B0406
Title: Inverse probability weighting-based mediation analysis for microbiome data
Authors: Yuexia Zhang - The University of Texas at San Antonio (United States) [presenting]
Linbo Wang - University of Toronto (Canada)
Jianhua Hu - Columbia University (United States)
Jian Wang - University of Texas MD Anderson Cancer Center (United States)
Jiayi Shen - University of Southern California (United States)
Jessica Galloway-Pena - Texas A and M University (United States)
Samuel Shelburne - University of Texas MD Anderson Cancer Center (United States)
Abstract: Mediation analysis is an important tool for studying causal associations in biomedical and other scientific areas and has recently gained attention in microbiome studies. Using a microbiome study of acute myeloid leukemia (AML) patients, we investigate whether the effect of induction chemotherapy intensity levels on the infection status is mediated by microbial taxa abundance. The unique characteristics of the microbial mediators--- high dimensionality, zero inflation, and dependence---call for new methodological developments in mediation analysis. The presence of an exposure-induced mediator-outcome confounder, antibiotic use, further requires a delicate treatment in the analysis. To address these unique challenges in our motivating AML microbiome study, we propose a novel nonparametric identification formula for the interventional indirect effect (IIE), a measure recently developed for studying mediation effects. We develop the corresponding estimation algorithm using the inverse probability weighting method. We also test the presence of mediation effects via constructing the standard normal bootstrap confidence intervals. Simulation studies show that the proposed method has good finite-sample performance in terms of the IIE estimation, and type-I error rate and power of the corresponding test. In the AML microbiome study, our findings suggest that the effect of induction chemotherapy intensity levels on infection is mainly mediated by patients' gut microbiome.
