A Comparison of Correlation Structure Selection Penalties for Generalized Estimating Equations
A Comparison of Correlation Structure Selection Penalties for Generalized Estimating Equations
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DOI：
10.1080/00031305.2016.1200490
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发表时间：
2017-01-01
影响因子：
1.8
通讯作者：
Burchett, Woodrow W.
中科院分区：
文献类型：
--
作者：
Westgate, Philip M.;Burchett, Woodrow W.
相关数据通常使用使用群体平均广义估计方程（GEE）构建的模型进行分析。总体平均GEE模型的规范包括选择描述重复测量相关性的结构。这种结构的准确规格可以提高效率，而滋扰相关参数的有限样本估计可以膨胀的回归参数估计的方差。因此，相关性结构选择标准应该惩罚或解释相关性参数估计。在这篇文章中，我们比较了最近提出的惩罚，其相关性结构的选择和回归参数估计的影响，并给出了实际的考虑数据分析。本文的补充材料可在网上查阅。
Correlated data are commonly analyzed using models constructed using population-averaged generalized estimating equations (GEEs). The specification of a population-averaged GEE model includes selection of a structure describing the correlation of repeated measures. Accurate specification of this structure can improve efficiency, whereas the finite-sample estimation of nuisance correlation parameters can inflate the variances of regression parameter estimates. Therefore, correlation structure selection criteria should penalize, or account for, correlation parameter estimation. In this article, we compare recently proposed penalties in terms of their impacts on correlation structure selection and regression parameter estimation, and give practical considerations for data analysts. Supplementary materials for this article are available online.