A comparison of bias-corrected empirical covariance estimators with generalized estimating equations in small-sample longitudinal study settings
A comparison of bias-corrected empirical covariance estimators with generalized estimating equations in small-sample longitudinal study settings
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DOI：
10.1002/sim.7917
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发表时间：
2018-12-10
影响因子：
2
通讯作者：
Westgate, Philip M.
中科院分区：
文献类型：
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作者：
Ford, Whitney P.;Westgate, Philip M.
来自纵向研究的数据通常用广义估计方程进行分析。先前的文献表明，当受试者数量较少时，使用经验三明治协方差矩阵估计可能会产生自由推理。因此，我们采用了两种不同的方法来提高推理的有效性。首先，对经验估计量进行了许多不同的小样本修正，以减少由此产生的标准误差估计中的偏差。其次，临界值可以从具有近似自由度的t分布或f分布中获得。虽然关于这些小样本校正和自由度比较的有限研究已经发表，但有必要在更广泛的情况下对现有方法进行全面研究。因此，在本文中，我们进行了这样的模拟研究，发现在各种设置中有两种方法比其他方法更一致地获得名义I型错误率：首先，是Westgate和Burchett （2016, Statistics in Medicine 35, 3733-3744）最近提出的一种方法，该方法指定了协方差估计量和自由度。其次，是Mancl和DeRouen （2001, Biometrics 57, 126-134）和Kauermann和Carroll （2001, Journal of the American Statistical Association 96, 1387-1396）开发的两种流行修正的平均值，其自由度等于边际模型中受试者的数量减去参数的数量。
Data arising from longitudinal studies are commonly analyzed with generalized estimating equations. Previous literature has shown that liberal inference may result from the use of the empirical sandwich covariance matrix estimator when the number of subjects is small. Therefore, two different approaches have been used to improve the validity of inference. First, many different small-sample corrections to the empirical estimator have been offered in order to reduce bias in resulting standard error estimates. Second, critical values can be obtained from a t-distribution or an F-distribution with approximated degrees of freedom. Although limited studies on the comparison of these small-sample corrections and degrees of freedom have been published, there is a need for a comprehensive study of currently existing methods in a wider range of scenarios. Therefore, in this manuscript, we conduct such a simulation study, finding two methods to attain nominal type I error rates more consistently than other methods in a variety of settings: First, a recently proposed method by Westgate and Burchett (2016, Statistics in Medicine 35, 3733-3744) that specifies both a covariance estimator and degrees of freedom, and second, an average of two popular corrections developed by Mancl and DeRouen (2001, Biometrics 57, 126-134) and Kauermann and Carroll (2001, Journal of the American Statistical Association 96, 1387-1396) with degrees of freedom equaling the number of subjects minus the number of parameters in the marginal model.