Statistical Modelling 10 (2010), 421–439

A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use

Brian H Neelon
Children's Environmental Health Intiative,
Box 90328,
Nicholas School of the Environment,
Duke University
Durham, NC 27708
USA
eMail: brian.neelon@duke.edu

A James O’Malley
Department of Health Care Policy,
Harvard Medical School
USA

Sharon-Lise T Normand
Department of Health Care Policy,
Harvard Medical School
and
Department of Biostatistics,
Harvard School of Public Health
USA

Abstract:

In applications involving count data, it is common to encounter an excess number of zeros. For example, in the study of outpatient service utilization, the number of utilization days will take on integer values, with many subjects having no utilization (zero values). Mixed distribution models, such as the zero-inflated Poisson and zero-inflated negative binomial, are often used to fit such data. A more general class of mixture models, called hurdle models, can be used to model zero deflation as well as zero inflation. Several authors have proposed frequentist approaches to fitting zero-inflated models for repeated measures. We describe a practical Bayesian approach which incorporates prior information, has optimal small-sample properties and allows for tractable inference. The approach can be easily implemented using standard Bayesian software. A study of psychiatric outpatient service use illustrates the methods.

Keywords:

Bayesian inference; hurdle model; repeated measures; zero-altered model; zero-inflated model

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