Statistical Methods for Ophthalmologic and Cluster Data

描述（由申请方提供）：由于成对眼睛的相关反应，相关数据是眼科数据的标准。现在有大量的方法可以通过引入聚类效应来增强标准模型（Rosner，1984; Liang and Zeger，1986）。这些方法通常限于正态分布或二元结果。然而，在眼科学中使用的许多尺度是连续的，但非正态分布（例如视野面积）。其他量表是有序和非正态分布的，但有时也被视为正态分布（例如糖尿病视网膜病变等级）。非参数方法是一种自然的方法，这样的规模。在这个建议中，我们提出了一个模型，将聚类效应的排名数据，并使用该模型来扩展测试，如曼惠特尼U测试的聚类数据的情况。此外，我们提出了扩展，以允许捆绑值和调整其他协变量的影响。 眼科数据中的另一个问题是一些终点本质上是复合终点（例如，核性白内障、皮质性白内障、PSC白内障、对照）。 一个有趣的问题是，对于不同类型的白内障，某些风险因素的风险因素概况可能不同，而对于其他风险因素，它可能是相同的。（马歇尔和奇泽姆，1985年）。 我们建议在这种情况下使用一种灵活的多分类回归模型，并通过考虑匹配设计以及不相互排斥的结果类别来增强它。此外，我们建议开发一个用户友好的软件包，以方便地适应这种模型，包括使用逐步回归策略。 感兴趣的第三个领域是在小样本设置中使用相关数据方法。大多数以前的方法开发有良好的渐近性质，但依赖于至少一个中等大小的样本为他们的validity .We建议扩展现有的方法，将聚类效应，并使这些方法可由可导出的软件的精确推断。 最后，ROC曲线下面积经常用作logistic回归模型拟合优度的度量。 然而，如果在眼科研究中个体的右眼和左眼适合单独的逻辑模型，则其使用是有问题的。我们建议将传统的ROC曲线方法（汉利和McNeil，1982）扩展到对侧眼结果可能一致（双侧病例）或不一致（单侧病例）的聚类数据情况。

DESCRIPTION (provided by applicant): Correlated data are the norm in ophthalmologic data, due to correlated response for paired eyes. An abundance of methods are now available that enhance standard models by incorporating clustering effects (Rosner, 1984; Liang and Zeger, 1986). These methods are limited usually to either normally distributed or binary outcomes. However, many scales used in ophthalmology are continuous, but non-normally distributed (e.g. visual field area). Other scales are ordinal and non-normally distributed, but are sometimes treated as normally distributed as well (e.g. diabetic retinopathy grade). Nonparametric methods are a natural approach for such scales. In this proposal, we propose a model for incorporating clustering effects for ranked data and use this model to extend tests such as the Mann-Whitney U test for the clustered data situation. In addition, we propose extensions to allow for tied values and to adjust for other covariate effects. Another issue in ophthalmologic data is that some endpoints are composite in nature (e.g. nuclear cataract, cortical cataract, PSC cataract, control). An interesting issue is that the risk factor profile for some risk factors may be different for different types of cataract, while for other risk factors it may be the same. (Marshall & Chisholm, 1985). We propose to use a flexible type of polytomous regression model in this setting and to enhance it by considering matched designs as well as outcome categories that are not mutually exclusive. Furthermore, we propose to develop a user-friendly software package to easily fit such models including use of stepwise regression strategies. A third area of interest is the use of correlated data methods in a small sample setting. Most previous methods developed have good asymptotic properties but depend on at least a moderately sized sample for their validity .We propose to extend existing methods of exact inference by incorporating clustering effects and making these methods available by exportable software. Finally, the area under the ROC curve is frequently used as a measure of goodness-of-fit for logistic regression models. However, its use is problematic if separate logistic models are fit for the right and left eyes of an individual in ophthalmologic studies. We propose to extend traditional ROC curve methods (Hanley and McNeil, 1982) to the clustered data situation where outcome on fellow eyes may be either concordant (bilateral cases) or discordant (unilateral cases).