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

他汀类药物使用对痴呆风险的异质治疗效果的数据驱动发现

基本信息

批准号：
10678219
负责人：
Neal Jawadekar
金额：
$ 4.39万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10678219
关键词：
Affect Age Aging Algorithms Alzheimer&apos s disease related dementia Alzheimer&apos s disease risk American Award Behavioral Biological Biological Markers Characteristics Clinical Clinical Trials Cognitive aging Consensus Data Data Set Dementia Diagnosis Disease Economics Effectiveness Genetic Goals Grant Heterogeneity Hyperlipidemia Impaired cognition Individual Investigation Machine Learning Mentorship Methods Mission Modification Nature Observational Study Participant Pharmaceutical Preparations Policies Prevalence Prevention Prevention Research Prevention strategy Probability Prospective cohort Psychometrics Public Health Qualifying Race Reproducibility of Results Research Research Methodology Research Personnel Research Proposals Research Training Resources Risk Reduction Sampling Subgroup Testing Training Trees United States advanced dementia aged apolipoprotein E-4 biobank career cognitive function cohort comorbidity dementia risk design effective therapy follow-up forest improved innovation interest lipid metabolism machine learning algorithm machine learning method novel optimal treatments patient subsets precision medicine prevent recruit regression trees sex skills social sociodemographics study population treatment effect

项目摘要

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.

项目概要（摘要）阿尔茨海默病和相关痴呆症 (ADRD) 目前影响超过 400 万美国人及以上确定全球 5000 万人的痴呆症预防策略至关重要。与此同时，由于缺乏有效的治疗方法，人们越来越认识到脂质代谢是一个主要因素。 ADRD 的贡献者，可能是降低和预防风险的重要策略。药物（即他汀类药物）被广泛使用，但降血脂药物（即，他汀类药物）和 ADRD 在很大程度上尚无定论，对这一混合结果的一种可能解释是。研究人群及其特征的异质性例如，他汀类药物的有效性是。事实证明，其因年龄、ApoE4 状态和既往疾病状态而异。34-36 因此，越来越多的人需要确定影响他汀类药物疗效异质性的因素（即效果调节剂）本研究的目的是对痴呆症的识别和评估证据进行三角测量。通过使用三种因果机器学习方法（特别是诚实的方法）来获得异质治疗效果因果森林/策略树、双鲁棒自适应 LASSO 和贝叶斯自适应回归树 (BART)，确定他汀类药物对痴呆症的影响的新效应调节剂和最佳亚组。参数回归方法旨在测试有关效果修改的先验假设，例如方法不适合产生新颖的假设。该提案填补了这一空白；这些方法不仅有助于识别新颖的效果调节剂，而且还可以在这项研究中，我建议促进随后识别这些修饰符的最佳治疗规则。使用来自英国生物银行数据的 307,719 名个体的队列，这些人最初年龄至少为 55 岁从2006年到2010年招募的。分析样本很大，可以让我进行严格的调查具体而言，在目标 1 中，我建议估计不同亚组的异质治疗效果。然后，在目标 2 中，我将计算他汀类药物对整个样本中 ADRD 的真实平均治疗效果 (ATE)。应用三种因果机器学习算法来识别新颖的效果调节剂和相应的最佳效果最后，在目标 3 中，我将量化 ADRD 的降低程度。实施目标 2 下生成的每条最佳治疗规则所产生的病例和将它们与目标 1 下观察到的 ADRD 案例减少进行比较。此 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作为痴呆症、认知衰老及其心理测量方法的实质性训练。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衰老研究方法的独立研究者。