Nonparametric and Survival Methods in Ophthalmology

眼科非参数和生存方法

• 批准号: 8504222
• 负责人: Bernard A Rosner
• 金额: $ 42.35万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8504222
• 关键词: Accounting; Calibration; Clinical Trials; Communities; Confounding Factors (Epidemiology); Data; Databases; Discrimination; Disease; Education; Eye; Future; Genetic; Goals; Individual; Intervention Trial; Journals; Left; Macular degeneration; Measures; Methods; Modeling; Modification; Newsletter; Ophthalmology; Outcome; Paper; Patients; Persons; Research Personnel; Retinitis Pigmentosa; Risk; Risk Factors; Staging; Survival Analysis; Techniques; Testing; Visit; Writing; disorder of macula of retina; follow-up; high risk; innovation; longitudinal analysis; markov model; public health relevance; treatment trial

ABSTRACT 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.

摘要 眼科数据必然是双变量的。重要信息丢失时，眼睛具体 结果和暴露数据被分解成个人特定的分数。这需要 对标准推理方法进行调整以说明聚类。例如，混合 效应回归模型通常用于对正态分布的纵向数据建模， 但是当在对侧眼睛和重复访问之间都存在聚类时需要修改。 单独的.然而，许多眼部测量不是正态分布和非参数的 需要纵向分析方法。我们还考虑了非参数方法， 受试者可能处于不同分层的眼睛特异性协变量混杂背景 由左眼和右眼的混杂因素定义。这些是具体目标1的目标。 第二，随着以下发现，AMD的风险预测取得了重大进展： 重要的遗传预测因子。然而，普遍使用的歧视和 风险预测规则的校准需要对相关数据进行调整。此外，风险 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的病程 患者，其中随访访视次数和随访持续时间因个体而异 患者在具体目标3中，我们提出了传播信息的创新技术， 相关的数据方法，以眼科社区，包括定期通讯NEI 临床试验研究者，在ARVO提供教育课程，并撰写关于 眼科期刊的相关数据方法。