Nonparametric and Survival Methods in Ophthalmology

描述(申请人提供)：眼科数据是必要的双变量。当特定于眼睛的结果和暴露数据被压缩为特定于个人的分数时，重要信息就会丢失。这就需要对标准推理方法进行调整，以考虑到聚类。例如，混合效应回归模型通常用于对正态分布的纵向数据进行建模，但当同伴眼睛之间存在聚类和个人重复访问时，需要进行修改。然而，许多目测值并不是正态分布的，需要进行纵向分析的非参数方法。我们也考虑非参数方法在通过眼睛特定的协变量进行混淆的情况下，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的层中。这些都是特定目标1的目标。其次，随着重要遗传预测因子的发现，AMD的风险预测取得了重大进展。然而，常用的风险预测规则的判别和校准措施需要对相关数据进行调整。此外，危险因素可能因黄斑病变的不同阶段而不同。这就是具体目标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，其中个体患者的随访次数和随访时间有所不同。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临床试验研究人员发送时事通讯，在ARVO提供教育课程，并为眼科期刊撰写关于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.