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The identification of similar patient pathways is a crucial task inhealthcare analytics. A flexible tool to address this issue are parametriccompeting risks models, where transition intensities may be specified by avariety of parametric distributions, thus in particular being possiblytime-dependent. We assess the similarity between two such models by examiningthe transitions between different health states. This research introduces amethod to measure the maximum differences in transition intensities over time,leading to the development of a test procedure for assessing similarity. Wepropose a parametric bootstrap approach for this purpose and provide a proof toconfirm the validity of this procedure. The performance of our proposed methodis 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 fromurological clinical routine practice, which inspired this research.

Testing similarity of parametric competing risks models for identifying potentially similar pathways in healthcare