Project 2 Measurement

项目2：摘要/摘要 项目2题为《临床前AD的新测量方法》，旨在充分利用 事实上，BIOCARD研究中的受试者已经被跟踪了长达27年。这提供了一种 识别生物标志物及其组合的无与伦比的机会，可以预测 认知正常的人会进展为轻度认知障碍，以及这些变化的点 临床前阿尔茨海默病期间的生物标记物。然而，这些丰富的数据也提出了许多分析 挑战。这些挑战包括：如何以最佳或高效的方式将生物标记物测量和 如何估计它们的变化点，以及与发病率相关的生物标志物的变化率 轻度认知障碍。因此，该项目的重点是检查这些基本的生物统计学 临床前AD研究的问题，使用来自BIOCARD研究的数据。具体目标包括：(1) 开发预测的机器学习方法，并将这些方法与现有方法进行比较，(2) 检查组合多个生物标志物以进行长期或动态预测的基于模型的方法，以及 (3)发展和比较检测生物标志物变化点的方法。这些目标是对 《生物统计学核心》中描述的应用既定方法检查数据的分析方法 将在下一个资金周期中收集。

PROJECT 2: SUMMARY/ABSTRACT Project 2 is entitled “Novel Measurement Approaches to Preclinical AD”, and is designed to capitalize on the fact that the subjects in the BIOCARD study have been followed for up to 27 years. This offers an unparalleled opportunity to identify biomarkers, together with their combinations, that can predict which cognitively normal individuals will progress to mild cognitive impairment, as well as the changepoints in these biomarkers during preclinical Alzheimer’s disease. However, this wealth of data also presents many analytical challenges. These challenges include: how to optimally or efficiently combine biomarker measurements and how to estimate their changepoints, together with rates of change in the biomarkers, in relation to the incidence of mild cognitive impairment. The focus of this project is therefore to examine these essential biostatistical issues for the study of preclinical AD, using data from the BIOCARD study. The specific aims include: (1) To develop machine learning methods of prediction and compare these approaches to existing methods, (2) to examine model-based approaches to combining multiple biomarkers for long-term or dynamic prediction, and (3) to develop and compare methods for examining changepoints in biomarkers. These aims complement the analytic approaches described in the Biostatistical Core for applying established methods to examine the data to be collected in the next funding cycle.