314Data-adaptive methods for high-dimensional mediation analysis: Application to a randomised trial of tuberculosis vaccination

Abstract

Abstract Focus of Presentation Statistical methods for causal mediation analysis are useful for understanding the pathways by which a certain treatment or exposure impacts health outcomes. Existing methods necessitate modelling of the distribution of the mediators, which quickly becomes infeasible when mediators are high-dimensional (e.g., biomarkers). We propose novel data-adaptive methods for estimating the indirect effect of a randomised treatment that acts via a pathway represented by a high-dimensional set of measurements. This work was motivated by the Melbourne Infant Study: BCG for Allergy and Infection Reduction (MIS BAIR), a rando..