Improving Causal Inference Tools for Addiction Researchers

项目摘要 为成瘾和药物滥用找到真正有效的治疗方法不仅需要知道一种治疗方法 对人群平均有效，但也知道治疗的机制或途径 对谁的治疗最有效。方法学家们一直在开发新的统计学方法 依赖较少假设的技术，因此在提供准确答案方面比 史无前例。然而，对于许多应用研究人员来说，很难理解如何实现这些 新的方法和很少有这样的方法在易于使用的软件中得到实施。此应用程序 请求支持一项为期三年的R01研究，以改进稳健因果推理方法的使用 了解成瘾领域治疗效果的机制和调节因素。我们建议 要做到这一点，可以增强现有的方法，创建实现这些方法的软件，并着手 开展战略性外展活动，促进研究人员使用新方法和软件。在过去的时间里 十年来，我们的团队成功地鼓励更广泛地使用倾向评分方法来进行因果关系 通过(1)创建和增强名为twang or的统计软件包来研究成瘾问题 用于加权和分析不同组的工具包，可在R、SAS和STATA中获得，(2) 投稿了30多篇致力于实施倾向评分方法的方法论文章，以及(3) 举办20多个短期课程和讲习班。分析工具，如twang，使研究人员能够 评估接受不同治疗的组的可比性，并评估物质的因果影响 使用观察数据的虐待治疗计划作为大规模资金越来越有价值 随机研究正在减少。我们的团队计划从事一系列的方法和软件 有助于改进下一代成瘾健康服务研究的发展。我们的目标是 创建20个额外的软件命令和教程，为研究人员提供工具和培训 研究治疗如何和对谁起作用，研究治疗剂量的影响，并评估 他们的估计对不可控因素的敏感性。为了提高我们工具的实用性，我们将在 R和Stata以及在分析员通过网络浏览器和菜单访问新计算环境中 干劲十足。除方法创新外，拟议项目还将包括几项外联工作， 例如短期课程、网络研讨会和同行评议手稿，以促进研究人员的最佳做法 应用新开发的工具。通过这些努力，这个项目的目标不仅是开发新的方法， 工具和软件，还可以改进成瘾研究人员的统计做法，大大加强 根据科学信息作出决定，以改善我国的保健和直接会议 奈达呼吁“支持新一代卫生服务研究的方法”。

Project Summary Finding truly effective treatment for addiction and substance abuse requires not only knowing that a treatment works on average for a population, but also knowing the mechanisms or pathways through which a treatment works and for whom treatment is most effective. Methodologists have been developing new statistical techniques that rely on fewer assumptions and, as such, are more robust at providing accurate answers than ever before. However, it can be difficult for many applied researchers to understand how to implement these new methodologies and few such methods have been implemented in easy-to-use software. This application requests support for a three-year R01 study to improve the use of robust causal inference methods for understanding mechanisms and moderators of treatment effectiveness in the field of addiction. We propose to do so by enhancing existing methods, creating software to implement those methods, and embarking upon a strategic outreach campaign to facilitate researchers’ use of the new methods and software. Over the past decade, our team has successfully worked to encourage broader use of propensity score methods for causal research in the field of addiction by (1) creating and enhancing a statistical software package called TWANG or Toolkit for Weighting and Analysis of Nonequivalent Groups, which is available in R, SAS, and Stata, (2) contributing more than 30 methodological articles devoted to implementing propensity score methods, and (3) conducting over 20 short courses and workshops. Analytic tools such as TWANG which allow researchers to assess the comparability of groups receiving different treatments and estimate the causal impact of substance abuse treatment programs using observational data are increasingly valuable as funding for large scale randomized studies is dwindling. Our team plans to engage in a series of methodological and software developments that will help improve the next generation of addiction health services research. We aim to create 20 additional software commands and tutorials that will provide researchers with tools and training for studying how and for whom treatments work, studying the impact of treatment dose, and assessing the sensitivity of their estimates to uncontrolled factors. To enhance the utility of our tools, we will develop them in the R and Stata and in a new computing environment which analyst access through a web browser and is menu driven. In addition to methodological innovations, the proposed project will include several outreach efforts, such as short courses, webinars, and peer-review manuscripts to promote best practices among researchers applying the newly developed tools. Through these efforts, this project aims to not only develop new methods, tools and software, but also to improve the statistical practices of addiction researchers, greatly strengthening the scientific information upon which decisions are made to improve care in our country and directly meeting NIDA’s call for “methods that will support a new generation of health services research”.