论文标题
Geecorr：使用广义估计方程的相关二进制响应和集群内相关的回归模型的SAS宏
GEECORR: A SAS macro for regression models of correlated binary responses and within-cluster correlation using generalized estimating equations
论文作者
论文摘要
已经开发了一种基于Prentice（1988）估计方程方法的相关二进制数据的SAS宏Geecorr，该方法扩展了Liang and Zeger（1986）广义估计方程（GEE）方法，以包括二进制变量之间成对相关性的附加估计方程。这种扩展可以灵活地建模边际平均值和群内相关性，这是它们各自的协变风险因素的函数。本文概述了扩展估计方程方法，描述了地质宏的特征和功能，并将地质宏应用于三个不同的数据集。此外，本文介绍了Prentice（1988）提出的更详细的拟合算法，其中在地质宏中实施了变化。我们提供了一项小型仿真研究，以证明估计相关参数的详细方法的效率。
A SAS macro, GEECORR, has been developed for the analysis of correlated binary data based on the Prentice (1988) estimating equations method that extends the Liang and Zeger (1986) generalized estimating equations (GEE) method to include additional estimating equations for the pairwise correlation between binary variates. This extension allows for flexible modeling of both the marginal mean and within-cluster correlation as a function of their respective covariate risk factors. This paper provides an overview of the extended estimating equations method, describes the features and capabilities of the GEECORR macro, and applies the GEECORR macro to three different datasets. In addition, this paper describes the more detailed fitting algorithm proposed by Prentice (1988), of which a variation has been implemented in the GEECORR macro. We provide a small simulation study to demonstrate the efficiency of the detailed method for estimating correlation parameters.
