Data-Driven Discovery of Heterogeneous Treatment Effects of Statin Use on Dementia Risk

项目概要（摘要） 阿尔茨海默病和相关痴呆症（ADRD）目前影响着400多万美国人， 全球5000万人。确定痴呆症的预防策略至关重要， 由于缺乏有效的治疗。与此同时，越来越多的人认为脂质代谢是一个主要的 ADRD的贡献者，可能是减少和预防风险的重要策略。降血脂 代理（即，他汀类药物）被广泛使用，但是关于抗高血压药物（即， 他汀类药物）和ADRD在很大程度上没有定论。对这种混合结果的一种可能解释是 研究人群的异质性及其特征。例如，他汀类药物的有效性是 证明其随年龄、ApoE 4状态和预先存在的疾病状态而变化。34 -36因此， 需要识别因素（即，影响他汀类药物作用异质性的效应调节剂）， 痴呆本研究的目的是三角证据的识别和估计 通过使用三种因果机器学习方法，特别是诚实的 因果森林/策略树、双重鲁棒自适应LASSO和贝叶斯自适应回归树（BART）， 确定新的效果调节剂和最佳亚组的他汀类药物对痴呆症的影响。而传统 参数回归方法被设计用于测试关于效应修正的先验假设，例如 这些方法不适合于产生新的假设。中描述的因果机器学习方法 这个提议填补了这一空白;这样的方法不仅有助于识别新的效果调节剂，而且它们还可以 便于随后识别这些修改器之间的最佳处理规则。在这项研究中，我建议 使用来自英国生物银行数据的307，719人的队列，他们最初至少55岁， 从2006年到2010年。分析样本会很大，让我可以严格调查 不同亚组的治疗效果不一致。具体而言，在目标1中，我建议估计 整个样本中他汀类药物对ADRD的真实世界平均治疗效应（ATE）。然后，在目标2中， 应用三种因果机器学习算法来识别新的效果修饰符和相应的最佳效果修饰符。 使用他汀类药物对ADRD风险影响的亚组。最后，在目标3中，我将量化ADRD的减少 实施目标2下产生的每一个最佳治疗规则所产生的病例， 将其与目标1下观察到的ADRD病例减少进行比较。本F31提案申请将 支持我的论文研究，以及我对获得因果机器学习培训的兴趣，以及 作为痴呆症、认知老化及其心理测量方法的实质性培训。在我的指导下， 导师团队，我期待着推进痴呆症预防研究，同时也追求我的目标， 成为认知老化研究方法的独立研究者。

PROJECT SUMMARY (ABSTRACT) Alzheimer’s Disease and Related Dementias (ADRD) currently affects more than 4 million Americans and over 50 million individuals worldwide. The identification of prevention strategies for dementia is critical, particularly due to the lack of effective treatments. In parallel, there is growing consensus that lipid metabolism is a major contributor to ADRD and may be an important strategy for risk reduction and prevention. Antihyperlipidemic agents (i.e., statins) are widely used, yet evidence on the relationship between antihyperlipidemic agents (i.e., statins) and ADRD has been largely inconclusive. One possible explanation for the mixed findings is heterogeneity in study populations and their characteristics. For example, the effectiveness of statins is evidenced to vary by age, ApoE4 status, and pre-existing disease status.34-36 Accordingly, there is a growing need to identify the factors (i.e., effect modifiers) which influence heterogeneities in the effect of statins on dementia. The objective of this study is to triangulate evidence on the identification and estimation of heterogeneous treatment effects by using three causal machine learning methods, specifically the honest causal forest/policy tree, doubly robust adaptive LASSO, and Bayesian Adaptive Regression Trees (BART), to identify novel effect modifiers and optimal subgroups for the effect of statins on dementia. While traditional parametric regression approaches are designed to test a priori hypotheses regarding effect modification, such approaches are not suitable for yielding novel hypotheses. The causal machine learning methods described in this proposal fill this gap; not only do such approaches help identify novel effect modifiers, but they can also facilitate the subsequent identification of optimal treatment rules across those modifiers. In this study, I propose to use a cohort of 307,719 individuals from the UK Biobank data who were at least 55 when they were initially recruited from 2006 to 2010. The analytical sample will be large, allowing me to rigorously investigate heterogeneous treatment effects across different subgroups. Specifically, in Aim 1, I propose to estimate the real-world average treatment effect (ATE) of statins on ADRD across the entire sample. I will then, in Aim 2, apply three causal machine learning algorithms to identify novel effect modifiers and corresponding optimal subgroups for the effect of statin use on ADRD risk. Finally, in Aim 3, I will quantify the reduction in ADRD cases that would result from implementing each of the optimal treatment rules generated under Aim 2 and compare them to the reduction in ADRD cases observed under Aim 1. This F31 proposal application will support my dissertation research, as well as my interest in gaining training in causal machine learning, as well as substantive training in dementia, cognitive aging, and its psychometric methods. Under the guidance of my mentorship team, I look forward to advancing dementia prevention research while also pursuing my goal of becoming an independent investigator in research methods on cognitive aging.