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病 (AD) 研究中心 (ADRC) 数据的统计方法。 第一个目标是开发稳健的程序，对重复访问中的认知障碍进行分类。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将健康受试者标记为受损。 第二个项目的重点是对高维风险因素进行系统评估，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、那些可能死亡的受试者以及那些在某个时间点仍存活且无疾病的受试者区分开来。 该项目的完成将有助于识别可预测 AD 和生存的重要风险因素。 这些新发现的生物、临床和遗传标记将指导未来针对 AD 开发针对性干预措施的研究。 所提出的方法与疾病诊断和风险筛查相关，但也可以应用于经济、金融和工程等其他领域。 该项目将通过研究生的指导将研究和教育结合起来。第一个项目涉及使用多元混合效应模型建模的认知功能的多个领域得分的纵向测量。 然后计算纵向多变量规范比较 (MNC) 统计量，以测量受试者的领域得分与健康对照的估计规范之间的距离。基于卡方近似和排列，针对纵向 MNC 提出了不同的阈值方法，以从回顾性数据中识别认知障碍。 开发了两种家庭错误率控制程序，通过将纵向 MNC 的 p 值与自适应显着性水平进行比较，动态筛查每次持续访视的认知障碍。在第二个项目中，采用最近开发的 ROC 表面下体积 (VUS) 诊断测量方法作为有序竞争终点的无模型筛选指标。 VUS 可以很容易地通过一些加权 U 统计量估计为一致性概率。所提出的基于 VUS U 型估计量的筛选程序无需任何模型假设即可对标记的辨别能力进行系统和动态的评估。作为第一个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为更广泛的高维风险筛查领域做出贡献。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.