks set-ups.