Nonparametric and Survival Methods in Ophthalmology

眼科非参数和生存方法

• 批准号: 8926995
• 负责人: Bernard A Rosner
• 金额: $ 37.54万
• 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 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8926995
• 关键词: Accounting; Calibration; Clinical Trials; Communities; Confounding Factors (Epidemiology); Data; Databases; Discrimination; Disease; Education; Eye; Future; Goals; Health; 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; genetic predictors; high risk; innovation; longitudinal analysis; markov model; 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（由申请人提供）：眼科数据必然是双变量。当特定于眼睛的结果和暴露数据被分解为特定于个人的分数时，重要信息就会丢失。这需要调整标准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，但当同眼之间和个人重复访问之间都存在聚类时，需要进行修改。然而，许多视觉测量不是正态分布的，需要纵向分析的非参数方法。我们还考虑在眼睛特定协变量混杂的背景下考虑非参数方法，其中受试者可能处于由左眼和右眼混杂因素定义的不同阶层。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 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，危险因素可能因黄斑病的不同阶段而异。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，包括定期向 NEI 临床试验研究者发送通讯、在 ARVO 提供教育课程以及为眼科期刊撰写相关数据方法的评论论文。