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

 描述（由申请人提供）：2009年《家庭吸烟预防和烟草控制法》（FSPTCA）授权食品和药物管理局（FDA）限制但不消除香烟中的尼古丁含量，如果这样做可能改善公众健康。作为回应，FDA和美国国立卫生研究院（NIH）资助了几项随机试验，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。 不依从随机化治疗分配（即，吸烟市售非研究产品）排除了将这些试验中受试者经历的变化概括为整个人群中烟草使用的变化，如果香烟的尼古丁含量受到法规限制，并且正常尼古丁含量的香烟不再合法可用。在最近的VLNC香烟随机试验中，约75%的受试者报告不依从其随机治疗分配。这些不依从的受试者是有问题的，因为他们没有接受充分的干预（即，尼古丁减少）和他们的产品使用行为的措施很可能是 与他们只抽VLNC香烟的情况不同，他们被随机分配。估计VLNC香烟的因果效应的许多方法，即，统计学文献中提出了随机临床试验中无受试者不依从的影响。然而，所有这些都依赖于这样一个假设，即遵约状况可以确定地衡量。在VLNC香烟的随机试验中，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。 我们建议开发统计方法，用于识别和解释VLNC卷烟随机试验中的不依从性。在目标1中，我们将开发统计方法，用于估计受试者在尼古丁暴露生物标志物水平下依从的概率。这将使我们能够正确解释由于使用尼古丁暴露的生物标志物来检测不依从性而导致的错误分类。在目标2中，我们将开发一个统计框架，用于估计治疗的因果效应时，不遵守不精确测量。这些方法的发展将导致VLNC香烟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依从性相关的误差。 我们的申请与FDA烟草制品中心（CTP）的目标直接相关。尼古丁减少对烟草制品使用行为的因果效应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，这些方法将使我们能够使用尼古丁暴露的生物标志物来识别不合规行为，并估计与未来FDA法规最相关的因果效应

 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