Bootstrap- and permutation-based inference for the Mann-Whitney effect for right-censored and tied data

The Mann-Whitney effect is an intuitive measure for discriminating two survival distributions. Here we analyse various inference techniques for this parameter in a two-sample survival setting with independent right-censoring, where the survival times are even allowed to be discretely distributed. This allows for ties in the data and requires the introduction of normalized versions of Kaplan-Meier estimators from which adequate point estimates are deduced. Asymptotically exact inference procedures based on standard normal, bootstrap, and permutation quantiles are developed and compared in simulations. Here, the asymptotically robust andunder exchangeable dataeven finitely exact permutation procedure turned out to be the best. Finally, all procedures are illustrated using a real data set.