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      Causal Mediation Analysis With Observational Data: Considerations and Illustration Examining Mechanisms Linking Neighborhood Poverty to Adolescent Substance Use

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          Abstract

          Understanding the mediation mechanisms by which an exposure or intervention affects an outcome can provide a look into what has been called a “black box” of many epidemiologic associations, thereby providing further evidence of a relationship and possible points of intervention. Rapid methodologic developments in mediation analyses mean that there are a growing number of approaches for researchers to consider, each with its own set of assumptions, advantages, and disadvantages. This has understandably resulted in some confusion among applied researchers. Here, we provide a brief overview of the mediation methods available and discuss points for consideration when choosing a method. We provide an in-depth explication of 2 of the many potential estimators for illustrative purposes: the Baron and Kenny mediation approach, because it is the most commonly used, and a recently developed approach for estimating stochastic direct and indirect effects, because it relies on far fewer assumptions. We illustrate the decision process and analytical procedure by estimating potential school- and peer-based mechanisms linking neighborhood poverty to adolescent substance use in the National Comorbidity Survey Adolescent Supplement.

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          Most cited references40

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          Mediation Analysis: A Practitioner's Guide

          This article provides an overview of recent developments in mediation analysis, that is, analyses used to assess the relative magnitude of different pathways and mechanisms by which an exposure may affect an outcome. Traditional approaches to mediation in the biomedical and social sciences are described. Attention is given to the confounding assumptions required for a causal interpretation of direct and indirect effect estimates. Methods from the causal inference literature to conduct mediation in the presence of exposure-mediator interactions, binary outcomes, binary mediators, and case-control study designs are presented. Sensitivity analysis techniques for unmeasured confounding and measurement error are introduced. Discussion is given to extensions to time-to-event outcomes and multiple mediators. Further flexible modeling strategies arising from the precise counterfactual definitions of direct and indirect effects are also described. The focus throughout is on methodology that is easily implementable in practice across a broad range of potential applications.
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            Process Analysis: Estimating Mediation in Treatment Evaluations

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              Experimental Analysis of Neighborhood Effects

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                Author and article information

                Contributors
                Journal
                American Journal of Epidemiology
                Oxford University Press (OUP)
                0002-9262
                1476-6256
                March 2019
                March 01 2019
                December 18 2018
                March 2019
                March 01 2019
                December 18 2018
                : 188
                : 3
                : 598-608
                Affiliations
                [1 ]Department of Emergency Medicine, University of California, Davis, Sacramento, California
                [2 ]Division of Epidemiology, University of California, Berkeley, Berkeley, California
                [3 ]Division of Genetic Epidemiology, National Institute of Mental Health, Bethesda, Maryland
                [4 ]Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
                [5 ]Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
                [6 ]Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
                Article
                10.1093/aje/kwy248
                6395164
                30561500
                913e94e7-35fc-486b-87c8-c8d5d4821d26
                © 2018
                History

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