Innovative Statistical Methods for Detecting and Accounting for Non-Compliance in Randomized Trials of Very Low Nicotine Content Cigarettes

用于检测和解释极低尼古丁含量香烟随机试验中不合规情况的创新统计方法

• 批准号: 9248317
• 负责人: Joseph S. Koopmeiners
• 金额: $ 11.46万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9248317
• 关键词: Accounting; Address; Algorithms; Bayesian Modeling; Behavior; Biological Markers; Cigarette; Clinical Trials; Data Set; Development; Effectiveness; Environment; Exposure to; Family Smoking Prevention and Tobacco Control Act; Funding; Future; Goals; Intervention; Laws; Legal; Literature; Measures; Methodology; Methods; Nicotine; Outcome; Patient Self-Report; Patients; Population; Probability; Public Health; Randomized; Randomized Clinical Trials; Regulation; Reporting; Research; Research Personnel; Smoke; Smoker; Smoking; Statistical Methods; Tobacco; Tobacco use; United States Food and Drug Administration; United States National Institutes of Health; authority; direct application; experience; food environment; improved; innovation; interest; intervention effect; method development; non-compliance; public health relevance; randomized trial; response; tobacco regulatory science; treatment effect

 DESCRIPTION (provided by applicant): The 2009 Family Smoking Prevention and Tobacco Control Act (FSPTCA) gives the Food and Drug Administration (FDA) the authority to limit, but not eliminate, the nicotine content of cigarettes, if such action is likely to improve public healt. In response, the FDA and National Institutes of Health (NIH) have funded several randomized trials to evaluate the impact of Very Low Nicotine Content (VLNC) cigarettes on tobacco product use behavior. The presence of non-compliance to randomized treatment assignment (i.e., smoking commercially available non-study product) precludes generalizing the change experienced by subjects in these trials to the change in tobacco use in the entire population if the nicotine content of cigarettes was limited by regulation and normal nicotine content cigarettes were no longer legally available. In recent randomized trials of VLNC cigarettes, approximately 75% of subjects reported non-compliance to their randomized treatment assignment. These non-compliant subjects are problematic because they did not receive the full intervention (i.e., nicotine reduction) and their measures of product use behavior are likely to be different than if they had only smoked the VLNC cigarettes they were randomly assigned. A number of approaches to estimating the causal effect of VLNC cigarettes, i.e., the effect if no subjects were noncompliant, from randomized clinical trials have been proposed in the statistical literature. However, all rely on the assumption that the compliance status can be measured with certainty. In randomized trials of VLNC cigarettes, self-reported compliance status is not accurate so compliance must be estimated using biomarkers of nicotine exposure. We propose to develop statistical methods for identifying and accounting for non-compliance in randomized trials of VLNC cigarettes. In Aim 1, we will develop statistical methods for estimating the probability that a subject was compliant given their levels of biomarkers of nicotine exposure. This will allow us to properly account for the misclassification due to using biomarkers of nicotine exposure to detect non-compliance. In Aim 2, we will develop a statistical framework for estimating the causal effect of treatment when noncompliance is imprecisely measured. The development of these methods will result in consistent estimators of the causal effects of VLNC cigarettes, while accounting for the error associated with using biomarkers to identify non-compliance. Our application is directly relevant to the goals of the FDA Center for Tobacco Products (CTP). The estimation of the causal effect of nicotine reduction on tobacco product use behavior would represent a significant contribution to tobacco regulatory science. We will accomplish this goal through the development of innovative statistical methods that will allow us to identify non-compliance using biomarkers of nicotine exposure and estimate the causal effects that are most relevant for informing future FDA regulations

 描述（由适用提供）：2009年的《预防家庭吸烟和烟草控制法》（FSPTCA）赋予食品药品监督管理局（FDA），如果这种行动可能改善公共卫生，则限制但不消除香烟的尼古丁含量。作为回应，FDA和国立卫生研究院（NIH）资助了几项随机试验，以评估尼古丁含量（VLNC）卷烟对烟草产品使用行为的影响。如果在这些试验中，如果不再因法规和正常的尼古丁含量限制了烟气含量的限制，则不再存在于随机治疗分配不合规（即吸烟的非研究产品）规定整个人群中烟草使用变化的变化经验的规定。在最近对VLNC香烟的随机试验中，大约75％的受试者报告了其随机治疗分配不合规。这些不合格的受试者是有问题的，因为他们没有得到全部干预（即减少尼古丁），并且他们的产品使用行为的度量可能是 不同于他们只抽烟的VLNC香烟，它们是随机分配的。在统计文献中提出了许多估计VLNC香烟灾难性作用的方法，即，如果没有受试者不合规，则效果是从随机临床试验中提出的。但是，所有这些都依赖于可以确定性衡量合规性状态的假设。在VLNC香烟的随机试验中，自我报告的合规性状态不准确，因此必须使用尼古丁暴露的生物标志物估算依从性。我们建议开发统计方法，以在VLNC香烟的随机试验中识别和计算不合规的统计方法。在AIM 1中，我们将开发统计方法，以估计受试者符合尼古丁暴露的生物标志物的概率。这将使我们能够正确地说明由于使用尼古丁暴露的生物标志物来检测不合规的错误分类。在AIM 2中，我们将开发一个统计框架，用于估计不符合不合格的治疗的因果效应。这些方法的开发将导致对VLNC文明因果效应的一致估计，同时考虑到与使用生物标志物识别不合规的错误相关的错误。我们的应用与FDA烟草产品中心（CTP）的目标直接相关。尼古丁减少对烟草产品使用行为的因果关系的估计将代表对烟草监管科学的重要贡献。我们将通过开发创新的统计方法来实现这一目标

项目成果

Covariate selection with group lasso and doubly robust estimation of causal effects.

• DOI: 10.1111/biom.12736
• 发表时间: 2018-03
• 期刊: Biometrics
• 影响因子: 1.9
• 作者: Koch B;Vock DM;Wolfson J
• 通讯作者: Wolfson J