Link, transport, integrate: a Bayesian data integration framework for scalable algorithmic dementia classification in population-representative studies

链接、传输、集成：用于人口代表性研究中可扩展算法痴呆分类的贝叶斯数据集成框架

• 批准号: 10555237
• 负责人: Crystal Shaw
• 金额: $ 3.08万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-04 至 2023-07-03
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10555237
• 关键词: Address; Aging; Algorithms; Alzheimer' s Disease; Alzheimer' s disease related dementia; Attention; Bayesian Analysis; Bayesian Modeling; Biostatistical Methods; Characteristics; Classification; Clinical; Cognition; Cognitive; Cohort Studies; Consensus; Data; Data Linkages; Data Set; Dementia; Diagnosis; Disease; Disparity; Ethnic Population; Fellowship; Goals; Grant; Health; Health and Retirement Study; Hour; Incidence; Interview; Joints; Language; Link; Literature; Measures; Memory; Methodology; Methods; Modeling; Monitor; Neuropsychology; Outcome; Outcome Assessment; Participant; Pattern; Performance; Persons; Population; Population trends; Prevalence; Probability; Process; Production; Protocols documentation; Public Health; Research; Research Personnel; Resources; Review Literature; Risk Factors; Sampling Studies; Source; Specific qualifier value; Statistical Computing; Statistical Methods; Study models; Target Populations; Time; Training; Visuospatial; Work; adjudication; algorithm development; algorithm training; career; classification algorithm; clinical diagnosis; cognitive testing; cohort; cost; data integration; data modeling; demographics; direct application; disorder risk; disparity reduction; executive function; flexibility; health goals; improved; insight; instrument; multiple data sources; performance tests; predictive modeling; prevent; racial population; sociodemographic variables; sociodemographics; study population; tool; trend; user friendly software

Project Summary Nationally representative cohorts are crucial for monitoring population trends in incidence, prevalence, and disparities in Alzheimer’s disease (AD) and Alzheimer’s disease-related dementias (ADRD), as well as for understanding determinants of AD/ADRD. Clinical dementia diagnosis is a time- and resource- intensive process that is impossible to perform in large population-representative cohorts. Algorithmic dementia classification methods are often used as alternatives to this costly process. Current algorithms, however, cannot be developed in cohorts that do not contain a subset of clinically diagnosed dementia cases, such as the nationally representative National Health and Aging Trends Study (NHATS). Further, available methods can only incorporate measures available for all participants they aim to classify. Thus, existing models cannot be adapted to include newly available and more comprehensive cognitive data such as data from the 2016 Harmonized Cognitive Assessment Protocol (HCAP) Study. The goal of this proposal is to fill the need for scalable algorithmic dementia ascertainment in population-representative cohort studies. We propose a flexible Bayesian framework for algorithmic dementia classification, accomplished through the following aims: (1) transport the HCAP detailed cognitive assessment battery to (a) the full HRS population and (b) the NHATS population through data linkage and production of synthetic datasets and (2) develop a scalable model for inferring person-specific dementia probabilities through Bayesian data integration of multiple data sources. In Aim 1, we will create synthetic versions of HCAP cognitive assessment outcomes for each participant in HRS and NHATS by modeling main effects of socio-demographic and health characteristics and their interaction effects on cognitive test performance. In Aim 2, we will use a Bayesian framework to incorporate data from multiple sources to model the main effects of socio-demographic, health characteristics, and cognitive test performance (including synthetic data from Aim 1) and their interaction effects on dementia classifications. Prior distributions will be specified for the effects of these predictors on the probability of dementia. Person- specific dementia probabilities based on Bayesian inference will be used to estimate dementia incidence, prevalence, and inferences about disparities in dementia patterns in the HRS and NHATS populations. I am submitting this proposal to support my dissertation research which will produce a foundational body of work for my career as a researcher in AD/ADRD. During this fellowship, I will receive specialized training in advanced biostatistical methods and neuropsychological perspectives of AD/ADRD in both the clinical and research settings. I will contribute to the literature on AD/ADRD with advancements in statistical methods and create accessible statistical computing tools to aid efforts in accurate trend monitoring and building a comprehensive understanding of risk factors and disparities in AD/ADRD. Advancing these aims is central to the goal of developing effective strategies to prevent AD/ADRD and reduce disparities in the disease.

项目摘要 国家代表人群对于监测发生率，患病率和 阿尔茨海默氏病（AD）和阿尔茨海默氏病与疾病相关的痴呆症（ADRD）的差异，以及 理解确定AD/ADRD。临床痴呆诊断是一种时间和资源密集型 在大型人群代表人群中不可能执行的过程。算法痴呆 分类方法通常用作此代价高昂的过程的替代方法。但是，当前的算法，但是 不能在不包含一部分临床诊断痴呆症病例的队列中开发 全国代表性的民族健康和衰老趋势研究（NHATS）。此外，可用方法 只能为所有目标分类的参与者提供可用的措施。那，现有模型不能 可以适应新的可用和更全面的认知数据，例如2016年的数据 统一认知评估方案（HCAP）研究。该提议的目的是满足需求 可扩展的算法痴呆症确定性在人群代表性队列研究中。我们提出了一个灵活的 算法痴呆分类的贝叶斯框架，通过以下目的完成：（1） 将HCAP详细的认知评估电池运输到（a）全HRS人群和（b）NHATS 通过数据链接和合成数据集的生产的人口，（2）开发一个可扩展模型 通过贝叶斯数据集成多个数据源来推断人特定的痴呆可能性。在 AIM 1，我们将为每个参与HRS的HCAP认知评估结果创建合成版本 和NHAT通过建模社会人口统计学和健康特征的主要影响及其相互作用 对认知测试表现的影响。在AIM 2中，我们将使用贝叶斯框架合并来自 多种来源来建模社会人口统计学，健康特征和认知测试的主要影响 性能（包括来自AIM 1的合成数据）及其对痴呆分类的相互作用影响。 将指定这些预测因子对痴呆概率的影响的先验分布。人- 基于贝叶斯推论的特定痴呆可能性将用于估计痴呆事件， 患病率以及HRS和NHAT人群中痴呆模式差异的推论。 我正在提交此建议，以支持我的论文研究，该研究将产生一个基础 在我在AD/ADRD的研究人员的职业生涯中工作。在此奖学金期间，我将获得专门的培训 在临床和 研究环境。我将为有关AD/ADRD的文献做出贡献，并取得统计方法的进步和 创建可访问的统计计算工具，以帮助努力进行准确的趋势监控和建立 对AD/ADRD中风险因素和差异的全面理解。推进这些目标是 制定有效策略以防止AD/ADRD并减少疾病分布的目的。