Improving TWANG as a Research Tool for Addiction Researchers

改进 TWANG 作为成瘾研究人员的研究工具

• 批准号: 8664827
• 负责人: Beth Ann Griffin
• 金额: $ 34.51万
• 依托单位: RAND CORPORATION
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-01 至 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8664827
• 关键词: Address; Admission activity; Adopted; Aftercare; Alcohol abuse; Behavioral Research; Characteristics; Client; Communities; Complex; Computer software; Data; Data Set; Decision Making; Diagnostic; Education; Educational workshop; Effectiveness; Ensure; Environment; Equilibrium; Funding; Future; Government; Grant; Hand; Health Personnel; Health Services; Health Services Research; Individual; Intake; Marketing; Methodology; Methods; Modeling; Outcome; Pharmaceutical Preparations; Probability; Process; Program Effectiveness; Provider; Public Health; Randomized; Randomized Clinical Trials; Relative (related person); Request for Applications; Research; Research Personnel; Scoring Method; Series; Services; Statistical Computing; Statistical Models; Techniques; Testing; Text; Time; Treatment Effectiveness; United States; United States Substance Abuse and Mental Health Services Administration; Update; Weight; addiction; base; cost; flexibility; improved; meetings; online tutorial; operation; outreach; programs; public health relevance; randomized trial; software development; substance abuse treatment; tool; treatment effect; treatment program; uptake; web site

DESCRIPTION (provided by applicant): There are over two million admissions to substance abuse treatment programs in the United States each year, and there is a need to ensure that the services offered to these clients are producing positive and meaningful impacts on their lives. To address this need, the government has begun to require that treatment providers receiving funding collect data on their clients at intake, during treatment, and after treatment. A a result of these efforts, researchers now has available an increasing amount of observational data that track individuals from multiple treatment programs and that contain a wealth of information regarding program effectiveness. These data can be used to test the relative effectiveness of many different treatment programs and treatment services, but to fully capitalize on these data and assess the true causal impact of these programs requires appropriate and often cutting-edge statistical modeling tools. One particularly promising tool for estimating causal effects of treatment, developed by our team at RAND, is the Toolkit for Weighting and Analysis of Non- Equivalent Groups (TWANG) package developed in the R statistical computing environment. The TWANG package utilizes a sophisticated weighting technique based on an individual's probability of receiving a treatment given his or her pretreatment variables (i.e., th propensity score) to adjust for imbalances between treatment programs on the observed pretreatment characteristics of their clients, thereby enabling analysts and researchers to draw more robust inferences about the relative effectiveness of two treatment programs on outcomes than traditional methods. TWANG is an exceptional tool for estimating the relative effectiveness of two treatments, but many pressing research questions involve more complex settings, such as comparing multinomial (more than two) treatments and studying the relative effectiveness of time-varying sequences of treatments. Moreover, the R environment in which TWANG is currently available may be unfamiliar to some researchers and analysts, creating a barrier to use of TWANG. This proposal aims to extend the TWANG package to be more versatile and better able to meet the current and future needs of addiction researchers. It also aims to improve dissemination of the package. Specifically, this proposal aims to (1) extend TWANG to estimate propensity scores and assess balance for multinomial and time-varying treatments, (2) develop software to provide access to TWANG via environments other than R (e.g., SAS and Stata), and (3) develop and implement a dissemination strategy to make the updated software available to the addiction research community and to promote its uptake in that community. The contributions from this grant in the short and long term will be to improve both the TWANG package as a health services research tool and the statistical practices of addiction health service researchers. This grant will encourage broader use of modern causal modeling methods through our dissemination efforts. Thus, this grant will not only improve a promising new causal inference tool but more effectively place it directly into the hands of addiction researchers.

描述（由申请人提供）：在美国，每年有超过200万人进入药物滥用治疗计划，需要确保为这些客户提供的服务对他们的生活产生积极和有意义的影响。为了满足这一需求，政府已开始要求接受资助的治疗提供者在接受治疗时、治疗期间和治疗后收集客户的数据。作为这些努力的结果，研究人员现在有越来越多的观察数据，这些数据可以跟踪来自多个治疗方案的个体，并包含有关方案有效性的丰富信息。这些数据可用于测试许多不同治疗方案和治疗服务的相对有效性，但要充分利用这些数据并评估这些方案的真正因果影响，需要适当且通常是尖端的统计建模工具。我们在兰德的团队开发的一个特别有前途的估计治疗因果效应的工具是在R统计计算环境中开发的非等效组加权和分析工具包（TWANG）。TWANG包利用复杂的加权技术，该技术基于个人在给定预处理变量的情况下接受治疗的可能性（即，倾向评分）来调整治疗方案之间的不平衡，从而使分析师和研究人员能够得出比传统方法更可靠的关于两种治疗方案对结果的相对有效性的推断。TWANG是一种用于估计两种治疗方法的相对有效性的特殊工具，但许多紧迫的研究问题涉及更复杂的设置，例如比较多项（两种以上）治疗方法和研究随时间变化的治疗序列的相对有效性。此外，TWANG目前可用的R环境可能对一些研究人员和分析师来说是陌生的，这对TWANG的使用造成了障碍。该提案旨在扩展TWANG包，使其更加通用，能够更好地满足成瘾研究人员当前和未来的需求。它还旨在改进一揽子计划的传播。具体而言，该提案旨在（1）扩展TWANG以估计倾向评分并评估多项式和时变治疗的平衡，（2）开发软件以通过R以外的环境（例如，SAS和Stata），以及（3）制定和实施一项传播战略，使成瘾研究界可以使用更新后的软件，并促进该社区的吸收。这项赠款在短期和长期内的贡献将是改善作为卫生服务研究工具的TWANG包和成瘾卫生服务研究人员的统计实践。这笔赠款将通过我们的传播工作鼓励更广泛地使用现代因果建模方法。因此，这笔赠款不仅将改善一个有前途的新的因果推理工具，而且更有效地将其直接交给成瘾研究人员。