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.

翻译：识别相似患者路径是医疗保健分析中的关键任务。解决该问题的灵活工具是参数化竞争风险模型，其中转移强度可由多种参数分布指定，从而特别可能具有时间依赖性。我们通过检查不同健康状态之间的转移来评估两个此类模型之间的相似性。本研究引入了一种方法，用于衡量随时间变化的转移强度最大差异，进而开发了评估相似性的检验程序。为此，我们提出了一种参数化自助法，并提供了证明来确认该程序的有效性。通过模拟研究评估了我们提出方法的性能，考虑了不同样本量、不同删失程度以及多种相似性阈值。最后，我们通过一项泌尿科临床常规实践中的案例研究展示了该方法的应用，该研究正是本研究的灵感来源。