Developing Methodological Tools to Strengthen Concurrent State Opioid Policy Evaluation

项目摘要 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 每天近100个。各国颁布了一系列不同的政策来遏制危机。随着各国继续 为了解决如何分配资源以减少危机，高质量的评估研究至关重要， 找出最有效的政策。这项研究旨在为阿片类药物研究人员提供明确的统计数据， 我们需要有新的指导和新的统计方法来进行复杂、高质量的政策评价。 本项目将解决当前评价工作面临的若干关键挑战。一个是关于 如何最好地选择一个适当的对照组-这对于有效估计政策的影响至关重要， 执行某一政策的国家可能与不执行该政策的国家有着根本的不同。第二密钥 挑战是政策环境的动态性，因为各国在一个国家的不同领域颁布和修改各种政策， 以应对不断演变的危机。虽然各国经常制定一套共同发生的改革， 政策往往是单独评估的。然而，未能解释另一项政策的同时发生， 目标相同的结果可能产生对给定策略的有偏估计。此外，潜在的添加剂或 只有通过研究两种政策的相互作用，才能确定共同发生的政策的协同效应 这反过来又需要足够的样本量，以允许估计所需的效果。识别 最佳统计方法作为次佳统计方法是关键的（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，歪曲真实政策效果的大小甚至方向。 在这个项目中，我们将提供阿片类药物统计科学状态的全面总结 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，以确定哪些政策是最有效的， 帮助决策者应对阿片类药物（以及未来的危机）。这些工具将为以下方面奠定基础： 随后的研究，其中模拟结果和基础设施可以利用，以更好地评估 哪些类型的政策或政策组合在减少类阿片相关危害方面最为有效。最后我们 将开发新的统计方法，以解决识别鲁棒控制状态的复杂性， 评估共同发生的政策。该项目的方法开发工作将提供阿片类药物（以及更多） 更广泛地说，成瘾）研究人员与更适当的方法，以获得无偏估计的政策 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.