A Bayesian analysis of clustered discrete and continuous outcomes

作者：Bowman Dale^*; George E. Olusegun

来源：Journal of Applied Statistics, 2018, 45(3): 438-449.

DOI：10.1080/02664763.2017.1280003

• Mixed regression models
• latent variables
• Gibbs sampling
• Bayesian analysis
• risk analysis

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.

• 出版日期2018