Using Causal Machine Learning Methods to Inform Tobacco Regulatory Science

使用因果机器学习方法为烟草监管科学提供信息

基本信息

批准号：
10662955
负责人：
Shu Xu
金额：
$ 18.27万
依托单位：
NEW YORK UNIVERSITY
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：
2023-05-01 至 2028-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10662955
关键词：
Address Adult Age American Area Award Bayesian Network Behavior Behavioral Research Characteristics Cigarette Cigarette Smoker Complex Data Data Analyses Electronic cigarette Entropy Ethnic Origin Gender Goals Health Heterogeneity Institution Knowledge Learning Skill Literature Longitudinal Studies Machine Learning Measures Mentors Methods Modeling Observational Study Performance Pilot Projects Policy Making Population Population Analysis Population Assessment of Tobacco and Health Poverty Process Race Regression Analysis Regulation Research Research Design Research Personnel Research Priority Risk Science Scoring Method Selection Bias Smoke Smoker Subgroup Surgeon Time Tobacco Tobacco use Training Vulnerable Populations Youth cigarette smoking combustible cigarette complex data electronic cigarette use evidence base experience forest health disparity high dimensionality intersectionality longitudinal analysis machine learning method multidimensional data regression trees research study response secondary analysis theories tobacco prevention tobacco products tobacco regulation tobacco regulatory science

项目摘要

PROJCT SUMMARY/ABSTRACT Inconsistent findings regarding whether and how E-cigarette (EC) use influences subsequent tobacco use behaviors complicate evidence-based tobacco regulation. Among youth, EC use is associated with greater risk of transitioning to combustible cigarette (CC) smoking, but estimated effect sizes of EC exposure vary substantially across studies. Among adults who currently smoke CCs, ECs show potential to help quit CC smoking in some studies but not in others. These inconsistent findings may be due in part to a preponderance of observational studies, use of small size cross-sectional data, and inadequate control for covariates, Further, despite considerable heterogeneity in the size of estimated EC exposure effects, whether specific characteristics modify the EC exposure effects has been largely ignored in literature. Understanding how ECs influence subsequent CC smoking, particularly among vulnerable subgroups (e.g., age, gender), and their intersectionality, will help inform regulatory activities that address tobacco-related health disparities. Lastly, it is unclear whether estimated EC exposure effects from a certain population subgroup or at a certain time can be generalized to different subgroups or times. Generalizable EC exposure effects could provide critical evidence for tobacco regulators. To address these knowledge gaps, this study aims to use causal machine learning methods to determine the influence of ECs on subsequent CC smoking, in overall US youth and adult populations and in vulnerable subgroups, and to explore methods for estimating generalizable EC exposure effects. A secondary analysis of the longitudinal Population Assessment of Tobacco and Health study will be conducted to address the following specific aims. Aim 1: Determine average exposure effects of EC use on subsequent CC smoking in youth and adults. Aim 2: Determine heterogeneous EC exposure effects among vulnerable subgroups (age, gender, poverty, race/ethnicity). Aim 3: Evaluate the performance of causal machine learning methods to generalize EC exposure effects using both simulated and PATH Study data. To successfully accomplish these aims and develop into an independent methodologist in tobacco regulator science (TRS), I will obtain training in the following areas: 1) TRS theories and measures, especially health disparities in TRS; 2) Causal inference methods for evaluating exposure effects; and 3) Machine learning skills for high-dimensional data analysis. During the award period, I will be supported in my research and training goals by my institution and interdisciplinary mentoring team, which consists of experts in the fields of TRS, causal inference, machine learning, and health disparities. The K01 research and training experience will result in an R01 with the overarching goal of extending causal machine learning methods for generalization to address more complex real-world questions. In the long term, I will bring TRS, machine learning methods, and causal inference together to address pressing issues in TRS. This effort will put causal machine learning methods in the hands of tobacco researchers and facilitate the use of complex data to inform FDA regulations.

项目概要/摘要关于电子烟 (EC) 使用是否以及如何影响后续烟草使用的研究结果不一致行为使基于证据的烟草监管变得复杂。在青少年中，使用 EC 会带来更大的风险过渡到吸烟 (CC) 的影响，但 EC 暴露的估计影响大小各不相同基本上跨研究。在目前吸烟 CC 的成年人中，EC 显示出帮助戒烟的潜力在一些研究中吸烟，但在其他研究中则没有。这些不一致的发现可能部分是由于优势观察性研究、使用小规模横截面数据以及对协变量控制不充分，此外，尽管估计的 EC 暴露影响的大小存在相当大的异质性，无论是具体的特性修改 EC 暴露效应在文献中很大程度上被忽略了。了解 EC 如何影响随后的 CC 吸烟，特别是在弱势亚群体（例如年龄、性别）及其交叉性，将有助于为解决与烟草相关的健康差异的监管活动提供信息。最后，就是不清楚是否可以估计特定人群或特定时间的 EC 暴露影响推广到不同的子组或时间。普遍的 EC 暴露效应可以提供关键证据对于烟草监管机构。为了解决这些知识差距，本研究旨在使用因果机器学习确定 ECs 对美国青少年和成人随后吸烟的影响的方法人群和弱势亚群体，并探索估计普遍 EC 暴露的方法影响。烟草与健康纵向人口评估研究的二次分析将是为实现以下具体目标而进行。目标 1：确定 EC 使用的平均暴露影响青少年和成人随后吸烟。目标 2：确定 EC 暴露的异质效应弱势群体（年龄、性别、贫困、种族/民族）。目标 3：评估因果关系的表现机器学习方法使用模拟数据和 PATH 研究数据来概括 EC 暴露效应。到成功实现这些目标并发展成为烟草监管机构的独立方法学家科学（TRS），我将获得以下方面的培训：1）TRS理论和措施，特别是健康 TRS 的差异； 2）评估暴露效应的因果推断方法； 3）机器学习技能用于高维数据分析。奖励期间，我的研究和培训将得到支持我的机构和跨学科指导团队的目标，该团队由 TRS 领域的专家组成，因果推理、机器学习和健康差异。 K01研究和培训经验将产生在 R01 中，其总体目标是将因果机器学习方法扩展到泛化解决更复杂的现实问题。长远来看我会带来TRS，机器学习方法，和因果推理共同解决 TRS 中的紧迫问题。这项工作将使因果机器学习烟草研究人员手中的方法，并促进使用复杂的数据来告知 FDA 法规。