A compositional mediation model for a binary outcome: Application to microbiome studies.

Abstract

The delicate balance of the microbiome is implicated in our health and is shaped by external factors, such as diet and xenobiotics. Therefore, understanding the role of the microbiome in linking external factors and our health conditions is crucial to translate microbiome research into therapeutic and preventative applications. We introduced a sparse compositional mediation model for binary outcomes to estimate and test the mediation effects of the microbiome utilizing the compositional algebra defined in the simplex space and a linear zero-sum constraint on probit regression coefficients. For this model with the standard causal assumptions, we showed that both the causal direct and indirect effects are identifiable. We further developed a method for sensitivity analysis for the assumption of the no unmeasured confounding effects between the mediator and the outcome. We conducted extensive simulation studies to assess the performance of the proposed method and applied it to real microbiome data to study mediation effects of the microbiome on linking fat intake to overweight/obesity. An R package can be downloaded from https://github.com/mbsohn/cmmb. Supplementary files are available at Bioinformatics online.