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可比的事件动态，对于评估治疗等效性、合并不同队列的数据，或比较不同医院或策略间的临床路径至关重要。我们提出了一种统计框架，用于基于转移概率正式检验竞争风险模型的相似性，其中转移概率代表各事件随时间累积的风险。我们的方法定义了两个多状态过程转移概率矩阵之间的最大型距离，并采用一种新颖的约束参数自助法检验来评估在管理性右删失和随机右删失下的相似性。我们从理论上证明了该自助法检验的渐近有效性和一致性。通过广泛的模拟研究，我们表明该方法能可靠地控制第一类错误，并且比现有的基于强度的方法获得更高的统计功效。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，我们确定了具有和没有既往院内融合活检的患者表现出可比再入院动态的最小相似性阈值。所提出的方法为量化事件历史模型的相似性提供了一个稳健且可解释的工具。