title: Causal mediation analysis on failure time outcome without sequential ignorability.
abstract: Mediation analysis is an important topic as it helps researchers to understand why an intervention works. Most previous mediation analyses define effects in the mean scale and require a binary or continuous outcome. Recently, possible ways to define direct and indirect effects for causal mediation analysis with survival outcome were proposed. However, these methods mainly rely on the assumption of sequential ignorability, which implies no unmeasured confounding. To handle the potential confounding between the mediator and the outcome, in this article, we proposed a structural additive hazard model for mediation analysis with failure time outcome and derived estimators for controlled direct effects and controlled mediator effects. Our methods allow time-varying effects. Simulations showed that our proposed estimator is consistent in the presence of unmeasured confounding while the traditional additive hazard regression ignoring unmeasured confounding produces biased results. We applied our method to the Women's Health Initiative data to study whether the dietary intervention affects breast cancer risk through changing body weight.
authors.affiliation: Joseph J. Zilber School of Public Health, University of Wisconsin, 1240 North 10th Street, Room 378, Milwaukee, WI, 53205, USA. zhengc@uwm.edu.
authors.forename: Cheng
authors.identifier: 0000-0002-6562-870X
authors.initials: C
authors.lastname: Zheng
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA, USA.
authors.forename: Xiao-Hua
authors.initials: XH
authors.lastname: Zhou
mesh_terms: D001943:Breast Neoplasms / Q000453:epidemiology / Q000517:prevention & control; D015984:Causality; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D018752:Diet, Fat-Restricted; D005260:Female; D006801:Humans; D015993:Life Tables; D016016:Proportional Hazards Models; D012044:Regression Analysis
keywords: Additive hazard model; Causal inference; Generalized estimating equation; Inverse censoring probability weighting