As standards of care advance, patients are living longer and once-fatal diseases are becoming manageable. Clinical trials increasingly focus on reducing disease burden, which can be quantified by the timing and occurrence of multiple non-fatal clinical events. Most existing methods for the analysis of multiple event-time data require stringent modeling assumptions that can be difficult to verify empirically, leading to treatment efficacy estimates that forego interpretability when the underlying assumptions are not met. Moreover, most existing methods do not appropriately account for informative terminal events, such as premature treatment discontinuation or death, which prevent the occurrence of subsequent events. To address these limitations, we derive and validate estimation and inference procedures for the area under the mean cumulative function (AUMCF), an extension of the restricted mean survival time to the multiple event-time setting. The AUMCF is nonparametric, clinically interpretable, and properly accounts for terminal competing risks. To enable covariate adjustment, we also develop an augmentation estimator that provides efficiency at least equaling, and often exceeding, the unadjusted estimator. The utility and interpretability of the AUMCF are illustrated with extensive simulation studies and through an analysis of multiple heart-failure-related endpoints using data from the Beta-Blocker Evaluation of Survival Trial (BEST) clinical trial. Our open-source R package MCC makes conducting AUMCF analyses straightforward and accessible.

翻译：随着医疗标准的进步，患者生存期延长，曾经致命的疾病逐渐转变为可管理的慢性病。临床试验日益关注减轻疾病负担，这可通过多个非致命临床事件的发生时间与频次进行量化。现有多数多事件时间数据分析方法需要严格的建模假设，这些假设往往难以通过经验验证，导致当基础假设不满足时，治疗效应估计量会丧失可解释性。此外，现有方法大多未能恰当处理信息性终末事件（如提前终止治疗或死亡），这些事件会阻止后续事件的发生。为解决这些局限，我们推导并验证了平均累积函数下面积（AUMCF）的估计与推断方法，该方法将限制平均生存时间扩展至多事件时间场景。AUMCF具有非参数特性、临床可解释性，并能妥善处理终末竞争风险。为实现协变量调整，我们还开发了一种增强估计量，其效率至少等同于且通常优于未调整估计量。通过大量模拟研究，并利用β受体阻滞剂生存评估试验（BEST）临床数据对多个心力衰竭相关终点进行分析，我们展示了AUMCF的实用性与可解释性。我们开源的R软件包MCC使AUMCF分析变得直观易行。