Modeling Partly Conditional Means With Longitudinal Data
提出一种混合边际回归与经典转移模型的纵向数据建模方法，通过条件于部分过程历史而非全部，使用对角工作协方差矩阵的广义估计方程进行估计，适用于经济学等领域的重复测量数据分析。
Abstract We propose a general modeling approach to longitudinal data that is a hybrid of the marginal regression models of Zeger and Liang and of the classical transition models such as used in time series analyses. Rather than conditioning at time t only on covariate values, as is typical with the marginal approach, or on the entire history of the process up to t, as is typical with the transition model approach, we suggest models that condition on a subset of the process history. Estimation proceeds using generalized estimating equation methodology but with the restriction that the working covariance matrix is diagonal. The proposed regression models share common features with Cox regression models for failure time data in that they are composed of a nuisance baseline function of time and a simple parametric function of the covariates. Two illustrative examples are presented.