Innovative Statistical Methods for Evaluating the Impact of Tobacco Product Standards

评估烟草产品标准影响的创新统计方法

• 批准号: 9976479
• 负责人: Joseph S. Koopmeiners
• 金额: $ 38.31万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9976479
• 关键词: Address; Affect; Area; Bayesian Method; Bayesian Modeling; Behavior; Cigarette; Data; Data Sources; Development; Enrollment; Environment; Family Smoking Prevention and Tobacco Control Act; Funding; Future; Goals; Gold; Heterogeneity; Intention; Intervention; Legal; Mental disorders; Methodology; Methods; Modeling; Nicotine; Policies; Population; Population Characteristics; Population Heterogeneity; Probability; Public Health; Randomized; Randomized Controlled Trials; Regulation; Research; Scientist; Smoker; Source; Statistical Methods; Study Subject; Subgroup; Surveys; Target Populations; Tobacco; Tobacco use; Toxic effect; United States; United States Food and Drug Administration; Vulnerable Populations; authority; base; design; improved; innovation; interest; intervention effect; multiple data sources; novel; novel strategies; study characteristics; tobacco products; tobacco regulatory science; tool; treatment effect; treatment group

SUMMARY The FDA Center for Tobacco Products (FDACTP) has identified impact analyses of potential FDA regulatory action as an area of scientific interest. Randomized controlled trials (RCTs) are the gold-standard for understanding the impact of an intervention, but standard approaches to analyzing RCTs have a number of drawbacks which limit our understanding of the impact of a product standard. First, subjects may not adhere to their randomized treatment assignment. Second, an RCT that was designed to evaluate the effect of a product standard in the overall population may not be adequately powered to estimate the treatment effect within important sub-groups. Finally, the characteristics of the population enrolled in the RCT may not represent the target population. These limitations must be addressed to obtain a complete understanding of the impact of potential product standards on public health. The goal of this application is to develop novel statistical methodology that addresses these concerns. In Aim 1, we will develop statistical methods to estimate causal effects (i.e., the effect if compliance were legally mandated) from multiple RCTs. Estimating causal effects is central to understanding the impact of an intervention as a regulatory policy and combining data from multiple trials in a principled manner will result in more efficient estimators of causal effects without introducing bias. In Aim 2, we will develop a robust approach for estimating causal effects in vulnerable populations. A number of vulnerable populations are disproportionally burdened by tobacco, and the methodology developed in this aim will result in precise estimates of the impact of a product standard in these sub-populations, while providing a novel approach to elucidating population heterogeneity. In Aim 3, we will develop methods to calibrate estimation of causal effects to a relevant target population. The enrolled population of a RCT may not be representative of the target population and the methodology developed in this aim will allow results of RCTs to be extrapolated to a target population in the presence of treatment effect heterogeneity. This application addresses FDACTP scientific interest “Impact Analysis – Understanding the impact of potential FDA regulatory actions”. Our application represents a significant contribution to the field of tobacco regulatory science through the development of innovative statistical methods that will result in more precise estimates of the impact of potential FDA regulatory action (including impact in vulnerable populations) by principally combining data from the many trials or product standards funded by FDACTP.

摘要 FDA烟草产品中心(FDACTP)已经确定了对 FDA可能采取的监管行动作为一个具有科学意义的领域。随机对照试验 (随机对照试验)是了解干预影响的黄金标准，但也是标准 分析RCT的方法有许多缺点，这些缺点限制了我们对 产品标准的影响。首先，受试者可能不会坚持他们的随机治疗 任务。第二，旨在评估产品标准在以下方面效果的RCT 总体人口可能没有足够的能力来估计 重要的子组。最后，参加随机对照研究的人口的特征可能不会 代表目标人群。必须解决这些限制才能获得完整的 了解潜在的产品标准对公众健康的影响。 该应用程序目标是开发新的统计方法，以解决 这些担忧。在目标1中，我们将开发统计方法来估计因果效应 (即，在法律上强制遵守的效果)来自多个区域协调机制。估算 因果效应是理解干预作为一种监管政策的影响的核心 以有原则的方式组合来自多个试验的数据将产生更高的效率 在不引入偏差的情况下对因果效应的估计值。在目标2中，我们将开发一个健壮的 估计脆弱人群因果效应的方法。一些易受攻击的 人口不成比例地背负着烟草的负担，而在这方面开发的方法 AIM将导致对产品标准在这些子群体中的影响进行准确估计， 同时为阐明种群异质性提供了一种新的方法。在《目标3》中，我们将 开发方法来校正对相关目标人群的因果影响的估计。 随机对照试验的登记人口可能不能代表目标人口和 为实现这一目标而开发的方法将使随机对照试验的结果能够外推到一个目标 群体中存在处理效应的异质性。 该应用程序解决了FDACTP的科学兴趣“影响分析- 了解FDA可能采取的监管行动的影响“。我们的应用程序代表 通过发展对烟草监管科学领域的重大贡献 创新的统计方法，将导致更准确地估计 FDA可能采取的监管行动(包括对脆弱人群的影响)主要是 结合FDACTP资助的许多试验或产品标准的数据。