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

阿尔茨海默病进展的动态多变量规范比较和风险筛查

• 批准号: 1916001
• 负责人: Yu Cheng
• 金额: $ 17.99万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1916001&HistoricalAwards=false
• 关键词: Dynamic; Multivariate; Normative; Comparison; Risk

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.

该项目的重点是开发用于分析阿尔茨海默氏病（AD）研究中心（ADRC）数据的统计方法。 第一个目的是制定强大的程序，以对重复访问进行认知障碍分类。这具有重要的临床意义，因为当前的诊断方法往往会错误地标记健康受试者。 第二个项目的重点是对高维风险因素的系统评估，以选择有希望的功能，这些特征可以将发展广告，可能死亡的人以及将在某个时间点上生存和疾病的人分开。 该项目的完成将导致识别重要的风险因素，这些风险因素可以预测AD和生存。 这些新确定的生物学，临床和遗传标记将指导未来的研究开发针对AD的靶向干预措施。 所提出的方法与疾病诊断和风险筛查有关，但也可能应用于其他领域，例如经济，金融和工程。 该项目将通过指导研究生来整合研究和教育。第一个项目涉及使用多元混合效应模型建模的认知功能多个领域得分的纵向测量。 然后，计算纵向多元规范性比较（MNC）统计量，以测量受试者的域分数与健康对照的估计规范之间的距离。基于卡方近似和置换术，提出了针对纵向MNC的不同阈值方法，以从回顾性数据中识别认知障碍。 通过比较来自纵向MNC的p值和适应性显着性水平的p值，开发了两个家庭视率控制程序，以动态筛选认知障碍。在第二个项目中，采用了最近开发的对ROC表面（VU）下体积的诊断度量，以作为序数竞争终点的无模型筛选度量。通过某些加权U统计量，可以很容易地将VU视为一致性概率。基于VUS的U型估计器的拟议筛选程序提供了对标记歧视能力的系统和动态评估，而无需任何模型假设。由于第一种专门针对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的法定任务，并被认为是值得通过基金会的评估来支持的。智力优点和更广泛的影响审查标准。

项目成果

Quantifying diagnostic accuracy improvement of new biomarkers for competing risk outcomes

量化新生物标志物诊断准确性的提高，以应对竞争风险结果

• DOI: 10.1093/biostatistics/kxaa048
• 发表时间: 2020
• 期刊: Biostatistics
• 影响因子: 2.1
• 作者: Wang, Zheng;Cheng, Yu;Seaberg, Eric C;Becker, James T
• 通讯作者: Becker, James T

Quantile association regression on bivariate survival data

• DOI: 10.1002/cjs.11577
• 发表时间: 2020-11-01
• 期刊: The Canadian journal of statistics = Revue canadienne de statistique
• 作者: CHEN LW;CHENG Y;DING Y;LI R
• 通讯作者: LI R

Dynamic impairment classification through arrayed comparisons

• DOI: 10.1002/sim.9601
• 发表时间: 2022-11-01
• 期刊: STATISTICS IN MEDICINE
• 影响因子: 2
• 作者: Wang，Zheng;Wang，Zi;Becker，James T.
• 通讯作者: Becker，James T.