Dataset for: Longitudinal Beta-Binomial Modeling using GEE for Over-Dispersed Binomial Data

Longitudinal binomial data are frequently generated from multiple questionnaires and assessments in various scientific settings for which the binomial data are often over-dispersed. The standard generalized linear mixed effects model (GLMM) 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 over-dispersed binomial data and estimate the regression parameters under a probit model using the Generalized Estimating Equation (GEE) 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 (HD) from a multi-site observational study of prodromal HD.