A joint modeling approach for longitudinal microbiome data improves ability to detect microbiome associations with disease.

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

SUBMITTER: Luna PN 

PROVIDER: S-EPMC7769610 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature