A Bayesian random effects model for analyzing mixed negative binomial and continuous longitudinal responses. Issue 8 (17th April 2016)

• Record Type: Journal Article
• Authors: Samani, E. Bahrami; Ganjali, M.
• Is Part Of: Communications in statistics. Volume 45:Issue 8(2016)
• Page Start: 2354
• Page End: 2367
• Publication Date: 2016-04-17
• Subjects: Bayesian inference -- Gibbs sampler -- Markov Chain Monte Carlo -- Maximum curvature.; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2013.857865
• Languages: English
• ISSNs: 0361-0926

Abstract

ABSTRACT: A general Bayesian random effects model for analyzing longitudinal mixed correlated continuous and negative binomial responses with and without missing data is presented. This Bayesian model, given some random effects, uses a normal distribution for the continuous response and a negative binomial distribution for the count response. A Markov Chain Monte Carlo sampling algorithm is described for estimating the posterior distribution of the parameters. This Bayesian model is illustrated by a simulation study. For sensitivity analysis to investigate the change of parameter estimates with respect to the perturbation from missing at random to not missing at random assumption, the use of posterior curvature is proposed. The model is applied to a medical data, obtained from an observational study on women, where the correlated responses are the negative binomial response of joint damage and continuous response of body mass index. The simultaneous effects of some covariates on both responses are also investigated.