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

项目摘要 具有全国代表性的队列对于监测人口的发病率、流行率和 阿尔茨海默病(AD)和阿尔茨海默病相关痴呆(ADRD)之间的差异，以及 了解AD/ADRD的决定因素。痴呆症的临床诊断是一项耗费时间和资源的工作 这一过程是不可能在具有人口代表性的大群体中执行的。算法痴呆症 分类方法经常被用作这一昂贵过程的替代方法。然而，目前的算法， 不能在不包含临床诊断的痴呆症病例子集的队列中发生，例如 具有全国代表性的国民健康和老龄化趋势研究(NHATS)。此外，可用的方法 只能纳入他们目标分类的所有参与者可用的措施。因此，现有的模型不能 进行调整，以包括新获得的更全面的认知数据，如2016年的数据 协调认知评估议定书(HCAP)研究。这项建议的目标是满足以下需要 人群代表性队列研究中的可扩展算法痴呆症确证。我们提出了一个灵活的 贝叶斯算法痴呆症分类框架，通过以下目标实现：(1) 将HCAP详细认知评估小组运送到(A)全部HRS人群和(B)NHATS 通过数据链接和合成数据集的生产来实现种群，以及(2)开发可扩展的模型 通过多个数据源的贝叶斯数据集成推断特定人的痴呆症概率。在……里面 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 通过模拟社会人口和健康特征的主要影响及其相互作用 对认知测试成绩的影响。在目标2中，我们将使用贝叶斯框架合并来自 多个来源，模拟社会人口统计、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)及其对痴呆症分类的交互影响。 将为这些预测因子对痴呆症概率的影响指定先验分布。人- 基于贝叶斯推理的特定痴呆症概率将用于估计痴呆症发病率， HRS和NHATS人群中痴呆症模式差异的患病率和推论。 我提交这项建议是为了支持我的论文研究，这将产生一个基础性的主体 作为AD/ADRD的一名研究员，我为自己的职业生涯工作。在这次奖学金期间，我将接受专门的培训 AD/ADRD的临床和神经心理学研究进展 研究设置。我将对AD/ADRD的文献做出贡献，改进统计方法和 创建可访问的统计计算工具，以帮助进行准确的趋势监控和构建 全面了解AD/ADRD的风险因素和差异。推进这些目标是 制定有效战略以预防AD/ADRD和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.