  The identification of similar patient pathways is a crucial task in
healthcare analytics. A flexible tool to address this issue are parametric
competing risks models, where transition intensities may be specified by a
variety of parametric distributions, thus in particular being possibly
time-dependent. We assess the similarity between two such models by examining
the transitions between different health states. This research introduces a
method to measure the maximum differences in transition intensities over time,
leading to the development of a test procedure for assessing similarity. We
propose a parametric bootstrap approach for this purpose and provide a proof to
confirm the validity of this procedure. The performance of our proposed method
is evaluated through a simulation study, considering a range of sample sizes,
differing amounts of censoring, and various thresholds for similarity. Finally,
we demonstrate the practical application of our approach with a case study from
urological clinical routine practice, which inspired this research.
