Format

Send to:

Choose Destination
See comment in PubMed Commons below
Stat Modelling. 2010 Dec;10(4):421-439.

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

Author information

  • 1Department of Health Care Policy, Harvard Medical School, Boston, USA.

Abstract

In applications involving count data, it is common to encounter an excess number of zeros. In the study of outpatient service utilization, for example, 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 (ZIP) and zero-inflated negative binomial (ZINB), 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.

PMID:
21339863
[PubMed]
PMCID:
PMC3039917
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for PubMed Central
    Loading ...
    Write to the Help Desk