General Multiple Mediation Analysis With an Application to Explore Racial Disparities in Breast Cancer Survival

Abstract

Mediation refers to the effect transmitted by mediators that intervenes in the relationship between an exposure and a response variable. Mediation analysis has been broadly studied in many fields. However, it remains a challenge for researchers to differentiate individual effect from multiple mediators. This paper proposes general definitions of mediation effects that are consistent for all different types (categorical or continuous) of response, exposure, or mediation variables. With these definitions, multiple mediators can be considered simultaneously, and the indirect effects carried by individual mediators can be separated from the total effect. Moreover, the derived mediation analysis can be performed with general predictive models. For linear predictive models with continuous mediators, we show that the proposed method is equivalent to the conventional coefficients product method. We also establish the relationship between the proposed definitions of direct or indirect effect and the natural direct or indirect effect for binary exposure variables. The proposed method is demonstrated by both simulations and a real example examining racial disparities in three-year survival rates for female breast cancer patients in Louisiana.