Some covariance models for longitudinal count data with overdispersion.
A family of covariance models for longitudinal counts with predictive covariates is presented. These models account for overdispersion, heteroscedasticity, and dependence among repeated observations. The approach is a quasi-likelihood regression similar to the formulation given by Liang and Zeger (1986, Biometrika 73, 13-22). Generalized estimating equations for both the covariate parameters and the variance-covariance parameters are presented. Large-sample properties of the parameter estimates are derived. The proposed methods are illustrated by an analysis of epileptic seizure count data arising from a study of progabide as an adjuvant therapy for partial seizures.
Thall
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Statistics/Computer & Information Systems Department, George Washington University, Washington, D.C. 20052.
Vail
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D000704:Analysis of Variance; D001699:Biometry; D004827:Epilepsy / Q000188:drug therapy; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical