Multi-level zero-inflated poisson regression modelling of correlated count data with excess zeros.
Count data with excess zeros relative to a Poisson distribution are common in many biomedical applications. A popular approach to the analysis of such data is to use a zero-inflated Poisson (ZIP) regression model. Often, because of the hierarchical study design or the data collection procedure, zero-inflation and lack of independence may occur simultaneously, which render the standard ZIP model inadequate. To account for the preponderance of zero counts and the inherent correlation of observations, a class of multi-level ZIP regression model with random effects is presented. Model fitting is facilitated using an expectation-maximization algorithm, whereas variance components are estimated via residual maximum likelihood estimating equations. A score test for zero-inflation is also presented. The multi-level ZIP model is then generalized to cope with a more complex correlation structure. Application to the analysis of correlated count data from a longitudinal infant feeding study illustrates the usefulness of the approach.
Department of Epidemiology and Biostatistics, School of Public Health, Curtin University of Technology, Perth, WA, Australia. Andy.Lee@curtin.edu.au
Andy H
AH
Lee
Kui
K
Wang
Jane A
JA
Scott
Kelvin K W
KK
Yau
Geoffrey J
GJ
McLachlan
D000328:Adult; D001942:Breast Feeding / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008137:Longitudinal Studies; D008297:Male; D015233:Models, Statistical; D016012:Poisson Distribution; D012044:Regression Analysis; D014904:Western Australia