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

项目概要 具有全国代表性的队列对于监测人口的发病率、患病率和患病率趋势至关重要 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。临床痴呆诊断是一项耗时和资源密集型的工作 这一过程在大型人群代表群体中是不可能执行的。算法痴呆 分类方法通常被用作这一昂贵过程的替代方法。然而，当前的算法 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 具有全国代表性的国家健康和老龄化趋势研究（NHATS）。此外，可用的方法 只能纳入他们想要分类的所有参与者可用的措施。因此，现有模型无法 进行调整以包含新可用的和更全面的认知数据，例如 2016 年的数据 协调认知评估协议 (HCAP) 研究。该提案的目标是满足以下需求： 人群代表性队列研究中可扩展的算法痴呆症确定。我们提出了一种灵活的 痴呆症算法分类的贝叶斯框架，通过以下目标实现：(1) 将 HCAP 详细认知评估组运送至 (a) 全部 HRS 人群和 (b) NHATS 通过数据链接和合成数据集的生成来收集人口，以及（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推断特定人的痴呆概率。在 目标 1，我们将为 HRS 的每个参与者创建 HCAP 认知评估结果的综合版本 和 NHATS，通过对社会人口和健康特征的主要影响及其相互作用进行建模 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合并来自 多个来源来模拟社会人口、健康特征和认知测试的主要影响 表现（包括目标 1 的综合数据）及其对痴呆症分类的相互作用影响。 将指定这些预测变量对痴呆症概率的影响的先验分布。人- 基于贝叶斯推断的特定痴呆概率将用于估计痴呆发病率， 患病率以及对 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机构 作为 AD/ADRD 研究员，我的职业生涯一直在努力。在这次奖学金期间，我将接受专门的培训 AD/ADRD 在临床和临床方面的先进生物统计学方法和神经心理学观点 研究设置。我将通过统计方法和技术的进步为 AD/ADRD 的文献做出贡献 创建易于使用的统计计算工具，以帮助准确的趋势监测和建立 全面了解 AD/ADRD 的风险因素和差异。推进这些目标的核心是 制定有效策略来预防 AD/ADRD 并减少疾病差异的目标。

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.