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

项目摘要(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 全球有5000万人。确定痴呆症的预防策略至关重要，尤其是 由于缺乏有效的治疗方法。与此同时，越来越多的人一致认为，脂类代谢是一种主要的 这可能是减少和预防风险的一项重要战略。降高脂血症 药物(即他汀类药物)被广泛使用，但有证据表明抗高脂药(即， 他汀类药物)和ADRD在很大程度上没有定论。对这一混合发现的一种可能解释是 研究群体的异质性及其特征。例如，他汀类药物的有效性是 有证据表明，随着年龄、载脂蛋白E4状态和既往疾病状态的不同而不同。34-36相应地， 需要确定影响他汀类药物作用异质性的因素(即效应调节剂) 痴呆症。这项研究的目的是三角测量证据，以确定和估计 使用三种因果机器学习方法的异质性治疗效果，特别是诚实的 因果森林/策略树、双稳健自适应套索和贝叶斯自适应回归树(BART)，以 针对他汀类药物对痴呆的影响，确定新的效应调节剂和最佳亚组。虽然是传统的 参数回归方法被设计来测试关于效果修改的先验假设，例如 方法不适合产生新的假设。中描述的因果机器学习方法 这一提议填补了这一空白；这种方法不仅有助于识别新的效果修饰剂，而且它们还可以 促进随后确定这些修改剂中的最佳处理规则。在这项研究中，我建议 使用来自英国生物库数据的307,719人的队列，这些人最初年龄至少为55岁 2006年至2010年招募。分析样本会很大，让我可以严谨地调查 不同亚组之间的不同治疗效果。具体地说，在目标1中，我建议估计 他汀类药物在整个样本中对ADRD的真实世界平均治疗效果(ATE)。然后，我将在目标2中， 应用三种因果机器学习算法识别新的效果修改器和相应的最优 他汀类药物使用对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和目标2下产生的每项最佳治疗规则所产生的案例 将它们与目标1下观察到的ADRD病例的减少进行比较。此F31提案应用程序将 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 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.