Nonparametric estimation of the total treatment effect with multiple outcomes in the presence of terminal events
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终末事件存在时多结局总处理效应的非参数估计
- 研究问题
- 当死亡或治疗终止阻止后续非致命事件发生时，如何用可解释的指标衡量多事件疾病负担及总体处理效果？
- 方法与思路
- 作者以平均累积函数下的面积为目标，构建考虑终末竞争风险的非参数估计与推断，并提出协变量调整的增广估计。
- 材料陈述的贡献
- 摘要称，该指标将限制平均生存时间推广到多事件情形，增广估计效率至少等于未调整估计，并提供模拟与试验应用。
- 阅读关注点
- 应核对面积指标的时间范围、终末事件与删失处理、总处理效应的定义，以及增广估计效率结论的适用条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收未核实；摘要未说明删失等条件，效率结论也不能据此推广到任意应用。
原文摘要
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, many 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 clinically interpretable, properly accounts for terminal competing risks, and can be estimated nonparametrically. 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.