A Bayesian approach to analyse overdispersed longitudinal count data

ABSTRACT

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 [17,18] 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 [12].

KEYWORDS:

• Bayesian analysis
• Bayesian model assessment
• count data
• generalized linear mixed model
• over dispersion

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Fernanda Rizzato was partially supported by CNPq and CAPES (Brazilian Science Funding Agencies) and Clarice Demétrio by CNPq. Geert Molenberghs gratefully acknowledges support from the IAP Research Network P7/06 of the Belgian State (Belgian Science Policy).