@article {IOPORT.05975689,
    author       = {Albert, Jeffrey M. and Nelson, Suchitra},
    title        = {Generalized causal mediation analysis.},
    year         = {2011},
    journal      = {Biometrics},
    volume       = {67},
    number       = {3},
    issn         = {0006-341X},
    pages        = {1028-1038},
    publisher    = {Blackwell Publishing (Wiley), Malden, MA},
    doi          = {10.1111/j.1541-0420.2010.01547.x},
    abstract     = {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.},
    identifier   = {05975689},
}