基于多状态数据估算存在中间事件时的通路治疗效应
Estimating Pathway Treatment Effects in the Presence of Intermediate Events with Multi-State Data
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中文总结 AI 辅助
针对多状态数据中存在治疗诱导混杂的中间事件，提出多重稳健半参数有效估计量以估算通路治疗效应，分析利拉鲁肽在 LEADER 试验中对心血管等事件的效应及中介机制。
中文摘要 AI 辅助
在评估药物对生存终点影响的临床试验中，除主要事件外常出现中间事件。治疗可通过中间事件沿多条通路对主要终点产生影响。由于中间事件会导致治疗诱导混杂，用于识别中介效应的假设（如自然效应中的顺序可忽略性或可分离效应中的可排除成分条件）不再成立。为理解每条通路的效应，我们考虑事件状态转移间的假设干预，以模拟治疗机制；该假设干预可调整通过中间事件的效应，并对未观测到的治疗诱导混杂（若存在）进行边缘化处理。基于假设干预下反事实累积 incidence 的推导有效影响函数，我们构建了通路治疗效应的多重稳健半参数有效估计量。所提框架可检验每条转移、每个事件及每条通路的治疗效应。通过分析 LEADER 试验数据，我们发现利拉鲁肽（liraglutide）可显著降低心血管及微血管事件风险，全因死亡率的降低主要由其对扩展型主要不良心血管事件的效应介导。
英文摘要
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.
