title: Distribution-free models for longitudinal count responses with overdispersion and structural zeros.
abstract: Overdispersion and structural zeros are two major manifestations of departure from the Poisson assumption when modeling count responses using Poisson log-linear regression. As noted in a large body of literature, ignoring such departures could yield bias and lead to wrong conclusions. Different approaches have been developed to tackle these two major problems. In this paper, we review available methods for dealing with overdispersion and structural zeros within a longitudinal data setting and propose a distribution-free modeling approach to address the limitations of these methods by utilizing a new class of functional response models. We illustrate our approach with both simulated and real study data.
authors.affiliation: Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwoord Ave, Rochester, NY 14642, USA. qyuflow@gmail.com
authors.forename: Q
authors.initials: Q
authors.lastname: Yu
authors.forename: R
authors.initials: R
authors.lastname: Chen
authors.forename: W
authors.initials: W
authors.lastname: Tang
authors.forename: H
authors.initials: H
authors.lastname: He
authors.forename: R
authors.initials: R
authors.lastname: Gallop
authors.forename: P
authors.initials: P
authors.lastname: Crits-Christoph
authors.forename: J
authors.initials: J
authors.lastname: Hu
authors.forename: X M
authors.initials: XM
authors.lastname: Tu
mesh_terms: D015982:Bias; D056808:Biostatistics; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies; D015233:Models, Statistical; D009010:Monte Carlo Method; D016012:Poisson Distribution