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Activity Number: 11 - Recent Advances in Statistical Methods for Large-Scale Complex Biomedical Data
Type: Invited
Date/Time: Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #300504 Presentation
Title: Testing Mediation Effect in Compositional Microbiome Data
Author(s): Lei Liu* and Haixiang Zhang and Jun Chen and Zhigang Li
Companies: Washington University in St Louis and Tianjin University and Mayo Clinic and University of Florida
Keywords: Compositional mediators; High dimensional data; Isometric logratio transformation; Joint significance test

Mediation analysis has been commonly used to study the effect of an exposure on an outcome through a mediator. In this paper, we are interested in exploring the mediation mechanism of microbiome, whose special features make the analysis challenging. First, the relative abundances of the taxa in the microbiome have a compositional feature: each relative abundance is a non-negative value in [0, 1) which adds up to 1. Second, the number of taxa is high dimensional. We propose a novel solution to address these challenges: (1) we consider the isometric logratio transformation of the relative abundance as the mediator variable; (2) we develop an estimating and testing procedure for a targeted mediator of interest in the presence of a large number of mediators. Specially, we present a de-biased Lasso estimate for the targeted mediator and derive its standard error estimator, which can be used to develop a test procedure for the targeted mediation effect. Extensive simulation studies are conducted to assess the performance of our method. We apply the proposed approach to test the mediation effects of human gut microbiome between the dietary fiber intake and body mass index.

Authors who are presenting talks have a * after their name.

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