Statistical models for composite endpoints of death and non-fatal events: a review.
Statistical models for composite endpoints of death and non-fatal events: a review.
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DOI：
10.1080/19466315.2021.1927824
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发表时间：
2021
影响因子：
1.8
通讯作者：
Kim K
中科院分区：
文献类型：
--
作者：
Mao L;Kim K
对由死亡和非致命事件组成的复合终点进行正确分析是一个有趣且有时有争议的话题。当前分析第一个事件的时间的做法经常招致批评，因为它忽视了组成事件之间的不平等重要性，并且没有使用重复事件数据。最近提出了解决这些限制的新方法。为了比较新颖的方法与传统的方法，我们回顾了基于第一个事件的时间、复合事件过程和成对层次比较的复合终点的三种典型模型。这些模型的优缺点将参考相关监管指南进行讨论，例如最近发布的ICH-E9(R1)附录“临床试验中的估计值和敏感性分析”。我们还讨论了模型假设被违反时审查的影响，并探索了敏感性分析策略。进行模拟研究以评估所审查方法在不同设置下的性能。作为演示，我们使用公开的 R 包来分析一项主要心血管试验的真实数据。
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.
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