The analysis of multiple time-to-event outcomes in a randomised controlled clinical trial can be accomplished with exisiting methods. However, depending on the characteristics of the disease under investigation and the circumstances in which the study is planned, it may be of interest to conduct interim analyses and adapt the study design if necessary. Due to the expected dependency of the endpoints, the full available information on the involved endpoints may not be used for this purpose. We suggest a solution to this problem by embedding the endpoints in a multi-state model. If this model is Markovian, it is possible to take the disease history of the patients into account and allow for data-dependent design adaptiations. To this end, we introduce a flexible test procedure for a variety of applications, but are particularly concerned with the simultaneous consideration of progression-free survival (PFS) and overall survival (OS). This setting is of key interest in oncological trials. We conduct simulation studies to determine the properties for small sample sizes and demonstrate an application based on data from the NB2004-HR study.

翻译：在随机对照临床试验中，现有方法可实现对多时间至事件结局的分析。然而，根据所研究疾病的特征及研究规划时的具体情境，可能需要进行中期分析并在必要时调整研究设计。由于终点变量间预期的相依性，现有方法可能无法充分利用所有涉及的终点信息。我们提出一种解决方案，将终点变量嵌入多状态模型中。若该模型满足马尔可夫性，则可考虑患者的疾病史并实施数据依赖的设计调整。为此，我们引入一个适用于多种应用场景的灵活检验程序，特别关注无进展生存期（PFS）和总生存期（OS）的联合分析——该设定在肿瘤学试验中具有关键意义。我们通过模拟研究评估小样本情况下的性质，并基于NB2004-HR研究数据展示实际应用案例。