Cluster randomized trials, where clusters (for example, schools or clinics) are randomized to comparison arms but measurements are taken on individuals, are commonly used to evaluate interventions in public health, education, and the social sciences. Analysis is often conducted on individual-level outcomes, and such analysis methods must consider that outcomes for members of the same cluster tend to be more similar than outcomes for members of other clusters. A popular individual-level analysis technique is generalized estimating equations (GEE). However, it is common to randomize a small number of clusters (for example, 30 or fewer), and in this case, the GEE standard errors obtained from the sandwich variance estimator will be biased, leading to inflated type I errors. Some bias-corrected standard errors have been proposed and studied to account for this finite-sample bias, but none has yet been implemented in Stata. In this article, we describe several popular bias corrections to the robust sandwich variance. We then introduce our newly created command, xtgeebcv, which will allow Stata users to easily apply finite-sample corrections to standard errors obtained from GEE models. We then provide examples to demonstrate the use of xtgeebcv. Finally, we discuss suggestions about which finite-sample corrections to use in which situations and consider areas of future research that may improve xtgeebcv.

集群随机试验，其中集群（例如，学校或诊所）被随机分配到比较组，但对个人进行测量，通常用于评估公共卫生、教育和社会科学方面的干预措施。分析通常针对个人层面的结果进行，此类分析方法必须考虑到同一集群成员的结果往往比其他集群成员的结果更相似。一种流行的个体水平分析技术是广义估计方程 (GEE)。然而，随机化少量簇（例如，30 个或更少）是很常见的，在这种情况下，从三明治方差估计器获得的 GEE 标准误差将有偏差，导致 I 型错误膨胀。已经提出并研究了一些经过偏差校正的标准误差来解释这种有限样本偏差，但尚未在 Stata 中实施。在本文中，我们描述了几种流行的对稳健三明治方差的偏差校正。然后我们介绍我们新创建的命令，xtgeebcv，这将允许 Stata 用户轻松地将有限样本校正应用于从 GEE 模型获得的标准误差。然后我们提供示例来演示xtgeebcv的使用。最后，我们讨论了关于在哪些情况下使用哪些有限样本校正的建议，并考虑未来可能改进xtgeebcv的研究领域。