Bayesian Statistical Learning for Robust and Generalizable Causal Inferences in Alzheimer Disease and Related Disorders Research

贝叶斯统计学习在阿尔茨海默病和相关疾病研究中进行稳健且可推广的因果推论

• 批准号: 10590913
• 负责人: Linda Valeri
• 金额: $ 56.81万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-15 至 2028-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10590913
• 关键词: Accounting; Address; Aging; Air Pollutants; Air Pollution; Algorithms; Alzheimer disease prevention; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Area; Arsenic; Bayesian learning; Blood Pressure; Boston; Cadmium; Carbon Black; Cardiovascular Diseases; Cardiovascular system; Cessation of life; Chronic; Cognitive; Cohort Studies; Complex; Data; Dementia; Diagnosis; Disease; Dropout; Elderly; Ensure; Environmental Exposure; Environmental Policy; Epidemiology; Etiology; Event; Face; Functional disorder; Goals; Health; Health Care Costs; Heart; Heavy Metals; Hypertension; Impaired cognition; Incidence; Individual; Intervention; Investigation; Joints; Life; Life Cycle Stages; Life course epidemiology; Link; Longevity; Longitudinal Studies; Measurement; Mediating; Mediation; Mediator; Metals; Methodology; Methods; Modeling; Native Americans; Observational Study; Outcome; Participant; Population Heterogeneity; Prevention; Process; Public Health; Reproducibility; Research; Research Personnel; Risk Factors; Role; Sampling Studies; Science; Selection Bias; Severities; Statistical Methods; Target Populations; Testing; Time; Work; cardiovascular health; cognitive function; cohort; data fusion; disorder prevention; epidemiology study; hypertension prevention; machine learning method; middle age; model building; modifiable risk; multi-component intervention; neurotoxic; novel; novel strategies; pollutant; prevent; resilience; response; simulation; statistical learning; tool; translational study; usability; user friendly software; user-friendly

SUMMARY To develop multi-faceted interventions for Alzheimer’s Disease and Related Disorders (ADRD) prevention, it is key to quantify joint effects of environmental exposures throughout the life-span as well as the mechanisms through which the exposures operate, which involve dynamic disease processes. These causal investigations in observational studies face methodological challenges. This application, in response to PAR-22-093, will accomplish the following goals: (1) develop robust Bayesian machine learning methods for causal mediation analyses in life-course observational studies of ADRD; (2) apply these approaches in the analysis of two 30+ year cohort studies of Native Americans (Strong Heart Study) and of the Greater Boston area (Normative Aging Study) to determine whether and to what extent the onset and severity of hypertension and cardiovascular disease mediate the harmful effects of air pollution and heavy metals on ADRD; (3) develop and disseminate computationally efficient and user-friendly software for widespread application of the methods. The proposed work will address methodologic gaps in the causal investigation of health effects. First, no mediation analyses approaches are available that simultaneously allow for multiple exposures and multiple time-to-event and longitudinal mediators. Investigators can currently only build models that do not reflect real- life conditions, considering a single exposure, or a single mediator producing segmented results, limited in informing prevention. Second, most of observational studies in ADRD research are plagued by selection bias. Participants may die before an ADRD diagnosis (attrition due to death) or may drop-out due to cognitive impairment. Third, multicollinearity, skewness of exposures, complex exposure-response relationships and time dependent confounding challenge valid estimation and inference. Fourth, no statistical approaches are available to evaluate the generalizability of findings on determinants of and mechanisms leading to ADRD. We propose to fill these gaps by developing and applying Bayesian machine learning approaches for quantifying the total, direct and indirect effects of environmental and health factors on ADRD outcomes under the counterfactual framework. We will develop and apply the new methods to estimate complex exposure- response relationships of pollutants with time-to-event or longitudinal outcome potentially mediated by a single time-to-event or longitudinal mediator (Aim 1), and mechanisms through multiple longitudinal and time-to-event mediators (Aim 2). Furthermore, we will develop Bayesian data fusion algorithms to evaluate unmeasured confounding bias and to generalize evidence from the study sample to the target population (Aim 3). In the SHS and NAS we will investigate the role of hypertension and CVD trajectory, onset, and severity in mid and late life as mediators of the neurotoxic effects of AP and metals. We will develop user-friendly and efficient R packages that implement the proposed new methods. Our work has great potential to have broad impact on life-course epidemiology research and prevention for ADRD and other chronic health outcomes.

概括 为了开发有关阿尔茨海默氏病和相关疾病（ADRD）预防的多方面干预措施 是量化整个生命中环境暴露的关节影响以及机制的关键 通过该暴露的运作，涉及动态疾病过程。这些因果研究 在观察性研究中面临方法论上的挑战。该应用程序响应于PAR-22-093，将 实现以下目标：（1）为因果关系开发强大的贝叶斯机器学习方法 ADRD生命课程研究中的分析； （2）将这些方法应用于两个30+的分析 美国原住民的年同队研究（强大的心脏研究）和大波士顿地区（规范性 衰老研究），以确定高血压的发作和严重程度以及在何种程度上 心血管疾病介导空气污染和重金属对ADRD的有害影响； （3）发展 并传播计算高效且用户友好的软件，以应用方法的宽度应用。 拟议的工作将解决健康影响因果投资中的方法论差距。首先，不 可以使用调解分析方法，仅允许多次暴露和多个暴露 活动时间和纵向调解人。目前，调查人员只能构建不反映现实的模型 生活状况，考虑一次暴露或产生分段结果的单一调解人，限制 告知预防。其次，ADRD研究中的大多数观察性研究都受到选择偏见的困扰。 参与者可能会在诊断之前死亡（由于死亡导致的消耗）或可能由于认知而辍学 损害。第三，多重共线性，暴露的偏度，复杂的暴露响应关系和 时间依赖的混淆挑战有效的估计和推理。第四，没有统计方法是 可用于评估有关导致ADRD的确定性和机制的发现的普遍性。我们 通过开发和应用贝叶斯机器学习方法来量化这些空白的建议 环境和健康因素对ADRD结果的总体，直接和间接影响 反事实框架。我们将开发并应用新方法来估计复杂暴露 - 污染物与事件时间或纵向结果的响应关系可能由一个单一介导 事件时间或纵向调解人（AIM 1），以及通过多个纵向和事件的机制 调解人（目标2）。此外，我们将开发贝叶斯数据融合算法来评估未计量的 将偏见混淆并概括从研究样本到目标人群的证据（目标3）。在 SHS和NAS我们将调查高血压和CVD轨迹，发作和严重程度的作用 后期生活是AP和金属神经毒性作用的介体。我们将开发用户友好和高效的r 实施提出的新方法的软件包。我们的工作具有巨大的潜力，可以对 ADRD和其他慢性健康成果的生命过程流行病学研究和预防。