Zero-inflated Poisson mixed model for longitudinal count data with informative dropouts

Zero-inflated Poisson (ZIP) models are typically used for analyzing count data with excess zeros. If the data are collected longitudinally, then repeated observations from a given subject are correlated by nature. The ZIP mixed model may be used to deal with excess zeros and correlations among the repeated observations. Also, it is often the case that some follow-up measurements in a longitudinal study are missing. If the missing data are informative or nonignorable, it is necessary to incorporate a missingness mechanism into the observed likelihood function for a valid inference. In this paper, we propose and explore an efficient method for analyzing count data by addressing the complex issues of excess zeros, correlations among repeated observations, and missing responses due to dropouts. The empirical properties of the proposed estimators are studied based on Monte Carlo simulations. An application is provided using some real data obtained from a health study.