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

Mediation analysis seeks to identify and quantify the paths by which an exposure affects an outcome. Intermediate variables which are effected by the exposure and which effect the outcome are known as mediators. There exists extensive work on mediation analysis in the context of models with a single mediator and continuous and binary outcomes. However these methods are often not suitable for multi-omic data that include highly interconnected variables measuring biological mechanisms and various types of outcome variables such as censored survival responses. In this article, we develop a general framework for causal mediation analysis with multiple exposures, multivariate mediators, and continuous, binary, and survival responses. We estimate mediation effects on several scales including the mean difference, odds ratio, and restricted mean scale as appropriate for various outcome models. Our estimation method avoids imposing constraints on model parameters such as the rare disease assumption while accommodating continuous exposures. We evaluate the framework and compare it to other methods in extensive simulation studies by assessing bias, type I error and power at a range of sample sizes, disease prevalences, and number of false mediators. Using Kidney Renal Clear Cell Carcinoma data from The Cancer Genome Atlas, we identify proteins which mediate the effect of metabolic gene expression on survival. Software for implementing this unified framework is made available in an R package (this https URL).