Estimating Pathway Treatment Effects in the Presence of Intermediate Events with Multi-State Data
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存在中间事件时利用多状态数据估计路径处理效应
- 研究问题
- 临床试验中处理可经多个中间事件影响生存结局时，如何定义和估计各条作用路径的效应，并处理由处理诱发的混杂？
- 方法与思路
- 作者对事件状态转移定义模拟处理机制的假想干预，推导反事实累积发生率的有效影响函数，构造多重稳健估计量。
- 材料陈述的贡献
- 摘要称框架可分析各转移、事件及路径上的效应，并提供半参数有效估计；试验应用指出死亡风险下降主要经心血管事件路径实现。
- 阅读关注点
- 应核对假想转移干预的可解释性、未观测处理诱发混杂的处理依据，以及识别条件、多重稳健的模型组合和删失处理。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未列完整识别条件，不能将路径效应直接等同自然间接效应或临床建议。
原文摘要
During clinical trials evaluating a drug's effect on a survival endpoint, intermediate events often occur in addition to the primary event. The treatment can exert its effect on the primary endpoint along multiple pathways through intermediate events. Assumptions for identifying mediation effects, such as sequential ignorability in natural effects or the dismissible components condition in separable effects, fail because intermediate events act as treatment-induced confounding. To understand the effect along each pathway, we consider hypothetical interventions in transitions between event statuses to mimic the treatment mechanism. The hypothetical interventions adjust for effects through intermediate events and marginalize over unobserved treatment-induced confounding, if any. Based on the derived efficient influence functions for the counterfactual cumulative incidences under hypothetical interventions, we construct multiply robust and semiparametrically efficient estimators for pathway treatment effects. Our proposed framework enables the examination of treatment effects through each transition, on each event, and along each path. By analyzing data from the LEADER Trial, we find that liraglutide significantly reduces the risk of cardiovascular and microvascular events. The reduction in all-cause mortality is primarily mediated by its effects on expanded major adverse cardiovascular events.