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，曾经致命的疾病正变得可管理。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和发生情况来量化。现有分析多事件时间数据的方法大多需要严格的建模假设，这些假设难以通过经验验证，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。为解决这些局限性，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，该指标是将限制平均生存时间扩展至多事件时间场景的度量。AUMCF具有非参数性、临床可解释性，并能恰当处理终末竞争风险。为实现协变量调整，我们还开发了一种增强估计量，其效率至少等于且通常超过未调整的估计量。通过大量模拟研究，并利用β受体阻滞剂生存评估试验（BEST）临床试验数据对多个心力衰竭相关终点进行分析，我们展示了AUMCF的实用性和可解释性。我们开源的R软件包MCC使得进行AUMCF分析变得简单易行。