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: 9127535
• 负责人: Joseph S. Koopmeiners
• 金额: $ 11.41万
• 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 2018-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9127535
• 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; Health; Intervention; Laws; Literature; Measures; Methodology; Methods; Nicotine; Outcome; Patient Self-Report; Patients; Population; Probability; Public Health; Randomized; Randomized Clinical Trials; Regulation; Reporting; Research; Research Personnel; Science; Smoke; Smoker; Smoking; Statistical Methods; Tobacco; Tobacco use; United States Food and Drug Administration; United States National Institutes of Health; Weight; authority; direct application; experience; improved; innovation; interest; intervention effect; method development; non-compliance; randomized trial; response; 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（由申请人提供）：2009 年《家庭吸烟预防和烟草控制法案》(FSPTCA) 赋予美国食品和药物管理局 (FDA) 限制（但不消除）香烟尼古丁含量的权力（如果此类行动可能会改善）作为回应，FDA 和美国国立卫生研究院 (NIH) 资助了多项随机试验，以评估极低尼古丁含量 (VLNC) 卷烟对烟草产品使用行为的影响。如果香烟的尼古丁含量受到法规限制，不遵守随机治疗分配（即吸烟市售非研究产品）的情况就无法将这些试验中受试者经历的变化推广到整个人群烟草使用的变化在最近的 VLNC 香烟随机试验中，大约 75% 的受试者报告不遵守随机治疗分配。存在问题，因为他们没有接受充分的干预（即减少尼古丁），并且他们对产品使用行为的衡量可能会受到影响。 与只吸 VLNC 香烟的情况不同，统计文献中提出了多种估计 VLNC 香烟因果效应的方法，即没有受试者不依从的情况下的效应。然而，所有这些都依赖于可以确定地测量依从性状态的假设。在 VLNC 卷烟的随机试验中，自我报告的依从性状态并不准确，因此必须使用尼古丁暴露的生物标志物来估计依从性。建议开发统计方法来识别和解释 VLNC 卷烟随机试验中的不合规情况。在目标 1 中，我们将开发统计方法来估计受试者的尼古丁暴露生物标志物水平的合规概率。我们要正确解释由于使用尼古丁暴露生物标志物来检测不依从情况而导致的错误分类。在目标 2 中，我们将开发一个统计框架，用于估计不依从情况下治疗的因果影响。这些方法的开发将导致对 VLNC 卷烟因果效应的一致估计，同时考虑到使用生物标志物来识别不合规情况的相关误差。我们的应用与 FDA 烟草中心的目标直接相关。产品（CTP）。减少尼古丁对烟草产品使用行为的因果影响的估计将对烟草监管科学做出重大贡献，我们将通过开发创新的统计方法来实现这一目标，这将使我们能够识别。使用尼古丁暴露的生物标志物来评估不合规情况，并估计与告知未来 FDA 法规最相关的因果影响