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Mediation analysis seeks to identify and quantify the paths by which anexposure affects an outcome. Intermediate variables which are effected by theexposure and which effect the outcome are known as mediators. There existsextensive work on mediation analysis in the context of models with a singlemediator and continuous and binary outcomes. However these methods are oftennot suitable for multi-omic data that include highly interconnected variablesmeasuring biological mechanisms and various types of outcome variables such ascensored survival responses. In this article, we develop a general frameworkfor causal mediation analysis with multiple exposures, multivariate mediators,and continuous, binary, and survival responses. We estimate mediation effectson several scales including the mean difference, odds ratio, and restrictedmean scale as appropriate for various outcome models. Our estimation methodavoids imposing constraints on model parameters such as the rare diseaseassumption while accommodating continuous exposures. We evaluate the frameworkand compare it to other methods in extensive simulation studies by assessingbias, type I error and power at a range of sample sizes, disease prevalences,and number of false mediators. Using Kidney Renal Clear Cell Carcinoma datafrom The Cancer Genome Atlas, we identify proteins which mediate the effect ofmetabolic gene expression on survival. Software for implementing this unifiedframework is made available in an R package(https://github.com/longjp/mediateR).

A Framework for Mediation Analysis with Multiple Exposures, Multivariate Mediators, and Non-Linear Response Models