Statistical Methods for Ophthalmologic and Cluster Data

描述(申请人提供)：相关数据是眼科数据中的常态，这是由于成对眼睛的相关反应。现在有大量的方法可以通过结合集群效应来增强标准模型(Rosner，1984；Leung和Zeger，1986)。这些方法通常局限于正态分布或二元结果。然而，眼科中使用的许多尺度是连续的，但不是正态分布的(例如，视野区域)。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，糖尿病视网膜病变级别)。对于这种尺度，非参数方法是一种自然的方法。在该方案中，我们提出了一个包含排序数据的聚类效应的模型，并使用该模型扩展了针对聚类数据情况的测试，如Mann-Whitney U测试。此外，我们还提出了扩展，以允许结合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的(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，一些风险因素的风险因素可能不同，而对于其他风险因素，风险因素可能是相同的。(马歇尔和齐泽姆，1985)。我们建议在这种情况下使用灵活类型的多分类回归模型，并通过考虑匹配设计和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，以方便地拟合这些模型，包括使用逐步回归策略。 第三个感兴趣的领域是在小样本设置中使用相关数据方法。大多数已开发的方法都具有良好的渐近性质，但它们的有效性依赖于至少一个中等大小的样本。我们建议通过引入聚类效应并使这些方法在可输出软件中可用来扩展现有的精确推理方法。 最后，ROC曲线下的面积经常被用作Logistic回归模型的拟合优度的衡量标准。然而，在眼科研究中，如果单独的Logistic模型适用于个体的右眼和左眼，则它的使用是有问题的。我们建议将传统的ROC曲线方法(Hanley和McNeil，1982)扩展到对侧眼的结果可能是一致的(双眼情况)或不一致的(单眼情况)的聚集数据情况。

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).