A Bayesian analysis of clustered discrete and continuous outcomes. Issue 3 (17th February 2018)

• Record Type: Journal Article
• Authors: Bowman, Dale; George, E. Olusegun
• Is Part Of: Journal of applied statistics. Volume 45:Issue 3(2018)
• Page Start: 438
• Page End: 449
• Publication Date: 2018-02-17
• Subjects: Mixed regression models -- latent variables -- Gibbs sampling -- Bayesian analysis -- risk analysis; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2017.1280003
• Languages: English
• ISSNs: 0266-4763

Abstract

ABSTRACT: Many study designs yield a variety of outcomes from each subject clustered within an experimental unit. When these outcomes are of mixed data types, it is challenging to jointly model the effects of covariates on the responses using traditional methods. In this paper, we develop a Bayesian approach for a joint regression model of the different outcome variables and show that the fully conditional posterior distributions obtained under the model assumptions allow for estimation of posterior distributions using Gibbs sampling algorithm.