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.

在一项比较纵向临床研究中，通常在随访期间的时间安排和事件中收集多个感兴趣的临床事件。这些临床事件通常指示研究期间的疾病负担，并提供一种治疗相对于另一种治疗的获益/风险的总体证据。尽管通常将这些临床事件用于形成复合终点，但在主要功效分析中仅考虑复合终点事件的首次出现。通常执行这种类型的分析，但可能并不理想。大部分现有的用于分析多个事件时间数据的方法都是根据某些模型假设而开发的。但是，这些假设可能会大大影响对治疗效果的推论。在本文中，我们提出了一个简单的方法，多个事件的条件平均生存时间的非参数估计量，以量化治疗效果，具有临床意义。我们使用仿真研究来评估新方法的性能。此外，我们将这种方法应用于分析心血管临床试验的数据作为例证。