Improved standard error estimator for maintaining the validity of inference in cluster randomized trials with a small number of clusters
Improved standard error estimator for maintaining the validity of inference in cluster randomized trials with a small number of clusters
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DOI：
10.1002/bimj.201600182
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发表时间：
2017-05-01
影响因子：
1.7
通讯作者：
Westgate, Philip M.
中科院分区：
文献类型：
--
作者：
Ford, Whitney P.;Westgate, Philip M.
聚类随机试验（CRTs）是将受试者随机分组到不同试验组的研究。由于同一聚类内的结果具有相关性，广义估计方程（GEE）正日益成为分析crt数据的流行选择。过去的研究表明，由于使用经验三明治协方差矩阵估计器，使用GEE进行分析可能导致自由推理，当聚类数量不大时，会产生负偏的标准误差估计。已经提出了许多技术来纠正这种负面偏见；然而，使用这些修正仍然可能导致有偏差的标准误差估计，从而测试大小不一致地在其标称水平。因此，需要改进修正，以便始终产生名义上的第一类错误率。在这份手稿中，我们研究了使用最近开发的修正经验标准误差估计和使用两种流行的修正的组合。在一项广泛的模拟研究中，我们发现，当使用Mancl和DeRouen （, Biometrics57, 126-134）和Kauermann和Carroll （Journal of American Statistical Association96, 1387-1396）开发的两种流行校正的平均值时，可以一致地获得名义I型错误率。因此，使用这种新的校正被发现明显优于使用以前推荐的校正。
Cluster randomized trials (CRTs) are studies in which clusters of subjects are randomized to different trial arms. Due to the nature of outcomes within the same cluster to be correlated, generalized estimating equations (GEE) are growing as a popular choice for the analysis of data arising from CRTs. In the past, research has shown that analyses using GEE could result in liberal inference due to the use of the empirical sandwich covariance matrix estimator, which can yield negatively biased standard error estimates when the number of clusters is not large. Many techniques have been presented to correct this negative bias; however, use of these corrections can still result in biased standard error estimates and thus test sizes that are not consistently at their nominal level. Therefore, there is a need for an improved correction such that nominal type I error rates will consistently result. In this manuscript, we study the use of recently developed corrections for empirical standard error estimation and the use of a combination of two popular corrections. In an extensive simulation study, we found that nominal type I error rates can be consistently attained when using an average of two popular corrections developed by Mancl and DeRouen (, Biometrics57, 126-134) and Kauermann and Carroll (, Journal of the American Statistical Association96, 1387-1396). Therefore, use of this new correction was found to notably outperform the use of previously recommended corrections.