A Bayesian random effects model for analyzing mixed negative binomial and continuous longitudinal responses

作者：Samani E Bahrami^*; Ganjali M

来源：Communications in Statistics - Theory and Methods, 2016, 45(8): 2354-2367.

DOI：10.1080/03610926.2013.857865

• Bayesian inference
• Gibbs sampler
• Markov Chain Monte Carlo
• Maximum curvature

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.

• 出版日期2016