Using Causal Machine Learning Methods to Inform Tobacco Regulatory Science

项目概要/摘要 关于电子烟（EC）使用是否以及如何影响随后的烟草使用的不一致结果 行为使循证烟草监管复杂化。在年轻人中，使用EC与更大的风险有关 过渡到可燃香烟（CC）吸烟，但估计的影响大小EC暴露不同 基本上是跨研究的。在目前吸烟CC的成年人中，EC显示出帮助戒烟CC的潜力 在一些研究中吸烟，但在其他研究中没有。这些不一致的发现可能部分是由于 观察性研究，使用小规模的横断面数据，以及协变量控制不足，此外， 尽管估计的EC暴露效应的大小存在相当大的异质性， 特性修改EC暴露效应在文献中很大程度上被忽略。了解EC如何 影响随后的CC吸烟，特别是在脆弱的亚组（例如，年龄、性别），以及 这种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。最后是 尚不清楚是否可以估计某一人群亚组或某一时间的EC暴露效应， 一般化到不同的子组或时间。可推广的EC暴露效应可以提供关键证据 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EC对美国青少年和成年人随后CC吸烟的影响的方法 人群和脆弱亚群，并探讨估计可推广的EC暴露的方法 方面的影响.将对烟草与健康纵向人群评估研究进行二次分析， 旨在实现以下具体目标。目标1：确定EC使用对人体的平均暴露影响 青少年和成年人随后的CC吸烟。目标2：确定不同环境中的EC暴露效应 弱势亚群体（年龄、性别、贫困、种族/族裔）。目标3：评估因果关系的绩效 机器学习方法，使用模拟和PATH研究数据来概括EC暴露效应。到 成功地实现了这些目标，并发展成为烟草监管独立方法学家 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）评估暴露影响的因果推理方法; 3）机器学习技能 用于高维数据分析。在获奖期间，我将在研究和培训方面获得支持 目标由我的机构和跨学科的指导团队，其中包括在TRS领域的专家， 因果推理、机器学习和健康差异。K 01研究和培训经验将导致 在R 01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，我会带来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 烟草研究人员手中的方法，并促进使用复杂的数据，以告知FDA的规定。

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.