Article first published online: 17 SEP 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 32, Issue 8, pages 1283–1293, 15 April 2013
How to Cite
Wong, K.-Y. and Lam, K.F. (2013), Modeling zero-inflated count data using a covariate-dependent random effect model. Statist. Med., 32: 1283–1293. doi: 10.1002/sim.5626
- Issue published online: 14 MAR 2013
- Article first published online: 17 SEP 2012
- Manuscript Accepted: 2 SEP 2012
- Manuscript Received: 2 AUG 2011
- asymptotic normal data augmentation;
- multiple imputation;
- zero-inflated count data
In various medical related researches, excessive zeros, which make the standard Poisson regression model inadequate, often exist in count data. We proposed a covariate-dependent random effect model to accommodate the excess zeros and the heterogeneity in the population simultaneously. This work is motivated by a data set from a survey on the dental health status of Hong Kong preschool children where the response variable is the number of decayed, missing, or filled teeth. The random effect has a sound biological interpretation as the overall oral health status or other personal qualities of an individual child that is unobserved and unable to be quantified easily. The overall measure of oral health status, responsible for accommodating the excessive zeros and also the heterogeneity among the children, is covariate dependent. This covariate-dependent random effect model allows one to distinguish whether a potential covariate has an effect on the conceived overall oral health condition of the children, that is, the random effect, or has a direct effect on the magnitude of the counts, or both. We proposed a multiple imputation approach for estimation of the parameters. We discussed the choice of the imputation size. We evaluated the performance of the proposed estimation method through simulation studies, and we applied the model and method to the dental data. Copyright © 2012 John Wiley & Sons, Ltd.