Thall P F, Vail S C
Statistics/Computer & Information Systems Department, George Washington University, Washington, D.C. 20052.
Biometrics. 1990 Sep;46(3):657-71.
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.
提出了一个用于具有预测协变量的纵向计数的协方差模型族。这些模型考虑了过度离散、异方差性以及重复观测之间的相关性。该方法是一种类似于Liang和Zeger（1986年，《生物统计学》73卷，13 - 22页）给出的公式的拟似然回归。给出了协变量参数和方差 - 协方差参数的广义估计方程。推导了参数估计的大样本性质。通过对作为部分性癫痫辅助治疗药物丙戊酸研究中产生的癫痫发作计数数据的分析，说明了所提出的方法。
