Optimal Methods for Estimating Policy Effect Heterogeneity in Opioid Policy Research

方法研究项目摘要 该国的阿片类药物危机有增无减。致死过量的数量在2020年增加了近30%， 反映了芬太尼的持续传播以及压力增加、社会孤立、失业和 新冠肺炎大流行造成获得治疗的机会减少。各国继续颁布一系列不同的 与阿片类药物相关的政策，形成了复杂而充满活力的政策格局。阿片类药物政策的核心目标 研究是为了准确地确定将实现预期结果的政策，但州一级的评估 研究必须努力应对有限的样本容量所提供的有限的统计能力。此外， 人们对了解特定政策的预期收益在多大程度上 所有人口亚群都平等地经历，以及是否有国家的特征 具体政策或多或少是有效的。这项研究旨在为阿片类药物政策研究人员提供明确的 提供统计指导和新的统计方法，以进行复杂、高质量的政策评估。阿片类药物 政策的有效性可能在多个维度上有所不同，包括时间和国家、社区和个人- 水平因素。到目前为止，新的计量经济学方法关注的是时变效应。在这方面， 传统的差异差(DID)估计已被证明偏向于零。相比之下， 人们对检验各州不同政策有效性的方法一直关注有限-- 社区或个人层面的因素，但这些类型的政策影响异质性，并具有重要的 对实现公平的阿片类药物相关结果的影响。要获得可靠的效果估计，需要 轻松扩展到多级别设置并跨观测和级别借用信息的方法。在这 项目中，我们将全面总结统计科学在阿片类药物政策方面的状况 关于政策效果异质性估计的空间。我们还将开发新的统计方法来 解决检查政策效果异质性所固有的复杂性和样本量限制。最后， 我们将创建一系列模拟工具，为阿片类药物政策研究人员提供信息并改进方法 和策略研究人员更广泛地利用来确定哪些策略对 阿片类药物危机。这项工作将通过转移和改进 阿片类药物政策研究人员的统计做法，从而提高阿片类药物政策研究和 因此，关于哪些政策最有效以及对谁最有效的证据质量。我们的总体目标是 让应用政策研究人员深入了解替代方案的相对优势和局限性 评估不同政策影响的方法，并开发新的统计方法和可获得性 进行阿片类药物政策评价的统计指南，从而为外地提供更好的工具 实施这样的研究。

Methods Research Project Abstract The nation’s opioid crisis continues unabated. The number of fatal overdoses increased nearly 30% in 2020, reflecting the continued spread of fentanyl as well as the increased stress, social isolation, job loss, and reduced access to treatment imposed by the COVID-19 pandemic. States continue to enact a diverse array of opioid-related policies, producing a complex and dynamic policy landscape. A core objective in opioid policy research is to accurately identify policies that will achieve the intended outcomes, but state-level evaluation studies must grapple with the limited statistical power afforded by the constrained sample size. Furthermore, there is heightened interest in understanding the extent to which the intended gains from specific policies are experienced equitably by all population subgroups and whether there are characteristics of states for which specific policies are more or less effective. This study seeks to provide opioid policy researchers with clear statistical guidance and novel statistical methods to conduct complex, high-quality policy evaluations. Opioid policy effectiveness may vary along multiple dimensions including time and state-, community-, and individual- level factors. To date, new econometric methods have focused on time-varying effects. In this context, traditional difference-in-differences (DID) estimates have been shown to be biased towards zero. In contrast, there has been limited attention to methods for examining differential policy effectiveness across state-, community-, or individual-level factors, yet these types of policy effect heterogeneity and have important ramifications for achieving equitable opioid-related outcomes. Obtaining robust effect estimates will require methods that extend easily to multilevel settings and borrow information across observations and levels. In this project, we will provide a comprehensive summary of the state of the statistical science in the opioid policy space with regard to estimation of policy effect heterogeneity. We will also develop novel statistical methods to address the complexities and sample size constraints inherent in examining policy effect heterogeneity. Finally, we will create a series of simulation tools that will inform and improve the methods opioid policy researchers and policy researchers more broadly utilize to determine which policies are most effective for whom within the opioid crises. The work will make both short- and long-term contributions by shifting and improving the statistical practice of opioid policy researchers, thereby improving the accuracy of opioid policy studies and thus, the quality of evidence regarding which policies are most effective and for whom. Our overall goals are to give applied policy researchers insights regarding the relative advantages and limitations of alternative methods for estimating heterogeneous policy effects and to develop novel statistical methods and accessible statistical guidance for conducting opioid policy evaluations, thereby providing the field with better tools for implementing such research.