Hierarchical Modeling of Alcohol Treatment Outcomes of Group Therapy

团体治疗的酒精治疗结果的分层建模

• 批准号: 8318746
• 负责人: SUSAN M. PADDOCK
• 金额: $ 26.78万
• 依托单位: RAND CORPORATION
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8318746
• 关键词: Accounting; Address; Admission activity; Aftercare; Alcohol abuse; Alcohol dependence; Alcohols; Behavior; Client; Climate; Communities; Complex; Data; Data Analyses; Disease; Effectiveness; Enrollment; Event; Failure; Geographic Locations; Goals; Group Therapy; Knowledge; Lead; Left; Measures; Methods; Modality; Modeling; National Institute of Drug Abuse; National Institute on Alcohol Abuse and Alcoholism; Outcome; Persons; Pharmaceutical Preparations; Policies; Public Health; Research; Research Design; Research Personnel; Research Project Grants; Sample Size; Sampling; Statistical Bias; Statistical Models; Substance Abuse Treatment Centers; Techniques; Testing; Time; Treatment outcome; alcohol abuse therapy; alcohol and other drug; base; clinical practice; comparative efficacy; data modeling; experience; improved; innovation; member; multilevel analysis; peer; public health relevance; tool; treatment effect

DESCRIPTION (provided by applicant): Group therapy is a central treatment modality for alcohol or drug (AOD) disorders. Clients are often admitted to therapy groups under a rolling admissions basis, so that new members enter the group while others leave. Failure to properly adjust for this correlation could lead to biased statistical tests of treatment effects, thus impeding efforts to make AOD group therapy more effective. AOD treatment innovations for rolling groups should be guided by improved knowledge of the complex dynamics of client interactions. Despite the ubiquity of rolling groups in standard clinical practice, there is very little guidance available to researchers regarding proper analysis of these data. The key innovation of this project is that we develop an explicit model for the contribution of session participation on outcomes. Our model exploits similarities between sessions, such as their proximity to each other in time and the degree of overlap in participating clients. We do so by using statistical techniques developed to model data that are related due to geographic locations of sampled units. We take advantage of the conceptual similarity between measuring distance between geographic locations and measuring the closeness of sessions. These spatial statistical techniques provide a rich & powerful set of tools to model correlations among client outcomes in rolling groups. Our hierarchical (multilevel) models capture session-level effects and allow them to be correlated. This project provides a unique opportunity to integrate AOD treatment research with state-of-the-art statistical modeling to develop appropriate analytic techniques for rolling therapy group data. Specific Aims are to: 1) develop a hierarchical modeling framework to estimate the impact of rolling group admissions on treatment outcomes that incorporates methods initially developed for spatial data analysis; 2) extend this modeling framework to test whether client outcomes vary with rolling therapy session features and to identify which session-level features lead to improved client outcomes; 3) develop and disseminate analytic tools for study design, sample size determination, and analysis for rolling group studies. PUBLIC HEALTH RELEVANCE: This proposed research project is relevant to public health because its ultimate goal is to improve group-based treatments of alcohol and other drug (AOD) disorders. Motivated by the ubiquity of group therapy for treating AOD disorders, this project develops a statistical approach that can be used to test the effectiveness of AOD group therapies in a variety of realistic settings.

描述（由申请人提供）：小组治疗是酒精或药物（AOD）疾病的中央治疗方式。在滚动招生的基础上，经常将客户录入治疗小组，因此新成员进入该小组而其他人离开。无法正确调整这种相关性可能会导致治疗效应的统计检验，从而阻碍了AOD组治疗更有效的努力。滚动组的AOD治疗创新应通过对客户相互作用的复杂动态的了解来指导。尽管滚动组在标准临床实践中的无处不在，但研究人员对这些数据的正确分析几乎没有指导。该项目的关键创新是，我们为会议参与结果的贡献开发了一个明确的模型。我们的模型利用了会议之间的相似性，例如它们彼此之间的近距离和参与客户的重叠程度。我们通过使用开发的统计技术来建模与采样单位的地理位置相关的数据进行建模。我们利用测量地理位置之间的距离与测量会话的亲密关系之间的概念相似性。这些空间统计技术为滚动组中客户结果之间的相关性建模提供了丰富而强大的工具。我们的分层（多级）模型捕获了会话级效果，并允许它们与之相关。该项目提供了一个独特的机会，将AOD治疗研究与最先进的统计建模相结合，以开发适当的滚动治疗组数据分析技术。具体目的是：1）开发一个层次建模框架，以估计滚动组入院对结合最初用于空间数据分析的方法的治疗结果的影响； 2）扩展此建模框架以测试客户结果是否随滚动疗法会话功能而变化，并确定哪些会话级特征会改善客户端的结果； 3）开发和传播用于研究设计，样本量确定和滚动小组研究的分析的分析工具。 公共卫生相关性：该拟议的研究项目与公共卫生有关，因为其最终目标是改善基于群体的酒精和其他药物（AOD）疾病的治疗方法。由于治疗AOD障碍的小组疗法无处不在，该项目开发了一种统计方法，可用于测试AOD组疗法在各种现实环境中的有效性。