Causal mediation analysis on failure time outcome without sequential ignorability

Cheng Zheng () and Xiao-Hua Zhou
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Cheng Zheng: University of Wisconsin
Xiao-Hua Zhou: University of Washington

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2017, vol. 23, issue 4, No 2, 533-559

Abstract: 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.

Keywords: Additive hazard model; Causal inference; Inverse censoring probability weighting; Generalized estimating equation; 62N01; 62N02; 62P10 (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s10985-016-9377-9

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