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Modelling heterogeneity in clustered count data with extra zeros using compound Poisson random effect
Author(s) -
Ma Renjun,
Hasan M. Tariqul,
Sneddon Gary
Publication year - 2009
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3619
Subject(s) - count data , poisson distribution , random effects model , overdispersion , poisson regression , statistics , zero inflated model , quasi likelihood , variance (accounting) , multilevel model , computer science , hierarchical database model , compound poisson distribution , mathematics , econometrics , data mining , medicine , population , meta analysis , environmental health , accounting , business
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.