  In the context of the widely used competing risks set-up we discuss different
inference procedures for testing equality of two cumulative incidence
functions, where the data may be subject to independent right-censoring or
left-truncation. To this end we compare two-sample Kolmogorov-Smirnov- and
Cramer-von Mises-type test statistics. Since, in general, their corresponding
asymptotic limit distributions depend on unknown quantities, we utilize wild
bootstrap resampling as well as approximation techniques to construct adequate
test decisions. Here the latter procedures are motivated from testing
procedures for heteroscedastic factorial designs but have not yet been proposed
in the survival context. A simulation study shows the performance of all
considered tests under various settings and finally a real data example about
bloodstream infection during neutropenia is used to illustrate their
application.
