Department of Economics, Mathematics and Statistics, Birkbeck, London, UK.
Stat Med. 2010 Nov 30;29(27):2825-37. doi: 10.1002/sim.4073.
The statistical analysis of repeated measures or longitudinal data always requires the accommodation of the covariance structure of the repeated measurements at some stage in the analysis. The general linear mixed model is often used for such analyses, and allows for the specification of both a mean model and a covariance structure. Often the covariance structure itself is not of direct interest, but only a means to producing valid inferences about the response. Existing methods of analysis are often inadequate where the sample size is small. More precisely, statistical measures of goodness of fit are not necessarily the right measure of the appropriateness of a covariance structure and inferences based on conventional Wald-type procedures do not approximate sufficiently well their nominal properties when data are unbalanced or incomplete. This is shown to be the case when adopting the Kenward-Roger adjustment where the sample size is very small. A generalization of an approach to Wald tests using a bias-adjusted empirical sandwich estimator for the covariance matrix of the fixed effects parameters from generalized estimating equations is developed for Gaussian repeated measurements. This is shown to attain the correct test size but has very low power. Copyright © 2010 John Wiley & Sons, Ltd.
重复测量或纵向数据的统计分析通常需要在分析的某个阶段适应重复测量的协方差结构。一般线性混合模型常用于此类分析，并且允许指定均值模型和协方差结构。通常情况下，协方差结构本身并不是直接感兴趣的，而只是产生关于响应的有效推断的一种手段。在样本量较小时，现有的分析方法往往不够充分。更确切地说，拟合优度的统计度量不一定是协方差结构适当性的正确度量，并且当数据不平衡或不完整时，基于传统 Wald 型程序的推断并不能很好地逼近其名义特性。当采用肯沃德-罗杰调整时，当样本量非常小时，就会出现这种情况。针对广义估计方程中固定效应参数的协方差矩阵，开发了一种使用偏差校正经验三明治估计器对 Wald 检验进行广义化的方法，用于高斯重复测量。结果表明，该方法可以达到正确的检验大小，但功效非常低。版权所有©2010 约翰威立父子有限公司。
