Small sample performance of bias-corrected sandwich estimators for cluster-randomized trials with binary outcomes
Small sample performance of bias-corrected sandwich estimators for cluster-randomized trials with binary outcomes
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DOI：
10.1002/sim.6344
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发表时间：
2015-01-30
影响因子：
2
通讯作者：
Redden, David T.
中科院分区：
文献类型：
--
作者：
Li, Peng;Redden, David T.
广义估计方程（GEE）方法中的三明治估计量低估了小样本情况下的真实方差，从而导致假设检验中的I类错误率膨胀。这一事实限制了GEE在具有少数聚类的聚类随机化试验（CRT）中的应用。在各种CRT的情况下，相关的二进制结果，我们评估的小样本性质的GEE Wald测试使用偏差校正的三明治估计。我们的研究结果表明，GEE Wald z-检验应避免在CRT的分析与少数集群，即使使用偏差校正的三明治估计。在t分布近似下，Kauermann和卡罗尔（KC）校正即使在聚类数低至10时也能将检验规模保持在标称水平，并且对聚类规模的适度变化具有鲁棒性。然而，在簇大小变化较大的情况下，应使用Fay和Graubard（FG）校正。此外，我们推导出一个公式来计算功率和最小的总数量的集群之一需要使用的t检验和KC校正的CRT与二进制的结果。所提出的公式预测的功率水平与模拟的经验功率吻合得很好。所提出的方法说明使用真实的CRT数据。我们的结论是，在小样本量下适当控制I类错误率，我们建议使用GEE方法在CRT与二进制结果，因为较少的假设和稳健性的协方差结构的错误指定。版权所有（c）2014约翰威利父子有限公司
The sandwich estimator in generalized estimating equations (GEE) approach underestimates the true variance in small samples and consequently results in inflated type I error rates in hypothesis testing. This fact limits the application of the GEE in cluster-randomized trials (CRTs) with few clusters. Under various CRT scenarios with correlated binary outcomes, we evaluate the small sample properties of the GEE Wald tests using bias-corrected sandwich estimators. Our results suggest that the GEE Wald z-test should be avoided in the analyses of CRTs with few clusters even when bias-corrected sandwich estimators are used. With t-distribution approximation, the Kauermann and Carroll (KC)-correction can keep the test size to nominal levels even when the number of clusters is as low as 10 and is robust to the moderate variation of the cluster sizes. However, in cases with large variations in cluster sizes, the Fay and Graubard (FG)-correction should be used instead. Furthermore, we derive a formula to calculate the power and minimum total number of clusters one needs using the t-test and KC-correction for the CRTs with binary outcomes. The power levels as predicted by the proposed formula agree well with the empirical powers from the simulations. The proposed methods are illustrated using real CRT data. We conclude that with appropriate control of type I error rates under small sample sizes, we recommend the use of GEE approach in CRTs with binary outcomes because of fewer assumptions and robustness to the misspecification of the covariance structure. Copyright (c) 2014 John Wiley & Sons, Ltd.