Nonparametric and Survival Methods in Ophthalmology

眼科非参数和生存方法

• 批准号: 8728251
• 负责人: Bernard A Rosner
• 金额: $ 37.6万
• 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/8728251
• 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

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.

描述（由申请方提供）：眼科数据必须是双变量的。当眼睛特异性结果和暴露数据被折叠成个人特异性分数时，重要信息丢失。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对正态分布的纵向数据进行建模，但当在对侧眼和个体重复访视之间存在聚类时，需要进行修改。然而，许多眼睛的措施不是正态分布和纵向分析的非参数方法是必要的。我们还考虑了非参数方法在眼睛特异性协变量的混杂背景下，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分层中。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的风险预测取得了重大进展。然而，常用的风险预测规则的区分和校准措施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患者的病程，其中个体患者的随访访视次数和随访持续时间不同。在具体目标3中，我们提出了向眼科界传播相关数据方法信息的创新技术，包括向NEI临床试验研究者定期发送新闻稿，在ARVO开设教育课程，并为眼科期刊撰写相关数据方法的综述论文。