title: Hurdle models for multilevel zero-inflated data via h-likelihood.
abstract: Count data often exhibit overdispersion. One type of overdispersion arises when there is an excess of zeros in comparison with the standard Poisson distribution. Zero-inflated Poisson and hurdle models have been proposed to perform a valid likelihood-based analysis to account for the surplus of zeros. Further, data often arise in clustered, longitudinal or multiple-membership settings. The proper analysis needs to reflect the design of a study. Typically random effects are used to account for dependencies in the data. We examine the h-likelihood estimation and inference framework for hurdle models with random effects for complex designs. We extend the h-likelihood procedures to fit hurdle models, thereby extending h-likelihood to truncated distributions. Two applications of the methodology are presented.
authors.affiliation: Department of Biostatistics, Erasmus MC, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands.
authors.forename: Marek
authors.initials: M
authors.lastname: Molas
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Lesaffre
mesh_terms: D000058:Accidental Falls / Q000517:prevention & control; D000368:Aged; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D064420:Drug-Related Side Effects and Adverse Reactions; D015444:Exercise; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical