论文标题
分组的广义估计方程进行纵向数据分析
Grouped Generalized Estimating Equations for Longitudinal Data Analysis
论文作者
论文摘要
考虑到同一受试者内的潜在相关性，广义估计方程（GEE）被广泛用于纵向数据的回归建模。尽管标准GEE在所有受试者中都假定了共同的回归系数，但是当受试者的回归系数潜在异质性时，这种假设可能是不现实的。在本文中，我们开发了一种灵活且可解释的方法，称为分组的GEE分析，以建模纵向数据，以允许回归系数的异质性。提出的方法假定受试者被分为同一组中的一个有限数量的组和受试者，共享相同的回归系数。我们提供了一种简单的算法，用于对受试者进行分组和同时估算回归系数，并显示提出的估计量的渐近性能。组的数量可以通过平均方法通过交叉验证来确定。我们通过仿真研究和对真实数据集的应用来证明所提出的方法。
Generalized estimating equation (GEE) is widely adopted for regression modeling for longitudinal data, taking account of potential correlations within the same subjects. Although the standard GEE assumes common regression coefficients among all the subjects, such an assumption may not be realistic when there is potential heterogeneity in regression coefficients among subjects. In this paper, we develop a flexible and interpretable approach, called grouped GEE analysis, to modeling longitudinal data with allowing heterogeneity in regression coefficients. The proposed method assumes that the subjects are divided into a finite number of groups and subjects within the same group share the same regression coefficient. We provide a simple algorithm for grouping subjects and estimating the regression coefficients simultaneously, and show the asymptotic properties of the proposed estimator. The number of groups can be determined by the cross-validation with averaging method. We demonstrate the proposed method through simulation studies and an application to a real dataset.
