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 具有全国代表性的队列对于监测发病率、患病率和死亡率的人口趋势至关重要。 阿尔茨海默病（AD）和阿尔茨海默病相关痴呆（ADRD）的差异，以及 了解AD/ADRD的决定因素。临床痴呆症诊断是一项时间和资源密集型的工作 这是一个不可能在大规模人群代表性队列中进行的过程。痴呆 分类方法经常被用作这种昂贵方法的替代方法。然而，目前的算法， 不能在不包含临床诊断的痴呆病例子集的队列中开发，例如 全国健康和老龄化趋势研究（NHATS）。此外，可用的方法 可以只包含他们旨在分类的所有参与者可用的措施。现有的模型不能 适应包括新的和更全面的认知数据，如2016年的数据 协调认知评估方案（HCAP）研究。该提案的目标是满足以下需求： 在人群代表性队列研究中可扩展的算法痴呆确定。我们建议灵活的 贝叶斯框架的算法痴呆症分类，通过以下目标完成：（1） 将HCAP详细的认知评估成套工具运送给（a）HRS全体人群和（B）NHATS 人口通过数据链接和生产合成数据集和（2）开发一个可扩展的模型， 通过多个数据源的贝叶斯数据集成来推断个人特定的痴呆概率。在 目标1，我们将为HRS中的每个参与者创建HCAP认知评估结果的合成版本 和NHATS通过模拟社会人口和健康特征及其相互作用的主要影响 对认知测试表现的影响。在目标2中，我们将使用贝叶斯框架来整合来自 多个来源来模拟社会人口统计学、健康特征和认知测试的主要影响 表现（包括目标1的合成数据）及其对痴呆症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─ 基于贝叶斯推理的特定痴呆概率将用于估计痴呆发病率， 患病率，以及HRS和NHATS人群中痴呆模式差异的推断。 我提交此提案是为了支持我的论文研究，该研究将产生一个基础性的 作为AD/ADRD的研究员，我为我的职业生涯而努力。在此期间，我将接受专门的培训， 先进的生物统计方法和AD/ADRD在临床和神经心理学方面的观点， 研究设置。我将通过统计方法的进步为AD/ADRD文献做出贡献， 创建可访问的统计计算工具，以帮助进行准确的趋势监测， 全面了解AD/ADRD的风险因素和差异。推进这些目标是 目标是制定有效的战略，以预防AD/ADRD和减少疾病的差异。