Improving Causal Inference Tools for Addiction Researchers

改进成瘾研究人员的因果推理工具

• 批准号: 9594711
• 负责人: Beth Ann Griffin
• 金额: $ 81.32万
• 依托单位: RAND CORPORATION
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9594711
• 关键词: Caring; Cloud Computing; Computer software; Country; Data; Data Analytics; Data Set; Dose; Educational workshop; Ensure; Environment; Funding; Generations; Government Agencies; Grant; Health Services; Health Services Research; Individual; Institutes; Internet; Manuscripts; Mediation; Methodology; Methods; National Institute of Drug Abuse; Online Systems; Outcome; Pathway interactions; Peer Review; Population; Publications; Randomized; Request for Applications; Research; Research Personnel; Scoring Method; Series; Statistical Methods; Substance abuse problem; Techniques; Time; Training; Treatment Effectiveness; United States National Institutes of Health; United States Substance Abuse and Mental Health Services Administration; Weight; Work; addiction; analytical method; analytical tool; dosage; effective therapy; improved; innovation; meetings; next generation; outreach; randomized trial; response; software development; substance abuse treatment; tool; treatment effect; treatment program; webinar

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”.

项目摘要 找到真正有效的治疗成瘾和药物滥用的方法不仅需要知道治疗方法 对一个群体平均有效，但也知道治疗的机制或途径， 谁的治疗最有效，谁的治疗最有效。方法学家一直在开发新的统计方法， 技术依赖于更少的假设，因此，在提供准确答案方面比 从未有过然而，对于许多应用研究人员来说，理解如何实现这些功能可能很困难。 新的方法和几个这样的方法已经在易于使用的软件中实现。本申请 要求支持一项为期三年的R01研究，以改善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 通过加强现有的方法，创建软件来实现这些方法，并着手 开展战略性外联活动，以促进研究人员使用新方法和软件。过去 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， （1）创建和增强一个名为TWANG的统计软件包， 不等价组的加权和分析工具包，可在R、SAS和Stata中使用，（2） 贡献了30多篇方法论文章，致力于实施倾向评分方法，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群体的可比性，并估计物质的因果影响 使用观察数据的虐待治疗计划越来越有价值，因为大规模的资金 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 这将有助于改善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， 研究治疗如何以及为谁工作，研究治疗剂量的影响，并评估 他们的估计对不受控制的因素的敏感性。为了提高我们的工具的效用，我们将在 分析人员可以通过Web浏览器和IS菜单访问新计算环境 有动力。除了方法上的创新外，拟议项目还将包括若干外联工作， 例如短期课程、网络研讨会和同行评审手稿，以促进研究人员的最佳实践 使用新开发的工具。通过这些努力，该项目不仅旨在开发新方法， 工具和软件，而且还改善成瘾研究人员的统计实践，大大加强 科学信息，根据这些信息做出决策，以改善我国的护理，并直接满足 NIDA呼吁“支持新一代卫生服务研究的方法”。