A Bayesian approach to analyse overdispersed longitudinal count data

<p>In this paper, we consider a model for repeated count data, with within-subject correlation and/or overdispersion. It extends both the generalized linear mixed model and the negative-binomial model. This model, proposed in a likelihood context [<a href="#CIT0017" target="_blank">17</a>,<a href="#CIT0018" target="_blank">18</a>] is placed in a Bayesian inferential framework. An important contribution takes the form of Bayesian model assessment based on pivotal quantities, rather than the often less adequate DIC. By means of a real biological data set, we also discuss some Bayesian model selection aspects, using a pivotal quantity proposed by Johnson [<a href="#CIT0012" target="_blank">12</a>].</p>