Estimating Pathway Treatment Effects in the Presence of Intermediate Events with Multi-State Data

2026-08-23

AI Summary

本文通过假设干预和半参数有效估计方法解决临床试验中因中介事件导致的治疗效果评估问题，以理解药物对生存终点的影响路径。

Abstract

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.

Problem

• Pathway Treatment Effects
• Intermediate Events
• Multi-State Data
• Treatment-Induced Confounding

Innovation

• hypothetical interventions
• pathway treatment effects
• multi-state data
• intermediate events
• multiply robust estimators