Developing Methodological Tools to Strengthen Concurrent State Opioid Policy Evaluation

项目概要 阿片类药物危机导致阿片类药物过量死亡人数稳步增加，目前死亡人数 每天近100个。各国制定了不同的政策来遏制危机。随着各州继续 解决如何分配资源以减少危机的问题，高质量的评估研究对于 确定最有效的政策。本研究旨在为阿片类药物研究人员提供清晰的统计数据 指导和新颖的统计方法来进行复杂、高质量的政策评估。 该项目将解决当前评估中面临的几个关键挑战。其一是问题 如何最好地选择适当的对照组——这对于有效估计政策的影响至关重要，因为 执行特定政策的国家可能与不执行特定政策的国家有根本不同。第二把钥匙 挑战在于政策环境的动态性，因为各国会针对不同的领域制定和修改各种政策。 以滚动的方式应对不断演变的危机。虽然各州经常实施一系列同时进行的改革，但这些改革 政策常常是单独评估的。然而，未能考虑到另一项政策的同时发生 目标相同的结果可能会产生对给定政策的有偏差的估计。此外，潜在的添加剂或 同时发生的政策的协同效应只能通过检查两项政策的相互作用来确定 这反过来又需要足够的样本量来估计所需的效果。识别 最佳统计方法至关重要，因为次优统计方法（例如次优回归） 规范、缺乏测试假设、对混杂因素的最小调整、短评估窗口）可能 产生不准确的推论，歪曲了真实政策效果的大小甚至方向。 在这个项目中，我们将全面总结阿片类药物的统计科学状况 政策空间。我们还将创建一系列模拟工具，以告知和改进阿片类药物的方法 政策研究人员和政策研究人员更广泛地利用来确定哪些政策最有效 帮助决策者应对阿片类药物（以及未来的危机）。这些工具将为 后续研究，其中可以利用模拟结果和基础设施来更好地评估 哪些类型的政策或政策组合在减少阿片类药物相关危害方面最有效。最后，我们 将开发新的统计方法来解决识别鲁棒控制状态的复杂性和 评估同时发生的政策。该项目的方法开发工作将提供阿片类药物（以及更多 广泛地说，成瘾）研究人员拥有更合适的方法来获得政策的公正估计 纵向观察数据的有效性。

Project Summary The opioid crisis has led to a steady increase in the number of opioid overdose deaths, which currently number nearly 100 a day. States have enacted heterogeneous sets of policies to curtail the crisis. As states continue to grapple with how to allocate resources to reduce the crisis, high-quality evaluation studies are crucial to identifying the most effective policies. This study seeks to provide opioid researchers with clear statistical guidance and novel statistical methods to conduct complex, high-quality policy evaluations. There are several key challenges faced in current evaluations that this project will address. One is the issue of how best to select an appropriate control group – this is crucial for valid estimates of the impact of policies, as states implementing a given policy may be fundamentally different than states that do not. A second key challenge is the dynamic nature of the policy landscape, as states enact and modify various policies on a rolling basis to address the evolving crisis. While states often enact a co-occurring set of reforms, these policies are often evaluated individually. Yet, failure to account for the co-occurrence of another policy that targets the same outcome may yield a biased estimate of a given policy. Furthermore, potential additive or synergistic effects of co-occurring policies can only be identified by examining the interaction of the two policies jointly which in turn will require sufficient sample sizes to allow for estimation of the needed effects. Identifying optimal statistical methods is critical as suboptimal statistical methods (e.g., suboptimal regression specification, lack of testing assumptions, minimal adjustment for confounding, short evaluation windows) may produce inaccurate inferences that misrepresent the magnitude or even direction of true policy effects. In this project, we will provide a comprehensive summary of the state of the statistical science in the opioid policy space. We will also 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 at helping decision makers deal with the opioid (and future crises). These tools will provide the groundwork for subsequent research, in which the simulation results and infrastructure can be leveraged to better assess which types of policies or policy combinations are most effective in reducing opioid-related harms. Finally, we will develop novel statistical methods to address the complexities of identifying robust control states and evaluating co-occurring policies. This project’s methods development work will provide opioid (and more broadly, addiction) researchers with more appropriate methods for obtaining unbiased estimates of policy effectiveness from longitudinal observational data.