Statistical Methods for Ophthalmologic and Cluster Data

眼科和聚类数据的统计方法

基本信息

批准号：
7456385
负责人：
Bernard A Rosner
金额：
$ 32.89万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
1998
资助国家：
美国
起止时间：
1998-08-01 至 2010-06-30
项目状态：
已结题

项目摘要

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).

描述（由申请人提供）：眼科数据给数据分析师带来了具有挑战性的方法学问题。首先，提供一个人两只眼睛的数据，这些数据通常呈正相关，但不相同。面临的挑战是如何以有效的方式使用所有数据，以解释其他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，这一领域已经取得了实质性进展。 SAS 过程 PROC MIXED 和 PROC GENMOD 有效地处理这些类型的数据。然而，眼科中的许多量表本质上是有序的（例如 ETDRS 糖尿病视网膜病变量表、LOGS III 白内障量表），并且一些连续量表（例如 Humphrey 视野）往往是倾斜的和非正态分布的。因此，非参数分析对于两组比较是可取的。在本次资助的上一个周期中，我们扩展了 Wilcoxon 秩和检验和符号秩检验，以在随机化单位是受试者、但分析单位是眼睛和双眼的情况下考虑聚类数据。受试者被随机分配接受相同的治疗。在此更新应用程序中，我们扩展了这些方法以涵盖随机化单位为主题但其他眼睛可能会或可能不会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 秩和检验分析的聚类数据提供功效和样本大小的估计，并评估当聚类大小不等时不同加权方法的效率。此外，我们建议将我们的聚类数据 Wilcoxon 秩和检验方法应用于评估 GEE 模型拟合优度的重要问题，无论是拟合纵向数据还是拟合具有相关结果的横截面数据（例如，对视）。最后，在上一个资助周期中，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，但限制是受试者只能存在一种类型。在这个周期中，我们建议扩展这些方法，以允许受试者同时患有多种疾病亚型（例如不同类型的白内障）的情况。本提案以及上一周期中开发的方法将以 SAS 宏的形式提供，可以通过以下网站 URL 访问：(http://www.qeocities.com/bernardrosner/channina.html) 。