Generalized Causal Mediation Analysis
Article first published online: 9 FEB 2011
© 2011, The International Biometric Society
Volume 67, Issue 3, pages 1028–1038, September 2011
How to Cite
Albert, J. M. and Nelson, S. (2011), Generalized Causal Mediation Analysis. Biometrics, 67: 1028–1038. doi: 10.1111/j.1541-0420.2010.01547.x
- Issue published online: 14 SEP 2011
- Article first published online: 9 FEB 2011
- Received August 2009. Revised November 2010. Accepted November 2010.
- G-computation algorithm;
- Generalized linear model;
- Path analysis;
- Potential outcome;
- Sensitivity analysis
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.