Assessing whether two patient populations exhibit comparable event dynamics is essential for evaluating treatment equivalence, pooling data across cohorts, or comparing clinical pathways across hospitals or strategies. We introduce a statistical framework for formally testing the similarity of competing risks models based on transition probabilities, which represent the cumulative risk of each event over time. Our method defines a maximum-type distance between the transition probability matrices of two multistate processes and employs a novel constrained parametric bootstrap test to evaluate similarity under both administrative and random right censoring. We theoretically establish the asymptotic validity and consistency of the bootstrap test. Through extensive simulation studies, we show that our method reliably controls the type I error and achieves higher statistical power than existing intensity-based approaches. Applying the framework to routine clinical data of prostate cancer patients treated with radical prostatectomy, we identify the smallest similarity threshold at which patients with and without prior in-house fusion biopsy exhibit comparable readmission dynamics. The proposed method provides a robust and interpretable tool for quantifying similarity in event history models.

翻译：评估两个患者群体是否表现出相似的事件动态，对于检验治疗等效性、跨队列数据合并，或比较不同医院或策略的临床路径至关重要。我们提出一个基于转移概率（即每个事件随时间累积的风险）来正式检验竞争风险模型相似性的统计框架。该方法定义了多状态过程转移概率矩阵之间的最大型距离，并采用新型约束参数自举检验来评估在管理删失和随机右删失两种情况下的相似性。我们从理论上证明了该自举检验的渐近有效性和一致性。通过广泛的模拟研究，我们展示了该方法能可靠地控制第一类错误，且比现有的基于强度的检验方法具有更高的统计功效。将该框架应用于接受根治性前列腺切除术的前列腺癌患者的常规临床数据，我们确定了院内融合活检与未行该活检患者的再入院动态呈现可比性的最小相似性阈值。所提出的方法为量化事件历史模型中的相似性提供了稳健且可解释的工具。