Journal
BIOMETRICAL JOURNAL
Volume 45, Issue 4, Pages 437-452Publisher
WILEY
DOI: 10.1002/bimj.200390024
Keywords
count data; generalised linear mixed models; negative binomial; Poisson regression; random effects; zero-inflation
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In many biometrical applications, the count data encountered often contain extra zeros relative to the Poisson distribution. Zero-inflated Poisson regression models are useful for analyzing such data, but parameter estimates may be seriously biased if the nonzero observations are over-dispersed and simultaneously correlated due to the sampling design or the data collection procedure. In this paper, a zero-inflated negative binomial mixed regression model is presented to analyze a set of pancreas disorder length of stay (LOS) data that comprised mainly same-day separations. Random effects are introduced to account for inter-hospital variations and the dependency of clustered LOS observations. Parameter estimation is achieved by maximizing an appropriate log-likelihood function using an EM algorithm. Alternative modeling strategies, namely the finite mixture of Poisson distributions and the non-parametric maximum likelihood approach, are also considered. The detemidnation of pertinent covariates would assist hospital administrators and clinicians to manage LOS and expenditures efficiently.
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