Use of Correlated Data Methods in Ophthalmology

项目摘要/摘要 眼科的一些暴露可能不会产生立竿见影的效果，但相反，滞后是必要的。为 例如，有一篇关于糖皮质激素滴眼液(CS)在葡萄膜炎中可能导致白内障的文献 病人。然而，剂量和/或使用时间的确切影响尚不清楚。此外，CS之间的滞后 白内障的管理和发展尚不清楚。另一个可能的应用是研究 饮食和/或补充剂在AMD的发展中的使用，在AMD的发展中也可能出现滞后效应。的目标是 具体目标1是使用眼科终点的潜伏期分析方法。延迟方法一直是 用于药物流行病学，但据我们所知，从未用于眼科终点。 AREDS研究是AMD流行病学中的一项里程碑式的研究。这项研究的一个副产品是 制定AREDS严重程度量表，这是一个从1到9+的顺序分级 高级AMD(AAMD)。通常的风险因素分析是基于COX的事件发生时间分析 比例风险模型，其中事件达到9级以上。这是一个有效的分析，但效率低下。 有许多眼睛(约40%)显示变化(或增加或减少)，但没有 发展AAMD。存在与这些变化相关的风险因素，但所有这些变化都是 被视为经审查的数据，并被视为“非事件”。在目标2中，我们建议使用顺序回归 为连续访问之间的变化建立模型，以便更有效地利用数据。有 以前已经提出了多状态序数模型，但单独的模型适用于每种可能的过渡和 没有纳入对特定协变量的总体风险评估。这不仅适用于 AMD，但也适用于其他眼科疾病，如糖尿病视网膜病变。 对于目标3，我们建议继续将相关数据方法应用于以下风险预测 终端，如AUC。我们将专门比较小样本估计AUC的方法，广泛的 并列预测分数的数量以及双侧和单侧受试者的存在。此外，我们还将 采用集群数据方法来估计NRI，据我们所知，从来没有这样做过 在此之前。 在目标4中，我们将继续为眼科研究社区翻译集群数据方法的工作 包括(A)生存分析中的相关数据方法，(B)纵向双目数据的分析，以及 (C)以眼睛为分析单位计算样本量/能量。

Project Summary / Abstract Some exposures in ophthalmology may not have an immediate effect, but instead a lag is necessary. For example, there is a literature on possible cataractogenic effects of corticosteroid eyedrops (CS) among uveitis patients. However, the precise impact of dose and/or duration of use are unknown. Also, the lag between CS administration and development of cataract is unknown. Another possible application is to study the effects of dietary and/or supplement use on development of AMD, where a lag effect is also likely to occur. The goal of specific aim 1 is to use latency analysis methods for ophthalmological endpoints. Latency methods have been used in pharmacoepidemiology, but to our knowledge, have never been used for ophthalmologic endpoints. The AREDS study was a landmark study in the epidemiology of AMD. A byproduct of this study was the development of the AREDS severity scale which is an ordinal scale ranging from 1 for no AMD to 9+ for advanced AMD (AAMD). The usual analysis for risk factors is a time-to-event analysis based on the Cox Proportional Hazards Model, where the event is reaching grade 9+. This is a valid, but inefficient analysis. There are many eyes (about 40%) which show changes (either an increase or decrease), but which don't develop AAMD. There are risk factors which are associated with these changes, but all such changes are treated as censored data and are considered “non-events”. In Aim 2, we propose to use an ordinal regression model for changes between successive visits which would provide a more efficient use of the data. There have been previous multi-state ordinal models proposed, but separate models are fit for each possible transition and are not integrated into an overall assessment of risk for specific covariates. This has application not only for AMD, but also for other ordinal scales used for other ophthalmologic conditions, such as diabetic retinopathy. For Aim 3, we propose to continue our work on applying correlated data methods to risk prediction for endpoints such as AUC. We will specifically compare methods for estimating AUC for small samples, extensive numbers of tied prediction scores and presence of both bilateral and unilateral subjects. In addition, we will incorporate clustered data methods for estimation of NRI, which to our knowledge, has never been done before. In Aim 4, we will continue our work on translation of clustered data methods for the eye research community including (a) correlated data methods in survival analysis, (b) analysis of longitudinal binary ocular data, and (c) sample size/power calculations based on the eye as the unit of analysis.