Generalized Causal Mediation Analysis

• Published in: Biometrics Vol. 67; no. 3; pp. 1028 - 1038
• Main Authors: Albert, Jeffrey M., Nelson, Suchitra
• Format: Journal Article
• Language: English
• Published: Malden, USA Blackwell Publishing Inc 01.09.2011; Wiley-Blackwell; Blackwell Publishing Ltd
• Subjects: Adolescent; adolescents; BIOMETRIC METHODOLOGY; Biometrics; biometry; Causality; Cohort Studies; Confidence interval; Copula; Dental Caries; Dental models; Estimation methods; Expected values; G-computation algorithm; Generalized linear model; Graphs; Humans; Infant, Low Birth Weight; Infant, Newborn; Inference; linear models; Longitudinal Studies; low birth weight; Mediation; Modeling; Parametric models; Path analysis; Potential outcome; Root Cause Analysis - methods; Sealants; Sensitivity analysis; Tooth enamel; Treatment Outcome
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/j.1541-0420.2010.01547.x

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.