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Activity Number: 338
Type: Topic Contributed
Date/Time: Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #309122
Title: A Stochastic Intervention Approach to Causal Mediation in a Survival Setting
Author(s): Wenjing Zheng*+ and Mark Van der Laan
Companies: University of California, Berkeley and UC Berkeley - Biostatistics
Keywords: causal inference ; survival ; mediation
Abstract:

The effect of an exposure on an outcome of interest is often mediated by intermediate variables. The goal of causal mediation analysis is to evaluate the role of these intermediate variables (mediators) in the causal effect of the exposure on the outcome. In this talk, we consider causal mediation of a baseline exposure on a survival (or time-to-event) outcome, when the mediator is time-dependent. The challenge in this setting lies in that the event process takes places jointly with the mediator process; in particular, the length of the mediator history depends on the survival time. As a result, we propose to use a stochastic interventions perspective, introduced by Didelez, Dawid, and Geneletti (2006), to formulate the causal mediation analysis problem in this setting. Under this formulation, the mediators are regarded as intervention variables, onto which a given counterfactual distribution is enforced. The natural direct and indirect effects can be defined analogously to the ideas in Pearl (2001). In particular, they also allow for total effect decomposition and an interpretation of the natural direct effect as a weighted average of controlled direct effects.


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