Utilizing tobacco discrete choice experiments to predict the population impacts of FDA regulatory policies

项目总结 作为评估消费者对新产品偏好的最先进方法，离散选择实验 (DCES)越来越多地被用于研究潜在政策对烟草使用行为的影响， 特别是在产品标准和报文传送开发领域(例如，警告和宣传活动)。 与观测调查数据相比，DCES的优势是提供了关于新兴事物的灵活证据 低流行率的产品，并在它们之前预测潜在政策对行为的影响 在现实世界中实施。然而，它们在指导和捍卫FDA政策方面的潜力还没有完全发挥出来 由于几个方法上的挑战而实现。第一个挑战是烟草DCE缺乏一致性。 设计和报告。研究人员通常采用不同的设计特征(例如，是否包括选择退出 选项)，并在报告中使用不同的衡量标准，例如支付意愿(WTP)与相对重要性分数。 因此，重要的是回顾当前的实践，探索如何综合现有的DCE证据，以及 为未来的DCE设计和报告提出指导方针。第二个挑战是假设性偏见--偏见 在声明的偏好(SP)或“人们声称他们会做的事”和透露的偏好(RP)之间 “人们在现实世界中实际做的是什么。”最近的方法开发结合了DCE和RP数据(例如， 零售和调查数据)，以减轻假设偏差，为我们提供了重新审视现有DCE的独特机会 并提高其在政策影响预测中的准确性。这个项目的首要目标是让 使用现有的DCE证据分析FDA监管政策对烟草使用行为的影响。我们将进行 系统评审以综合有关产品标准和报文传送开发的证据 政策重要性和WTP的排名。此外，我们将把现有的DCES与尼尔森零售扫描仪相结合 数据和具有全国代表性的调查，以减轻DCES中的假设偏差，这改善了 使用现有数据准确预测尚未实施的潜在监管政策的影响 还没有实施。具体目标是1)对烟草DCES操纵行为进行系统审查 产品属性。2)对操纵产品信息的烟草DCES进行系统审查。3) 结合声明偏好DCES和揭示偏好现实世界零售和调查数据来预测 FDA监管政策对产品市场份额和烟草使用的影响。该项目将提供第一个 在告知FDA监管政策的背景下，对烟草DCES进行系统审查，进一步改进 DCE在预测政策影响方面的准确性。总而言之，该项目使用现有数据来解决优先级问题 RFA-OD-21-003的科学领域，包括烟草产品特性、通信实践和 烟草使用模式。

PROJECT SUMMARY As the state-of-the-art method to assess consumers’ preferences for new products, discrete choice experiments (DCEs) have been increasingly applied to study the impacts of potential policies on tobacco use behaviors, particularly in the areas of product standards and messaging development (e.g., warnings and campaigns). Compared to observational survey data, DCEs have the advantage of providing nimble evidence on emerging products with low prevalence and predicting the impacts of potential policies on behaviors before they are implemented in the real world. However, their potential in guiding and defending FDA policies has not been fully realized due to several methodological challenges. The first challenge is the lack of consistency in tobacco DCE design and reporting. Researchers often adopt different design features (e.g., whether to include an opt-out option) and use different measures in reporting, such as willingness to pay (WTP) vs. relative importance scores. Therefore, it is important to review current practices, explore how to synthesize existing DCE evidence, and propose guidelines for future DCE design and reporting. The second challenge is hypothetical bias – the bias between stated preferences (SP) or “what people claim that they would do” and revealed preferences (RP) or “what people actually do in the real world.” Recent method development has combined DCEs and RP data (e.g., retail and survey data) to mitigate hypothetical bias, providing us a unique opportunity to revisit existing DCEs and improve their accuracy in policy impact prediction. The overarching goal of this project is to inform the impact of FDA regulatory policies on tobacco use behaviors using existing DCE evidence. We will conduct systematic reviews to synthesize evidence on product standards and messaging development by assessing the ranking of policy importance and WTP. Moreover, we will combine existing DCEs with Nielsen Retail Scanner data and nationally representative surveys to mitigate the hypothetical bias in DCEs, which improves the accuracy of using existing data to predict the impact of potential regulatory policies that have not been implemented yet. The specific aims are to 1) Conduct a systematic review of tobacco DCEs manipulating product attributes. 2) Conduct a systematic review of tobacco DCEs manipulating product messaging. 3) Combine stated-preference DCEs and revealed-preference real-world retail and survey data to predict the impacts of FDA regulatory policies on product market shares and tobacco use. This project will provide the first systematic review of tobacco DCEs in the context of informing FDA regulatory policies, further improving the accuracy of DCE in predicting policy impacts. In summary, this project uses existing data to address priority scientific domains of RFA-OD-21-003, including tobacco product characteristics, communication practices, and tobacco use patterns.