Using Causal Machine Learning Methods to Inform Tobacco Regulatory Science

项目摘要/摘要 关于电子烟(EC)的使用是否以及如何影响随后的烟草使用的研究结果不一致 行为使循证烟草监管复杂化。在年轻人中，使用EC与更大的风险相关 向可燃香烟(CC)吸烟过渡的可能性，但EC暴露的估计影响大小各不相同 基本上是跨研究的。在目前吸食CCS的成年人中，ECS显示出帮助戒除CC的潜力 在一些研究中吸烟，但在另一些研究中不吸烟。这些不一致的发现可能在一定程度上是由于 观察性研究，使用小规模的横断面数据，以及对协变量的不充分控制，进一步， 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 特征修饰对EC暴露的影响在很大程度上被文献忽视。了解ECS如何 影响随后的CC吸烟，特别是在弱势群体(例如，年龄、性别)和他们 交叉性，将有助于为解决与烟草有关的健康差距的监管活动提供信息。最后，它是 不清楚特定人群或特定时间的估计EC暴露影响是否可以 泛化到不同的子群或时代。可概括的EC暴露效应可提供关键证据 对烟草监管机构来说。为了解决这些知识差距，本研究旨在使用因果机器学习 方法在总体美国青年和成人中，确定ECs对随后的CC吸烟的影响 在人群和易受伤害的亚群中，并探索估计可推广的EC暴露的方法 效果。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的。目标1：确定使用EC的平均暴露影响 随后在青年和成人中进行CC吸烟。目标2：确定不同来源的EC暴露的影响 弱势群体(年龄、性别、贫困、种族/族裔)。目标3：评估因果关系的表现 使用模拟和路径研究数据来概括EC暴露效应的机器学习方法。至 成功实现这些目标，并发展成为烟草监管领域的独立方法论专家 理科(TRS)，我将接受以下方面的培训：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理方法；3)机器学习技能 用于高维数据分析。在获奖期间，我的研究和培训将得到支持 我的机构和由TRS领域的专家组成的跨学科指导团队的目标， 因果推理、机器学习和健康差异。K01的研究和培训经验将产生 在R01中，首要目标是将因果机器学习方法扩展到 解决更复杂的现实问题。从长远来看，我将把TRS，机器学习方法， 和因果推理一起解决TRS中的紧迫问题。这一努力将把因果机器学习 方法掌握在烟草研究人员手中，并便于使用复杂的数据来告知FDA的法规。

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.