  The Mann-Whitney effect is an intuitive measure for discriminating two
survival distributions. Here we analyze 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. From an asymptotic analysis of the latter, asymptotically exact
inference procedures based on standard normal, bootstrap- and
permutation-quantiles are developed and compared in simulations. Here, the
asymptotically robust and, in case of equal survival and censoring
distributions, even finitely exact permutation procedure turned out to be the
best. Finally, all procedures are illustrated using a real data set.
