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.

概括 FDA 烟草产品中心 (FDACTP) 已确定以下因素的影响分析： FDA 可能采取的监管行动是一个具有科学意义的领域。 （随机对照试验）是了解干预措施影响的黄金标准，但标准 分析 RCT 的方法有许多缺点，限制了我们对 首先，受试者可能不遵守随机治疗。 其次，一项旨在评估产品标准效果的随机对照试验。 总体人群可能没有足够的能力来估计治疗效果 最后，参与 RCT 的人群的特征可能并不重要。 必须解决这些限制才能获得完整的目标人群。 了解潜在产品标准对公众健康的影响。 该应用程序的目标是开发新颖的统计方法来解决 在目标 1 中，我们将开发统计方法来估计因果效应。 （即，如果法律强制要求合规的话）来自多个 RCT 的估计。 因果效应对于理解干预措施作为监管政策的影响至关重要 以有原则的方式结合多次试验的数据将导致更有效的结果 在目标 2 中，我们将开发一个稳健的因果效应估计器。 估计弱势群体因果影响的方法 一些弱势群体。 人口承受着不成比例的烟草负担，本研究中开发的方法 目标将导致对产品标准对这些亚人群的影响进行精确估计， 在目标 3 中，我们将提供一种新的方法来阐明群体异质性。 开发方法来校准对相关目标人群的因果影响的估计。 RCT 的入组人群可能不能代表目标人群，并且 为此目的开发的方法将允许随机对照试验的结果外推到目标 存在治疗效果异质性的人群。 该应用程序解决了 FDACTP 的科学兴趣“影响分析 – 了解 FDA 潜在监管行动的影响”。 通过开发对烟草监管科学领域做出的重大贡献 创新的统计方法将导致更准确地估计影响 FDA 潜在的监管行动（包括对弱势群体的影响）主要是 结合 FDACTP 资助的许多试验或产品标准的数据。