Harvard Medical School, Channing Laboratory, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA 02115, USA.
Stat Med. 2011 Nov 20;30(26):3117-24. doi: 10.1002/sim.4300. Epub 2011 Jul 11.
We investigate methods for data-based selection of working covariance models in the analysis of correlated data with generalized estimating equations. We study two selection criteria: Gaussian pseudolikelihood and a geodesic distance based on discrepancy between model-sensitive and model-robust regression parameter covariance estimators. The Gaussian pseudolikelihood is found in simulation to be reasonably sensitive for several response distributions and noncanonical mean-variance relations for longitudinal data. Application is also made to a clinical dataset. Assessment of adequacy of both correlation and variance models for longitudinal data should be routine in applications, and we describe open-source software supporting this practice.
我们研究了在使用广义估计方程分析相关数据时，基于数据选择工作协方差模型的方法。我们研究了两种选择标准：基于模型敏感和模型稳健回归参数协方差估计之间差异的测地线距离和高斯似然。模拟发现，高斯似然对于几种响应分布和纵向数据的非规范均值-方差关系具有相当的敏感性。该方法也应用于临床数据集。对于纵向数据，评估相关性和方差模型的适当性应该是应用中的常规操作，我们描述了支持这种做法的开源软件。
