Abstract:SUMMARY. When regression models adjust for mediators on the causal path from exposure to outcome, the regression coefficient of exposure is commonly viewed as a measure of the direct exposure effect. This interpretation can be misleading, even with a randomly assigned exposure. This is because adjustment for post-exposure measurements introduces bias whenever their association with outcome is confounded by more than just the exposure. By the same token, adjustment for such confounders stays problematic when th… Show more
“…Numerous other methods have been developed (1,2,9,13,24,26,35,58), which we could not address in this article. Some of these are discussed in the book length treatment of mediation (47).…”
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 exposuremediator 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.
“…Numerous other methods have been developed (1,2,9,13,24,26,35,58), which we could not address in this article. Some of these are discussed in the book length treatment of mediation (47).…”
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 exposuremediator 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.
“…Moreover, the difference between a total effect and a natural direct effect can be interpreted as an indirect effect but not a controlled direct effect, in general [6][7][8][9]. In recent years, there have been many discussions about the conditions to identify a direct and indirect effect, especially in an epidemiologic research, and some analytical methods for this identification have been properly developed on the basis of counterfactual definitions [10][11][12][13].…”
“…If violated, we end up with time-dependent or intermediate confounding. It remains to be investigated how techniques such as inverse probability weighting [46] or Gestimation [47], that can deal with intermediate confounding concerning the estimation of the controlled direct effect in single-level settings, could be applied to multilevel settings.…”
Crossover trials are widely used to assess the effect of a reversible exposure on an outcome of interest. To gain further insight into the underlying mechanisms of this effect, researchers may be interested in exploring whether or not it runs through a specific intermediate variable: the mediator. Mediation analysis in crossover designs has received scant attention so far and is mostly confined to the traditional Baron and Kenny approach. We aim to tackle mediation analysis within the counterfactual framework and elucidate the assumptions under which the direct and indirect effects can be identified in AB/BA crossover studies. Notably, we show that both effects are identifiable in certain statistical models, even in the presence of unmeasured time-independent (or upper-level) confounding of the mediator-outcome relation. Employing the mediation formula, we derive expressions for the direct and indirect effects in within-subject designs for continuous outcomes that lend themselves to linear modelling, under a large variety of settings. We discuss an estimation approach based on regressing differences in outcomes on differences in mediators and show how to allow for period effects as well as different types of moderation. The performance of this approach is compared to other existing methods through simulations and is illustrated with data from a neurobehavioural study. Lastly, we demonstrate how a sensitivity analysis can be performed that is able to assess the robustness of both the direct and indirect effect against violation of the "no unmeasured lowerlevel mediator-outcome confounding" assumption.
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