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

总结 为了开发多方面的干预措施来预防阿尔茨海默病和相关疾病（ADRD）， 是量化整个生命周期中环境暴露的联合效应以及机制的关键 通过这些接触发生作用，这涉及到动态的疾病过程。这些因果调查 面临着方法学上的挑战。本申请是对PAR-22-093的响应，将 实现以下目标：（1）开发用于因果调解的强大的贝叶斯机器学习方法 分析ADRD的生命过程观察性研究;（2）将这些方法应用于两个30+的分析 对美洲原住民（强心研究）和大波士顿地区（规范性研究）进行了为期一年的队列研究。 衰老研究），以确定高血压的发病和严重程度， 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 并传播计算效率高和用户友好的软件，以广泛应用这些方法。 拟议的工作将解决健康影响的因果调查中的方法差距。一是不 中介分析方法可以同时允许多个公开和多个 事件发生时间和纵向介质。调查人员目前只能建立不能反映真实的的模型- 生活条件，考虑到单次暴露，或产生分段结果的单一介质， 为预防提供信息。其次，ADRD研究中的观察性研究大多存在选择偏倚。 受试者可能在ADRD诊断前死亡（死亡导致的减员），或可能因认知障碍而脱落。 损伤第三，多重共线性，暴露的偏度，复杂的风险-响应关系， 时间依赖性混杂挑战有效的估计和推断。第四，没有统计方法 可用于评估ADRD的决定因素和机制的结果的普遍性。我们 我建议通过开发和应用贝叶斯机器学习方法来填补这些空白， 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和应用新的方法来估计复杂的暴露- 污染物与事件发生时间或纵向结果的响应关系可能由单个 事件发生时间或纵向介质（目标1），以及通过多个纵向和事件发生时间的机制 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测量的 混杂偏倚，并将研究样本的证据推广到目标人群（目标3）。在 SHS和NAS，我们将研究高血压和CVD轨迹，发病和严重程度的作用， 晚年作为AP和金属的神经毒性作用的介质。我们将开发用户友好和高效的R 实现所提出的新方法的包。我们的工作有很大的潜力产生广泛的影响， ADRD和其他慢性健康结果的生命过程流行病学研究和预防。

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.