Improving TWANG as a Research Tool for Addiction Researchers

描述（由申请人提供）：美国每年有超过200万人接受药物滥用治疗项目，有必要确保向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收集其客户在入院时、治疗期间和治疗后的数据。这些努力的结果是，研究人员现在可以获得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。这些数据可以用来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，需要适当的、通常是前沿的统计建模工具。我们兰德公司的团队开发了一个特别有前途的工具，用于估计治疗的因果效应，这是在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一揽子方案采用了一种复杂的加权技术，该技术基于个人接受治疗的概率，给定他或她的预处理变量（即倾向得分），以调整治疗方案之间对观察到的客户预处理特征的不平衡，从而使分析人员和研究人员能够对两种治疗方案对结果的相对有效性得出比传统方法更可靠的推断。TWANG是一种评估两种治疗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（两个以上）治疗和研究时变治疗序列的相对有效性。此外，一些研究人员和分析人员可能不熟悉当前可用的R环境，这对使用TWANG造成了障碍。该提案旨在扩展TWANG包，使其更多功能，更好地满足成瘾研究人员当前和未来的需求。它还旨在改进整套方案的传播。具体来说，本提案旨在(1)将TWANG扩展到估计倾向得分和评估多项和时变治疗的平衡，(2)开发软件，通过R以外的环境（例如，SAS和Stata）提供对TWANG的访问，以及(3)开发和实施传播策略，使更新的软件可用于成瘾研究社区，并促进其在该社区的吸收。从短期和长期来看，这笔赠款将用于改进作为卫生服务研究工具的TWANG一揽子计划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，这笔拨款不仅将改进一个有前途的新因果推理工具，而且更有效地将其直接交到成瘾研究人员手中。

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.