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The human microbiome has an important role in determining health. Mediationanalyses quantify the contribution of the microbiome in the causal path betweenexposure and disease; however, current mediation models cannot fully capturethe high dimensional, correlated, and compositional nature of microbiome dataand do not typically accommodate dichotomous outcomes. We propose a novelapproach that uses inverse odds weighting to test for the mediating effect ofthe microbiome. We use simulation to demonstrate that our approach gains powerfor high dimensional mediators, and it is agnostic to the effect ofinteractions between the exposure and mediators. Our application to infant gutmicrobiome data from the New Hampshire Birth Cohort Study revealed a mediatingeffect of 6-week infant gut microbiome on the relationship between maternalprenatal antibiotic use during pregnancy and incidence of childhood allergy by5 years of age.

HT-MMIOW: A Hypothesis Test approach for Microbiome Mediation using Inverse Odds Weighting