Innovative Analytical Methods for Repeated Measures in the OHTS Study

在眼科研究中，通常会遇到聚集或纵向数据。自然地，左和右 同一对象的眼睛是成对的和依赖的。此外，在纵向眼科研究中，多个 对同一主题的衡量往往是相互关联的。这些相关性(或两者之间的异质性 不同的主题)通常由两个统计模型来描述：固定效应模型和随机效应 模特们。然而，这两种方法都有其局限性。在精确度和精确度之间取得适当的平衡 和效率，我们在固定效应和随机效应模型之间提出了一种新的方法，假设 每个受试者的异质性属于不同的群体。通过惩罚聚变效应( 两个特定于对象的效果之间的差异)，我们自动将特定于对象的效果分组，而不是 提前了解受试者的组员情况。因此，我们把我们的方法称为“聚变效应”。 模特们。我们将融合效应模型应用于纵向眼科研究有三个目的：(1) 提出了融合效应模型的估计和推断方法，并应用于连续(线性)模型， 二元(Logistic)和事件发生时间(Cox模型)结果；(2)考虑多重融合效应(例如，融合 截获和融合斜率)和多层次融合效果模型；(3)开发和传播用户友好的 统计软件包，将使研究人员能够轻松地实施这些方法。 为了说明我们的方法，我们将使用眼部高血压治疗研究第一阶段和第二阶段(OHTS)，a 从1994年到2009年，对1636名参与者进行了双眼测量的纵向研究。OHTS 研究具有多种类型的结果和多层次的结构，是提出的理想研究 研究。

Clustered or longitudinal data are commonly encountered in ophthalmology studies. Naturally, left and right eyes of the same subject are paired and dependent. Also, in longitudinal ophthalmology studies, the multiple measures on the same subject tend to be correlated. These correlations (or the heterogeneity between different subjects) are usually described by two statistical models: fixed effects models and random effects models. However, both methods have their limitations. To achieve an appropriate balance between accuracy and efficiency, we propose a new approach in between the fixed effects and random effects models, assuming that the heterogeneity for each subject belongs to different groups. By penalizing the fusion effects (the differences between two subject-specific effects), we automatically group the subject-specific effects without knowing the group membership of the subjects in advance. We thus term our method as “fusion effects” models. We have three aims in utilizing the fusion effects model in longitudinal ophthalmology studies: (1) propose the estimation and inference methods for fusion effects models, with application to continuous (linear), binary (logistic), and time-to-event (Cox’s model) outcomes; (2) consider multiple fusion effects (e.g., fusion intercept and fusion slope) and multi-level fusion effects models; (3) develop and disseminate a user-friendly statistical software package that will enable researchers to implement these methods with ease. To illustrate our methods, we will use the Ocular Hypertension Treatment Study Phase I and II (OHTS), a longitudinal study with measures from both eyes of 1,636 participants followed from 1994 to 2009. The OHTS study has multiple types of outcomes and multi-level structure, which is an ideal study for the proposed research.