Research article summary (published 13 Aug 2009):

Modelling heterogeneity in clustered count data with extra zeros using compound Poisson random effect.

Full Abstract

In medical and health studies, heterogeneities in clustered count data have been traditionally modeled by positive random effects in Poisson mixed models; however, excessive zeros often occur in clustered medical and health count data. In this paper, we consider a three-level random effects zero-inflated Poisson model for health-care utilization data where data are clustered by both subjects and families. To accommodate zero and positive components in the count response compatibly, we model the subject level random effects by a compound Poisson distribution. Our model displays a variance components decomposition which clearly reflects the hierarchical structure of clustered data. A quasi-likelihood approach has been developed in the estimation of our model. We illustrate the method with analysis of the health-care utilization data. The performance of our method is also evaluated through simulation studies. Copyright 2009 John Wiley & Sons, Ltd.

Author information

Author/s: Ma, Renjun (R); Hasan, M Tariqul (MT); Sneddon, Gary (G);

Affiliation: Department of Mathematics and Statistics, University of New Brunswick, Fredericton, NB, Canada E3B 5A3. renjun(-atsign-)unb.ca

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: Statistics in medicine (Stat Med), published in England. (Language: eng)

Reference: 2009-Aug; vol 28 (issue 18) : pp 2356-69

Dates: Created 2009/07/14; Completed 2009/10/01;

PMID: 19462420, status: MEDLINE (last retrieval date: 10/1/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Biometry Cluster Analysis Delivery of Health Care - statistics & numerical data; utilization Female Humans Likelihood Functions

Likelihood Functions Longitudinal Studies Male Models, Statistical Poisson Distribution Regression Analysis

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