Division of Pharmacometrics, OCP/OTS/CDER, US FDA.
Division of Biometrics II, OB/OTS/CDER, US FDA.
J Biopharm Stat. 2021 Mar;31(2):141-155. doi: 10.1080/10543406.2020.1814799. Epub 2020 Sep 6.
In a comparative longitudinal clinical study, multiple clinical events of interest are typically collected in timing and occurrence during the follow-up period. These clinical events are often indicative of disease burden over the study period and provide overall evidence of benefit/risk of one treatment relative to another. While these clinical events are usually used to form a composite endpoint, only the first occurrence of the composite endpoint event is considered in primary efficacy analysis. This type of analysis is commonly performed but it may not be ideal. Most of the existing methods for analyzing multiple event-time data were developed, relying on certain model assumptions. However, the assumptions may greatly affect the inferences for treatment effect. In this paper, we propose a simple, non-parametric estimator of conditional mean survival time for multiple events to quantify treatment effect which has clinically meaningful interpretation. We use simulation studies to evaluate the performance of the new method. Further, we apply this method to analyze the data from a cardiovascular clinical trial as an illustration.
在一项比较性纵向临床研究中，通常会在随访期间按时间和发生顺序收集多个感兴趣的临床事件。这些临床事件通常反映了研究期间的疾病负担，并提供了一种治疗方法相对于另一种治疗方法的获益/风险的总体证据。虽然这些临床事件通常用于形成复合终点，但在主要疗效分析中仅考虑复合终点事件的首次发生。这种类型的分析很常见，但可能并不理想。大多数现有的多事件时间数据分析方法都是基于某些模型假设开发的。然而，这些假设可能会极大地影响对治疗效果的推断。在本文中，我们提出了一种简单的、非参数的多事件条件生存时间估计器，用于量化具有临床意义解释的治疗效果。我们使用模拟研究来评估新方法的性能。此外，我们将该方法应用于心血管临床试验的数据进行分析，作为说明。
