Project 2 Measurement

项目2 测量

• 批准号: 10636941
• 负责人: Mei Cheng Wang
• 金额: $ 30.83万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10636941
• 关键词: Adopted; Age; Aging; Algorithms; Alzheimer' s Disease; Biological Markers; Biomedical Research; Biometry; Biostatistics Core; Classification; Cognitive; Complement; Data; Diagnostic; Disease; Disease Outcome; Event; Funding; Goals; Hazard Models; Impaired cognition; Incidence; Individual; Measurement; Measures; Methods; Modeling; Outcome; Performance; Proportional Hazards Models; Publishing; ROC Curve; Specific qualifier value; Statistical Methods; Structure; Symptoms; Time; Trees; Update; Work; biomarker identification; classification trees; cohort; design; experience; flexibility; forest; high dimensionality; indexing; machine learning method; mild cognitive impairment; novel; pre-clinical; preclinical study; rate of change; risk prediction; semiparametric; time interval

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.

项目2：摘要/摘要 项目2的标题为“临床前广告的新型测量方法”，旨在大写 生物卡研究中的受试者已遵循长达27年的事实。这提供了 无与伦比的机会识别生物标志物以及其组合，可以预测哪种 认知正常人将发展为轻度认知障碍，以及这些变更点 临床前阿尔茨海默氏病期间的生物标志物。但是，这些丰富的数据也提出了许多分析 挑战。这些挑战包括：如何最佳或有效地结合生物标志物测量和 如何估计其变更点以及生物标志物的变化率与事件有关 轻度认知障碍。因此，该项目的重点是检查这些基本生物统计学 使用Biocard研究的数据，研究临床前AD的问题。具体目的包括：（1）至 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）与 检查基于模型的方法，以合并多个生物标志物进行长期或动态预测，以及 （3）开发和比较用于检查生物标志物中变更点的方法。这些目标完成了 生物统计核心中描述的分析方法用于应用既定方法检查数据 要在下一个资金周期中收集。