• Thaddäus Tönnies, Sabrina Schlesinger, Alexander Lang, and Oliver Kuss.
• German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at the Heinrich-Heine University Düsseldorf, Institute for Biometrics and Epidemiology; German Center for Diabetes Research, Partner Düsseldorf, München-Neuherberg, Germany; Centre for Health and Society, Medical Faculty and University Hospital of Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf.
• Dtsch Arztebl Int. 2023 Oct 13; 120 (41): 681687681-687.

BackgroundMediation analysis addresses the question of the mechanisms by which an exposure causes an outcome. This article is intended to convey basic knowledge of statistical mediation analysis.MethodsSelected articles and examples are used to explain the principle of mediation analysis.ResultsThe goal of mediation analysis is to express an overall exposure effect as a combination of an indirect and a direct effect. For example, it might be of interest whether the increased risk of diabetes (outcome) due to obesity (exposure) is mediated by insulin resistance (indirect effect), and, if so, how much of a direct effect remains. In this example, insulin resistance is a potential mediator of the effect of obesity on the risk of diabetes. In general, for a mediation analysis to be valid, more confounders must be taken into account than in the estimation of the overall effect size. A regression-based approach can be used to ensure the consideration of all relevant confounders in a mediation analysis.ConclusionBy decomposing the overall exposure effect into indirect and direct components, a mediation analysis can reveal not just whether an exposure causes an outcome, but also how. For a mediation analysis to be valid, however, multiple assumptions must be satisfied that cannot easily be checked, potentially compromising such analyses as compared to the estimation of an overall effect.

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