title: 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 effects, and (3) reference and mediated interaction effects, using Monte Carlo simulations in standard statistical software. For each study subject, we estimated their nested potential outcomes corresponding to the (mediated) effects of an intervention on the exposure wherein the mediator was allowed to attain the value it would have under a possible counterfactual exposure intervention, under a pre-specified distribution of the mediator independent of any causes, or under a fixed controlled value. A final regression of the potential outcome on the exposure intervention variable was used to compute point estimates and bootstrap was used to obtain confidence intervals. Through contrasting different potential outcomes, this analytical framework provides an intuitive way of estimating effects under the recently introduced 3- and 4-way effect decomposition. This framework can be extended to complex multivariable and longitudinal mediation settings.
authors.affiliation: Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles (UCLA), 650 Charles E. Young Drive South, Los Angeles, CA, 90095-1772, USA. aolinw@ucla.edu.|California Center for Population Research (CCPR), Los Angeles, CA, USA. aolinw@ucla.edu.
authors.forename: Aolin
authors.initials: A
authors.lastname: Wang
authors.affiliation: Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles (UCLA), 650 Charles E. Young Drive South, Los Angeles, CA, 90095-1772, USA.|California Center for Population Research (CCPR), Los Angeles, CA, USA.|UCLA Center for Health Policy Research, Los Angeles, CA, USA.
authors.forename: Onyebuchi A
authors.initials: OA
authors.lastname: Arah
mesh_terms: D015982:Bias; D015984:Causality; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D015987:Effect Modifier, Epidemiologic; D016021:Epidemiologic Studies; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D012044:Regression Analysis; D060891:Root Cause Analysis / Q000379:methods*; D012907:Smoking / Q000453:epidemiology / Q000401:mortality*
keywords: Effect decomposition; G-computation; Mediation analysis; Parametric g-formula; Simulation