Longitudinal Mediation Analysis with Time-varying Mediators and Exposures, with Application to Survival Outcomes

Journal of Causal Inference

article

Wenjing Zheng¹  Mark van der Laan² 

[1] Division of Biostatistics, University of California;Department of School of Public Health, University of California

关键词: natural direct effects;    natural indirect effects;    mediation analysis;    mediation formula;    survival;    time-varying;    targeted maximum likelihood estimator;    IPTW;    G-computation;   

DOI  :  10.1515/jci-2016-0006

来源: De Gruyter

【 摘 要 】

Abstract: 1 In this paper, we study the effect of a time-varying exposure mediated by a time-varying intermediate variable. We consider general longitudinal settings, including survival outcomes. At a given time point, the exposure and mediator of interest are influenced by past covariates, mediators and exposures, and affect future covariates, mediators and exposures. Right censoring, if present, occurs in response to past history. To address the challenges in mediation analysis that are unique to these settings, we propose a formulation in terms of random interventions based on conditional distributions for the mediator. This formulation, in particular, allows for well-defined natural direct and indirect effects in the survival setting, and natural decomposition of the standard total effect. Upon establishing identifiability and the corresponding statistical estimands, we derive the efficient influence curves and establish their robustness properties. Applying Targeted Maximum Likelihood Estimation, we use these efficient influence curves to construct multiply robust and efficient estimators. We also present an inverse probability weighted estimator and a nested non-targeted substitution estimator for these parameters.

【 授权许可】

CC BY