Abstract
Changes in the composition of the microbiome over time are associated with myriad human illnesses. Unfortunately, the lack of analytic techniques has hindered researchers' ability to quantify the association between longitudinal microbial composition and time-to-event outcomes. Prior methodological work developed the joint model for longitudinal and time-to-event data to incorporate time-dependent biomarker covariates into the hazard regression approach to disease outcomes. The original implementation of this joint modeling approach employed a linear mixed effects model to represent the time-dependent covariates. However, when the distribution of the time-dependent covariate is non-Gaussian, as is the case with microbial abundances, researchers require different statistical methodology. We present a joint modeling framework that uses a negative binomial mixed effects model to determine longitudinal taxon abundances. We incorporate these modeled microbial abundances into a hazard function with a parameterization that not only accounts for the proportional nature of microbiome data, but also generates biologically interpretable results. Herein we demonstrate the performance improvements of our approach over existing alternatives via simulation as well as a previously published longitudinal dataset studying the microbiome during pregnancy. The results demonstrate that our joint modeling framework for longitudinal microbiome count data provides a powerful methodology to uncover associations between changes in microbial abundances over time and the onset of disease. This method offers the potential to equip researchers with a deeper understanding of the associations between longitudinal microbial composition changes and disease outcomes. This new approach could potentially lead to new diagnostic biomarkers or inform clinical interventions to help prevent or treat disease.
Highlights
Multiple studies have found differences in microbial compositions among people with various illnesses, including depression, obesity, asthma, and autism spectrum disorder [1,2,3,4,5,6,7]
Evaluating how changes in the human microbiome influence the onset of disease could lead to the development of novel approaches for diagnosis and treatment
Multiple methods use smoothing splines to determine the time intervals in which microbial compositions significantly differ between phenotypic groups [21,22,23]
Summary
Multiple studies have found differences in microbial compositions among people with various illnesses, including depression, obesity, asthma, and autism spectrum disorder [1,2,3,4,5,6,7]. Understanding the complex trajectories of different microbes within a community and the relationship of these trajectories to the onset of human disease is important to uncovering the origins of dysbiosis This enhanced understanding may eventually help researchers develop new methods for diagnosing and treating disease. Multiple methods use smoothing splines to determine the time intervals in which microbial compositions significantly differ between phenotypic groups [21,22,23]. While all of these methods analyze associations between longitudinal microbiome data and an outcome, they do not account for how these changes affect time-to-event disease outcomes. Two methods for determining associations between microbial compositions and event times have been developed [24, 25], but they only examine the microbiome composition at a single time point
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