Bayesian latent variable models for hierarchical clustered count outcomes with repeated measures in microbiome studies. Issue 3 (22nd January 2017)

• Record Type: Journal Article
• Authors: Xu, Lizhen; Paterson, Andrew D.; Xu, Wei
• Is Part Of: Genetic epidemiology. Volume 41:Issue 3(2017)
• Page Start: 221
• Page End: 232
• Publication Date: 2017-01-22
• Subjects: Bayesian latent variable model -- microbiome -- multivariate model -- repeated measures -- zero‐inflated count outcomes; Genetic epidemiology -- Periodicals; Heredity -- Periodicals; Medical geography -- Periodicals; 614
• DOI: 10.1002/gepi.22031
• Languages: English
• ISSNs: 0741-0395

Abstract

ABSTRACT: Motivated by the multivariate nature of microbiome data with hierarchical taxonomic clusters, counts that are often skewed and zero inflated, and repeated measures, we propose a Bayesian latent variable methodology to jointly model multiple operational taxonomic units within a single taxonomic cluster. This novel method can incorporate both negative binomial and zero‐inflated negative binomial responses, and can account for serial and familial correlations. We develop a Markov chain Monte Carlo algorithm that is built on a data augmentation scheme using Pólya‐Gamma random variables. Hierarchical centering and parameter expansion techniques are also used to improve the convergence of the Markov chain. We evaluate the performance of our proposed method through extensive simulations. We also apply our method to a human microbiome study.