Statistical Methods for Ophthalmologic and Cluster Data

描述（由申请人提供）：眼科数据为数据分析师提供了具有挑战性的方法学问题。首先，一个人的两只眼睛的数据是存在的，它们通常是正相关的，但并不相同。挑战在于如何以一种有效的方式使用所有数据，以解释同伴眼睛得分之间的相关性。在正态分布和二项分布结果变量的背景下，这一领域取得了实质性进展。SAS过程PROC MIXED和PROC GENMOD有效地处理这些类型的数据。然而，眼科中许多量表本质上是有序的（如ETDRS糖尿病视网膜病变量表、LOGS III白内障量表），一些连续的量表（如Humphrey视野量表）往往偏斜和非正态分布。因此，非参数分析是理想的两组比较。在本授权的前一个周期中，我们扩展了Wilcoxon秩和检验和signed秩检验，以解释随机化单位为受试者，但分析单位为眼睛的设置中的聚类数据，并且受试者的两只眼睛被随机分配到相同的治疗。在这个更新应用程序中，我们扩展了这些方法，以涵盖随机化单位是受试者的情况，但其他眼睛可能会或可能不会接受相同的治疗。这在眼部过敏试验中很常见。此外，我们建议扩展我们的方法，为通过Wilcoxon秩和检验分析的聚类数据提供功率和样本量的估计，并评估当聚类大小不等时不同加权方法的效率。此外，我们建议将我们的方法用于聚类数据的Wilcoxon秩和检验，以评估GEE模型在拟合纵向数据或具有相关结果（例如同伴眼）的横截面数据时的拟合度。最后，在前一个资助周期中，我们研究了评估复合终点的每个组成部分（例如不同类型的AMD）的暴露-疾病关系是否相同的方法，限制了受试者只能有一种类型。在这个循环中，我们建议扩展这些方法，以允许受试者同时具有多种疾病亚型（例如不同类型的白内障）的情况。在本建议中以及在前一个周期中开发的方法将以SAS宏的形式提供，这些宏可以在以下网站URL （http://www.qeocities.com/bernardrosner/channina.html）访问。

DESCRIPTION (provided by applicant): Ophthalmologic data present a data analyst with challenging methodologic problems. First, data are present for two eyes of an individual, which are usually positively correlated, but not the same. The challenge is how to use all the data in an efficient manner that accounts for the correlation between scores on fellow eyes. Substantial progress has been made in this area in the context of normally and binomially distributed outcome variables. SAS procedures PROC MIXED and PROC GENMOD deal effectively with these types of data. However, many scales in ophthalmology are ordinal in nature (e.g. ETDRS diabetic retinopathy scale, LOGS III cataract scale) and some continuous scales (e.g. Humphrey visual field) tend to be skewed and non-normally distributed. Hence, nonparametric analyses are desirable for two-group comparisons. In the previous cycle of this grant, we have extended the Wilcoxon rank sum test and signed rank test to account for clustered data in the setting where the unit of randomization is the subject, but the unit of analysis is the eye, and both eyes of a subject are randomized to the same treatment. In this renewal application, we extend these methods to cover the case when the unit of randomization is the subject, but fellow eyes may or may not receive the same treatment. This occurs commonly in ocular allergy trials. In addition, we propose to extend our methodology to provide estimates of power and sample size for clustered data analyzed by the Wilcoxon rank sum test, and assess efficiencies of different methods of weighting when clusters are of unequal size. Furthermore, we propose to apply our methodology for the Wilcoxon rank sum test for clustered data to the important problem of assessing goodness of fit of GEE models either when fit to longitudinal data or to cross-sectional data with correlated outcomes (e.g. fellow eyes). Finally, in the previous cycle of the grant we have studied methods for assessing whether exposure-disease relationships are the same for each component of a composite endpoint (e.g. different types of AMD), with the restriction that a subject can have only one type present. In this cycle, we propose to extend these methods to allow for the case where a subject can have multiple disease subtypes at the same time (e.g. different types of cataract). The methods developed in this proposal as well as in the previous cycle will be available in the form of SAS macros which can be accessed at the following web site URL:(http://www.qeocities.com/bernardrosner/channina.html).