Estimating Treatment Effects Through Intermediate-Event Pathways with Multi-State Data
Abstract
During clinical trials evaluating a drug's effect on a survival endpoint, intermediate events often occur before the primary event. The treatment can affect the primary event through multiple pathways via intermediate events. With multiple intermediate events, each is a treatment-induced confounder of transitions out of the others, so we cannot identify natural or separable effects. We instead consider stochastic interventions that assign each transition the identifiable observed-history hazard of either arm. Contrasts between the resulting counterfactual cumulative incidences define transition-, event-, and path-targeted effects, identifiable without cross-world or isolation assumptions. Based on the efficient influence functions for the counterfactual cumulative incidences under hypothetical interventions, we construct multiply robust and semiparametrically efficient estimators for these targeted treatment effects. Our proposed framework quantifies 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.
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BibTeX
@misc{deng2026estimating,
title = {{Estimating Treatment Effects Through Intermediate-Event Pathways with Multi-State Data}},
author = {Yuhao Deng and Haoyu Wei and Donglin Zeng and Rui Song and Xiao-Hua Zhou},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2608.22608},
eprint = {2608.22608},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.22608},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Estimating Treatment Effects Through Intermediate-Event Pathways with Multi-State Data
AU - Yuhao Deng
AU - Haoyu Wei
AU - Donglin Zeng
AU - Rui Song
AU - Xiao-Hua Zhou
PY - 2026
DA - 2026/11/05
PB - arXiv
DO - 10.48550/arxiv.2608.22608
UR - https://arxiv.org/abs/2608.22608
AB - During clinical trials evaluating a drug's effect on a survival endpoint, intermediate events often occur before the primary event. The treatment can affect the primary event through multiple pathways via intermediate events. With multiple intermediate events, each is a treatment-induced confounder of transitions out of the others, so we cannot identify natural or separable effects. We instead consider stochastic interventions that assign each transition the identifiable observed-history hazard of either arm. Contrasts between the resulting counterfactual cumulative incidences define transition-, event-, and path-targeted effects, identifiable without cross-world or isolation assumptions. Based on the efficient influence functions for the counterfactual cumulative incidences under hypothetical interventions, we construct multiply robust and semiparametrically efficient estimators for these targeted treatment effects. Our proposed framework quantifies 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.
ER -