Williamson J, Kim K
Department of Biostatistics, Rollins School of Public Health of Emory University, Atlanta, Georgia 30322, USA.
Stat Med. 1996 Jul 30;15(14):1507-18. doi: 10.1002/(SICI)1097-0258(19960730)15:14<1507::AID-SIM316>3.0.CO;2-Z.
In typical clinical trials or epidemiologic studies of a bilateral eye disease, the primary outcome data consist of pairs of ordered categorical responses that tend to be highly correlated. In such studies, interest often centres in associating the outcome data with a grouping variable such as the treatment indicator or the exposure status and other person- and eye-specific covariates. In this paper, we propose a latent variable regression model to analyse such bivariate ordered categorical data. We use as a joint distribution for bivariate latent random variables the cross ratio distribution proposed by Plackett, which results in modelling the dependency between the fellow eyes with the global odds ratio. We illustrate the proposed model with data from the Wisconsin Epidemiologic Study of Diabetic Retinopathy, a study that seeks to identify risk factors among younger-onset diabetics.
在典型的双侧眼病临床试验或流行病学研究中，主要结局数据由往往高度相关的有序分类反应对组成。在此类研究中，关注点通常在于将结局数据与一个分组变量（如治疗指标或暴露状态）以及其他个体和眼部特异性协变量联系起来。在本文中，我们提出一种潜在变量回归模型来分析此类双变量有序分类数据。我们将Plackett提出的交叉比分布用作双变量潜在随机变量的联合分布，这导致用全局优势比来对双眼之间的相关性进行建模。我们用来自威斯康星糖尿病视网膜病变流行病学研究的数据说明了所提出的模型，该研究旨在识别年轻发病糖尿病患者中的危险因素。
