Confounding in statistical mediation analysis: What it is and how to address it.

Abstract

Psychology researchers are often interested in mechanisms underlying how randomized interventions affect outcomes such as substance use and mental health. Mediation analysis is a common statistical method for investigating psychological mechanisms that has benefited from exciting new methodological improvements over the last two decades. One of the most important new developments is methodology for estimating causal mediated effects using the potential outcomes framework for causal inference. Potential outcomes-based methods developed in epidemiology and statistics have important implications for understanding psychological mechanisms. We aim to provide a concise introduction to and illustration of these new methods and emphasize the importance of confounder adjustment. First, we review the traditional regression approach for estimating mediated effects. Second, we describe the potential outcome framework. Third, we define what a confounder is and how the presence of a confounder can provide misleading evidence regarding mechanisms of interventions. Fourth, we describe experimental designs that can help rule out confounder bias. Fifth, we describe new statistical approaches to adjust for measured confounders of the mediator – outcome relation and sensitivity analyses to probe effects of unmeasured confounders on the mediated effect. All approaches are illustrated with application to a real counseling intervention dataset. Counseling psychologists interested in understanding the causal mechanisms of their interventions can benefit from incorporating the most up-to-date techniques into their mediation analyses.

Internet-delivered cognitive-behavioral therapy for significant others of treatment-refusing problem gamblers: A randomized wait-list controlled trial.

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Mechanisms of change in psychological interventions for posttraumatic stress symptoms: A systematic review with recommendations

“…If researchers do not wish to adopt such an approach, justifications for the assumptions made in traditional mediation analysis, e.g., not considering interaction effects and lack of confounders in the mechanism to outcome relation, should be provided. Problems of mechanism-outcome confounding can be examined by sensitivity analysis for the indirect effect (Imai et al 2015), and a number of design-and analysis-based solutions to overcome them exist (MacKinnon and Pirlott 2015; Valente et al 2017).…”

Reduced insulin resistance partly mediated the association of high dietary magnesium intake with less metabolic syndrome in a large Chinese population

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