Project 2 Measurement

项目2：总结/摘要 项目2题为“临床前AD的新测量方法”，旨在利用 事实上，BIOCARD研究中的受试者已被随访长达27年。这提供了一个 无与伦比的机会，以确定生物标志物，连同他们的组合，可以预测， 认知正常的个体将发展为轻度认知障碍，以及这些变化点， 在临床前阿尔茨海默病的生物标志物。然而，这一丰富的数据也提出了许多分析 挑战这些挑战包括：如何最佳地或有效地将联合收割机生物标志物测量和 如何估计其变化点，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，本项目的重点是研究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） 检查基于模型的方法，以结合多种生物标志物进行长期或动态预测，以及 (3)开发和比较检测生物标志物变化点的方法。这些目标补充了 生物统计核心中描述的分析方法，用于应用已建立的方法检查数据 将在下一个供资周期收集。

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.