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.
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Ma R,Hasan MT,Sneddon Gdoi
10.1002/sim.3619subject
Has Abstractpub_date
2009-08-15 00:00:00pages
2356-69issue
18eissn
0277-6715issn
1097-0258journal_volume
28pub_type
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