Bayesian semiparametric inference in longitudinal metabolomics data.

ABSTRACT: The article is motivated by an application to the EarlyBird cohort study aiming to explore how anthropometrics and clinical and metabolic processes are associated with obesity and glucose control during childhood. There is interest in inferring the relationship between dynamically changing and high-dimensional metabolites and a longitudinal response. Important aspects of the analysis include the selection of the important set of metabolites and the accommodation of missing data in both response and covariate values. With this motivation, we propose a flexible but parsimonious Bayesian semiparametric joint model for the outcome and the covariate generating processes, making novel use of nonparametric mean processes, latent factor models, and different classes of continuous shrinkage priors.

SUBMITTER: Sarkar A 

PROVIDER: S-EPMC11682272 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature