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首页> 外文期刊>Statistics in medicine >Augmented generalized estimating equations for improving efficiency and validity of estimation in cluster randomized trials by leveraging cluster-level and individual-level covariates
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Augmented generalized estimating equations for improving efficiency and validity of estimation in cluster randomized trials by leveraging cluster-level and individual-level covariates

机译：通过利用聚类水平和个体水平协变量来提高聚类随机试验中估计的效率和有效性的广义广义估计方程

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摘要

Recent methodological advances in covariate adjustment in randomized clinical trials have used semiparametric theory to improve efficiency of inferences by incorporating baseline covariates; these methods have focused on independent outcomes. We modify one of these approaches, augmentation of standard estimators, for use within cluster randomized trials in which treatments are assigned to groups of individuals, thereby inducing correlation. We demonstrate the potential for imbalance correction and efficiency improvement through consideration of both cluster-level covariates and individual-level covariates. To improve small-sample estimation, we consider several variance adjustments. We evaluate this approach for continuous and binary outcomes through simulation and apply it to data from a cluster randomized trial of a community behavioral intervention related to HIV prevention in Tanzania.
机译：随机临床试验中协变量调整的最新方法学进展已使用半参数理论，通过纳入基线协变量来提高推理效率。这些方法侧重于独立的结果。我们修改了这些方法之一，即标准估计量的增加，用于在将随机分组的治疗分配给个体的集群随机试验中使用，从而引起相关性。通过展示群集级协变量和个人级协变量，我们证明了不平衡校正和效率提高的潜力。为了改善小样本估计，我们考虑了几种方差调整。我们通过模拟评估此方法的连续性和二进制结果，并将其应用于坦桑尼亚社区与HIV预防相关的社区行为干预的集群随机试验的数据。

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