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)限制但不能消除香烟中的尼古丁含量，如果这样做可能会改善公共健康。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)香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的限制，并且正常的尼古丁含量不再合法获得，不遵守随机治疗分配(即吸食商业上可获得的非研究产品)的存在，就无法将受试者在这些试验中经历的变化概括为整个人口中烟草使用的变化。在最近的VLNC香烟随机试验中，大约75%的受试者报告没有遵守他们的随机治疗分配。这些不合规的受试者是有问题的，因为他们没有接受全面的干预(即减少尼古丁)，而且他们对产品使用行为的衡量很可能是 不同于如果他们只吸VLNC香烟，他们被随机分配。在统计文献中，已经提出了一些从随机临床试验中估计VLNC香烟的因果效应的方法，即如果没有受试者不服从的影响。然而，所有这些都依赖于这样一个假设，即合规状况可以肯定地进行衡量。在VLNC香烟的随机试验中，自我报告的遵从性状态并不准确，因此必须使用尼古丁暴露的生物标记物来评估遵从性。我们建议开发统计方法来识别和解释VLNC香烟随机试验中的违规行为。在目标1中，我们将开发统计方法来估计受试者在给定尼古丁暴露生物标记物水平的情况下符合要求的概率。这将使我们能够正确地解释由于使用尼古丁暴露的生物标记物来检测不遵守规定而造成的错误分类。在目标2中，我们将开发一个统计框架，用于在不精确测量不依从性的情况下估计治疗的因果效应。这些方法的发展将导致对VLNC香烟因果影响的一致估计，同时考虑到使用生物标记物来识别不遵守规定的相关误差。我们的申请与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记物来识别违规行为，并估计与通知未来FDA法规最相关的因果影响

 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