Dynamic Multivariate Normative Comparison and Risk Screening for Alzheimer's Disease Progression

这个项目的重点是开发统计方法来分析阿尔茨海默病(AD)研究中心(ADRC)的数据。第一个目标是制定可靠的程序，对反复就诊时的认知障碍进行分类。这具有重要的临床意义，因为目前的诊断方法往往错误地将健康受试者标记为受损。第二个项目的重点是系统地评估高维风险因素，以选择有希望的特征，这些特征可以将那些将发展为AD的受试者与那些可能死亡的受试者区分开来，以及那些将在特定时间点之前存活并没有疾病的受试者。该项目的完成将导致确定预测阿尔茨海默病和存活率的重要风险因素。这些新发现的生物、临床和遗传标记将指导未来的研究，为AD制定有针对性的干预措施。所提出的方法与疾病诊断和风险筛查相关，但也可以应用于其他领域，如经济、金融和工程。该项目将通过指导研究生将研究和教育结合起来。第一个项目涉及使用多变量混合效应模型建模的认知功能多领域得分的纵向测量。然后计算纵向多变量标准比较(MNC)统计量，以衡量受试者的领域得分与健康对照组估计常模之间的距离。为了从回溯数据中识别认知损伤，基于卡方近似和排列的纵向MNC提出了不同的阈值方法。通过比较纵向MNC的p值和适应性显著性水平，开发了两个家庭错误率控制程序，以在每次正在进行的访问中动态筛查认知障碍。在第二个项目中，采用了最近开发的ROC表面下体积(VUS)的诊断度量作为有序竞争端点的无模型筛选度量。通过一些加权的U-统计量，可以容易地将VUS估计为协调概率。所提出的基于VU的U型估计器的筛查程序在没有任何模型假设的情况下提供了对标记物的区分能力的系统和动态评估。作为第一个专门为有序疾病进展开发的筛查方法，第二个项目的成功完成将有助于更广泛的高维风险筛查领域。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project focuses on the development of statistical methods for analyzing data from the Alzheimer Disease (AD) Research Center (ADRC). The first objective is to develop robust procedures to classify cognitive impairment over repeated visits. This has important clinical implications, as current diagnostic methods tend to falsely flag healthy subjects as impaired. The second project focuses on systematic evaluation of high-dimensional risk factors to select promising features that can separate those subjects who will develop AD, from those who might die, and those who will be alive and disease free by a certain time point. The completion of this project will lead to the identification of important risk factors that are predictive of both AD and survival. These newly identified biological, clinical, and genetic markers will guide future studies developing targeted intervention for AD. The proposed methods are relevant for disease diagnosis and risk screening but may also be applied to other areas such as economics, finance and engineering. The project will integrate research and education through the mentoring of graduate students. The first project concerns longitudinal measures of multiple domain scores of cognitive functioning modeled using multivariate mixed-effect models. A longitudinal multivariate normative comparison (MNC) statistic is then computed to measure the distance between a subject's domain scores and the estimated norm of healthy controls. Different thresholding methods are proposed for the longitudinal MNC based on the Chi-square approximation and permutation to identify cognitive impairment from retrospective data. Two familywise-error-rate controlling procedures are developed to dynamically screen for cognitive impairment at each ongoing visit, by comparing the p-values from the longitudinal MNC with adaptive significance levels. In the second project, a recently developed diagnostic measure of the volume under the ROC surface (VUS) is adopted as a model-free screening metric for ordinal competing endpoints. The VUS can be readily estimated as a concordance probability by some weighted U-statistics. The proposed screening procedure based on the U-type estimator of the VUS provides systematic and dynamic evaluation of markers' discriminatory capacity without any model assumptions. As the first screening method developed specifically for ordinal disease progression, the successful completion of the second project will contribute to the broader field of high-dimensional risk screening.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.