Stiger T R, Barnhart H X, Williamson J M
Department of Biometrics, Pfizer Inc., Groton, CT 06340, USA.
Stat Med. 1999 Jun 15;18(11):1419-33. doi: 10.1002/(sici)1097-0258(19990615)18:11<1419::aid-sim127>3.0.co;2-q.
Generalized estimating equations (GEE) methodology as proposed by Liang and Zeger has received widespread use in the analysis of correlated binary data. Miller et al. and Lipsitz et al. extended GEE to correlated nominal and ordinal categorical data; in particular, they used GEE for fitting McCullagh's proportional odds model. In this paper, we consider robust (that is, empirically corrected) and model-based versions of both a score test and a Wald test for assessing the assumption of proportional odds in the proportional odds model fitted with GEE. The Wald test is based on fitting separate multiple logistic regression models for each dichotomization of the response variable, whereas the score test requires fitting just the proportional odds model. We evaluate the proposed tests in small to moderate samples by simulating data from a series of simple models. We illustrate the use of the tests on three data sets from medical studies.
梁和泽格提出的广义估计方程（GEE）方法在相关二元数据的分析中得到了广泛应用。米勒等人以及利普西茨等人将GEE扩展到相关的名义和有序分类数据；特别是，他们使用GEE来拟合麦卡拉的比例优势模型。在本文中，我们考虑了用于评估用GEE拟合的比例优势模型中比例优势假设的得分检验和 Wald 检验的稳健（即经验校正）版本和基于模型的版本。Wald检验基于为响应变量的每个二分法拟合单独的多元逻辑回归模型，而得分检验只需要拟合比例优势模型。我们通过模拟一系列简单模型的数据来评估所提出的检验在小到中等样本中的性能。我们在来自医学研究的三个数据集上说明了这些检验的使用。
