G-computation demonstration in causal mediation analysis.

ABSTRACT: Recent work has considerably advanced the definition, identification and estimation of controlled direct, and natural direct and indirect effects in causal mediation analysis. Despite the various estimation methods and statistical routines being developed, a unified approach for effect estimation under different effect decomposition scenarios is still needed for epidemiologic research. G-computation offers such unification and has been used for total effect and joint controlled direct effect estimation settings, involving different types of exposure and outcome variables. In this study, we demonstrate the utility of parametric g-computation in estimating various components of the total effect, including (1) natural direct and indirect effects, (2) standard and stochastic controlled direct

SUBMITTER: Wang A 

PROVIDER: S-EPMC4674449 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature