Statistical Methods for Ophthalmologic and Cluster Data

描述(由申请人提供)：眼科数据向数据分析师提出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通常是正相关的，但并不相同。挑战是如何有效地使用所有数据，以解释其他眼睛的分数之间的相关性。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，眼科的许多标尺本质上是有序的(如ETDRS糖尿病视网膜病变标尺、Logs III白内障标尺)，而一些连续标尺(如Humphrey视野)倾向于倾斜和非正态分布。因此，非参数分析对于两组比较是可取的。在这项资助的前一个周期中，我们扩展了Wilcoxon秩和检验和符号秩和检验，以说明在随机单位是受试者，但分析单位是眼睛，受试者的两只眼睛被随机分配到相同治疗的情况下的聚集数据。在这一更新申请中，我们将这些方法扩展到以随机化单元为对象，但对侧眼可能接受或可能不接受相同治疗的情况。这通常发生在眼部过敏试验中。此外，我们建议扩展我们的方法，为通过Wilcoxon秩和检验分析的集群数据提供功率和样本大小的估计，并评估当集群大小不相等时不同加权方法的效率。此外，我们建议将我们的方法应用于集群数据的Wilcoxon秩和检验，以评估GEE模型的拟合优度这一重要问题，无论是对纵向数据还是对具有相关结果的横截面数据(例如，同胞眼睛)。最后，在赠款的前一个周期中，我们研究了评估暴露-疾病关系对于复合终点的每个组成部分(例如，不同类型的AMD)是否相同的方法，并限制了受试者只能存在一种类型。在这个周期中，我们建议将这些方法扩展到允许受试者同时具有多种疾病亚型的情况(例如，不同类型的白内障)。本提案和上一个周期中制定的方法将以SAS宏的形式提供，可从以下网站URL：(http://www.qeocities.com/bernardrosner/channina.html).获取

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).