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Activity Number: 518 - Statistical Methods for Complex Interactions and Genetic and Environmental Epidemiology
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #304374
Title: JointMM: Joint Modeling of Longitudinal Microbiome and Time-To-Event Data with Application to a Type I Diabetes Study
Author(s): Jiyuan Hu* and Chan Wang and Martin Blaser and Huilin Li
Companies: New York Unversity School of Medicine and Division of Biostatistics, NYU School of Medicine and New York University School of Medicine and Rutgers University and NYU School of Medicine
Keywords: longitudinal; microbiome; joint modeling; generalized linear mixed effects model; 16S rRNA; relative abundance

Recent studies suggested that the dynamics of human microbiome may have associated with human health and disease. More and more longitudinal microbiome studies are conducted and time to disease onset is also collected at the same time, aiming to identify candidate microbes as biomarkers for the disease prognosis. We propose a novel joint modeling framework JointMM for longitudinal microbiome and time-to-event data to investigate the effect of the dynamic changes of microbiome profile on disease onset. JointMM can examine whether the temporal microbial presence/absence pattern and/or the abundance dynamics would alter time to disease onset. With the longitudinal sub-model of JointMM, we can also investigate the association between (time-varying) covariates and temporal microbial presence/absence and/or abundance patterns. JointMM is specifically designed to handle zero-inflated and highly skewed longitudinal microbiome proportion data. Comprehensive simulations and real data analyses demonstrated that JointMM is statistically more powerful at examining the longitudinal microbiome-survival association than competing methods with well-controlled type I error rates.

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

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