Nonparametric and Survival Methods in Ophthalmology

摘要 眼科数据必然是两个变量。当特定于眼睛时，重要信息会丢失 结果和暴露数据被压缩成特定于个人的分数。这是必要的 对标准推理方法进行调整，以考虑到集群。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， 但当在其他眼睛之间存在聚类并且重复访问时需要修改 个人的。然而，许多目测值并不是正态分布的，也不是参数的 需要采用纵向分析的方法。我们还考虑了非参数方法在 在受试者可能处于不同层级的情况下，通过眼睛特定协变量进行混淆的背景 由左眼和右眼的混杂因素定义。这些是具体目标1的目标。 其次，随着AMD的发现，AMD的风险预测有了重大进展 重要的基因预测因子。然而，常用的歧视和歧视措施 风险预测规则的校准需要对相关数据进行调整。此外，风险 影响因素可能因黄斑病变的不同阶段而异。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的病程。 患者，随访次数和随访期因人而异 病人。在具体目标3中，我们提出了传播信息的创新技术 眼科社区的相关数据方法，包括定期向NEI发送时事通讯 临床试验研究人员，在Arvo教授教育课程，并撰写关于 眼科期刊的相关数据方法。

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.