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Generalized Causal Mediation Analysis
Author(s) -
Albert Jeffrey M.,
Nelson Suchitra
Publication year - 2011
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2010.01547.x
Subject(s) - categorical variable , counterfactual thinking , mediation , directed acyclic graph , outcome (game theory) , estimator , econometrics , causal inference , context (archaeology) , path analysis (statistics) , covariate , confidence interval , statistics , causal model , computer science , mathematics , psychology , algorithm , social psychology , paleontology , mathematical economics , political science , law , biology
Summary The goal of mediation analysis is to assess direct and indirect effects of a treatment or exposure on an outcome. More generally, we may be interested in the context of a causal model as characterized by a directed acyclic graph (DAG), where mediation via a specific path from exposure to outcome may involve an arbitrary number of links (or “stages”). Methods for estimating mediation (or pathway) effects are available for a continuous outcome and a continuous mediator related via a linear model, while for a categorical outcome or categorical mediator, methods are usually limited to two‐stage mediation. We present a method applicable to multiple stages of mediation and mixed variable types using generalized linear models. We define pathway effects using a potential outcomes framework and present a general formula that provides the effect of exposure through any specified pathway. Some pathway effects are nonidentifiable and their estimation requires an assumption regarding the correlation between counterfactuals. We provide a sensitivity analysis to assess the impact of this assumption. Confidence intervals for pathway effect estimates are obtained via a bootstrap method. The method is applied to a cohort study of dental caries in very low birth weight adolescents. A simulation study demonstrates low bias of pathway effect estimators and close‐to‐nominal coverage rates of confidence intervals. We also find low sensitivity to the counterfactual correlation in most scenarios.