Nonparametric and Survival Methods in Ophthalmology

描述（由申请人提供）：眼科数据必须是双变量的。当眼睛特定的结果和暴露数据被分解成个人特定的分数时，重要的信息就丢失了。这就需要调整标准的推理方法来解释聚类。例如，混合效应回归模型通常用于模拟正态分布的纵向数据，但当在同伴眼睛和重复访问的个体之间存在聚类时，需要修改。然而，许多目测值并非正态分布，需要采用非参数的纵向分析方法。我们还考虑了由眼睛特定协变量混淆的背景下的非参数方法，其中受试者可能处于由左眼和右眼的混杂因素定义的不同层。这些是具体目标1的目标。其次，随着重要遗传预测因子的发现，AMD的风险预测也取得了重大进展。然而，常用的判别和校准风险预测规则的措施需要调整相关数据。此外，危险因素可能因黄斑病变的阶段而异。这是具体目标2的目标。在具体目标3中，我们寻求使用经验贝叶斯方法来更好地预测个体RP患者的病程，其中随访次数和随访时间因个体患者而异。在具体目标3中，我们提出了向眼科社区传播相关数据方法信息的创新技术，包括定期向NEI临床试验研究者发送通讯，在ARVO提供教育课程，并为眼科期刊撰写有关相关数据方法的综述论文。

DESCRIPTION (provided by applicant): Ophthalmic data is of necessity bivariate. Important information is lost when eye-specific outcome and exposure data are collapsed into person-specific scores. This necessitates adjustment to standard inferential methods to account for clustering. For example, mixed effects regression models are commonly used to model normally distributed longitudinal data, but require modification when clustering exists both among fellow eyes and repeat visits for an individual. However, many ocular measures are not normally distributed and nonparametric methods of longitudinal analysis are needed. We also consider nonparametric methods in the context of confounding by eye-specific covariates where a subject may be in different strata defined by confounders for the left and right eye. These are the goals of specific aim 1. Secondly, there have been major advances in risk prediction for AMD with the discovery of important genetic predictors. However, commonly used measures of discrimination and calibration of risk prediction rules require adjustment for correlated data. Furthermore, risk factors may vary by stage of maculopathy. This is the goal of specific aim 2. In specific aim 3, we seek to use empirical Bayes methods to better predict disease course for individual RP patients, where the number of follow-up visits and duration of follow-up differs for individual patients. In specific aim 3, we propose innovative techniques for disseminating information on correlated data methods to the ophthalmic community including periodic newsletters to NEI clinical trial investigators, giving education courses at ARVO and writing review papers on correlated data methods for ophthalmic journals.