Background and objectives: Generalized estimating equations (GEE) provide population-averaged model inference for longitudinal and clustered outcomes via a generalized linear model for the effect of explanatory variables on the marginal mean, while intra-cluster correlations are ordinarily treated as nuisance parameters. Software to richly parameterize and conduct inference for complex correlation structures in the marginal modeling framework is scarce.

Methods: A SAS macro, GEECORR, has been developed for the analysis of clustered binary data based on GEE to include additional estimating equations for modeling pairwise correlation between binary variates as a function of covariates.

Results: We illustrate the macro in a surveillance study with repeated measures, a longitudinal study, and a study with biological clustering.

Conclusions: This article provides an overview of the GEE method consisting of a pair of estimating equations, describes the features and capabilities of the GEECORR macro including regression diagnostics and finite-sample bias-corrected covariance estimators, and demonstrates the macro usage for three studies.

背景和目标：广义估计方程 (GEE) 通过广义线性模型为解释变量对边际均值的影响提供总体平均模型推断，用于纵向和集群结果，而集群内相关性通常被视为有害参数。在边缘建模框架中对复杂的相关结构进行丰富的参数化和推理的软件很少。

方法： SAS 宏 GEECORR 已开发用于分析基于 GEE 的聚类二元数据，以包括额外的估计方程，用于将二元变量之间的成对相关建模为协变量的函数。

结果：我们在重复测量的监测研究、纵向研究和生物聚类研究中说明了宏观。

结论：本文概述了由一对估计方程组成的 GEE 方法，描述了 GEECORR 宏的特征和功能，包括回归诊断和有限样本偏差校正协方差估计器，并演示了三项研究的宏用法。