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

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 Med

journal_title

Statistics in medicine

authors

Ma R,Hasan MT,Sneddon G

doi

10.1002/sim.3619

subject

Has Abstract

pub_date

2009-08-15 00:00:00

pages

2356-69

issue

18

eissn

0277-6715

issn

1097-0258

journal_volume

28

pub_type

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