title: Longitudinal beta-binomial modeling using GEE for overdispersed binomial data.
abstract: Longitudinal binomial data are frequently generated from multiple questionnaires and assessments in various scientific settings for which the binomial data are often overdispersed. The standard generalized linear mixed effects model may result in severe underestimation of standard errors of estimated regression parameters in such cases and hence potentially bias the statistical inference. In this paper, we propose a longitudinal beta-binomial model for overdispersed binomial data and estimate the regression parameters under a probit model using the generalized estimating equation method. A hybrid algorithm of the Fisher scoring and the method of moments is implemented for computing the method. Extensive simulation studies are conducted to justify the validity of the proposed method. Finally, the proposed method is applied to analyze functional impairment in subjects who are at risk of Huntington disease from a multisite observational study of prodromal Huntington disease. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Department of Biostatistics, University of Iowa, Iowa City, IA, USA.
authors.forename: Hongqian
authors.initials: H
authors.lastname: Wu
authors.affiliation: Department of Biostatistics, Indiana University, Indianapolis, IN,, USA.|Department of Mathematics, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Department of Biostatistics, University of Iowa, Iowa City, IA, USA.|Department of Psychiatry, University of Iowa, Iowa City, IA, USA.
authors.forename: Jeffrey D
authors.initials: JD
authors.lastname: Long
mesh_terms: D000465:Algorithms; D016010:Binomial Distribution; D003627:Data Interpretation, Statistical; D006801:Humans; D006816:Huntington Disease / Q000453:epidemiology; D016014:Linear Models; D008137:Longitudinal Studies; D015233:Models, Statistical; D062706:Prodromal Symptoms; D012307:Risk Factors
keywords: beta-binomial model; generalized estimating equation; generalized linear mixed-effects model; overdispersion; probit model