Some covariance models for longitudinal count data with overdispersion. (English) Zbl 0712.62048

Thall, Peter F.; Vail, Stephen C.

Biometrics 46, No. 3, 657-671 (1990).

Summary: 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 K.-Y. Liang and S. L. Zeger [Biometrika 73, 13-22 (1986; Zbl 0595.62110)]. 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.

MSC:

62H12	Estimation in multivariate analysis
62P10	Applications of statistics to biology and medical sciences; meta analysis

Keywords:

quasi-likelihood; covariance models; longitudinal counts; predictive covariates; overdispersion; heteroscedasticity; dependence among repeated observations; quasi-likelihood regression; Generalized estimating equations; covariate parameters; variance-covariance parameters; Large- sample properties; epileptic seizure count data