Mao Lu, Kim KyungMann
Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison.
Stat Biopharm Res. 2021;13(3):260-269. doi: 10.1080/19466315.2021.1927824. Epub 2021 Jul 6.
The proper analysis of composite endpoints consisting of both death and non-fatal events is an intriguing and sometimes contentious topic. The current practice of analyzing time to the first event often draws criticisms for ignoring the unequal importance between component events and for leaving recurrent-event data unused. Novel methods that address these limitations have recently been proposed. To compare the novel versus traditional approaches, we review three typical models for composite endpoints based on time to the first event, composite event process, and pairwise hierarchical comparisons. The pros and cons of these models are discussed with reference to the relevant regulatory guidelines, such as the recently released ICH-E9(R1) Addendum "Estimands and Sensitivity Analysis in Clinical Trials". We also discuss the impact of censoring when the model assumptions are violated and explore sensitivity analysis strategies. Simulation studies are conducted to assess the performance of the reviewed methods under different settings. As demonstration, we use publicly available R-packages to analyze real data from a major cardiovascular trial.
对由死亡和非致命事件组成的复合终点进行恰当分析是一个有趣且有时颇具争议的话题。当前对首次事件发生时间进行分析的做法常常受到批评，原因在于它忽视了组成事件之间重要性的不平等，且未利用复发事件数据。最近有人提出了能解决这些局限性的新方法。为比较新方法与传统方法，我们回顾了基于首次事件发生时间、复合事件过程和成对分层比较的三种典型复合终点模型。参照相关监管指南，如最近发布的ICH-E9(R1)附录《临床试验中的估计量和敏感性分析》，讨论了这些模型的优缺点。我们还讨论了模型假设被违反时删失的影响，并探索敏感性分析策略。进行了模拟研究以评估所回顾方法在不同设定下的性能。作为示范，我们使用公开可用的R包来分析一项大型心血管试验的真实数据。
