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: 9104577
• 负责人: SUSAN M. PADDOCK
• 金额: $ 25.87万
• 依托单位: RAND CORPORATION
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-15 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9104577
• 关键词: Accounting; Address; Aftercare; Alcohol abuse; Climate; Communities; Complex; Data; Data Analyses; Dependency; Development; Effectiveness; Enrollment; Face; Failure; Foundations; Goals; Group Therapy; Individual; Intervention; Lead; Length of Stay; Methodology; Methods; Modality; Modeling; Outcome; Participant; Pharmacotherapy; Phase; Public Health; Randomized; Research; Research Personnel; Research Project Grants; Statistical Methods; Statistical Models; Structure; Techniques; Testing; Thinking; Time; Treatment Effectiveness; Treatment Factor; Treatment outcome; Variant; Waiting Lists; active method; alcohol abuse therapy; alcohol and other drug; alcohol use disorder; arm; base; causal model; coping; design; experience; group intervention; improved; innovation; interest; member; novel; novel strategies; primary outcome; public health relevance; response; success; treatment effect

 DESCRIPTION (provided by applicant): Group-based treatments for problem alcohol use are designed to reflect the pragmatic realities of treatment settings. One such consideration is that participants are often enrolled into therapy groups on an open, or rolling, basis, as space becomes available in the therapy group. A barrier to testing such interventions has been the interrelatedness of group members' experiences. The ongoing entry and departure of different group participants at different times in open-enrollment groups (OEGs) induces complex correlations among group members' outcomes. Failure to account for this correlation could lead to incorrect statistical tests of treatment effects, undermining our ability to draw conclusions about the effectiveness of treatment. In our previous project, we addressed this correlation by innovatively conceptualizing OEG sessions as spatially related and drawing upon a wealth of existing statistical methodology for spatial data analysis using conditional autoregression (CAR). We demonstrated the versatility of CAR for analyzing data collected during the active treatment phase or post-treatment. Despite these advances, alcohol treatment researchers continue to need new statistical methods to model correlation among OEG participant outcomes. This renewal application responds to NIAAA's recognition of the need to "further develop, refine, validate, and creatively implement" novel statistical methods for the treatment of alcohol use disorders (PA-13-160). Further guidance is needed regarding how to examine multimodal outcomes that are ubiquitous in group-based alcohol treatment research. Relaxing aspects of the standard CAR approach may result in better-fitting models. Our current methods allow one to estimate a causal treatment effect of a primary outcome for participants who are experimentally assigned to an OEG-based intervention versus a comparison when participants do not interact across study arms. However, a growing body of alcohol treatment research focuses on testing the effects of non- randomized factors on outcomes. Causal inference is challenging when a factor of interest varies among individuals within a therapy group, resulting in interference among individuals. Specific Aims: (1) Develop innovative approaches to analyze multimodal or semicontinuous outcomes from group therapy studies; (2) explore alternatives to conditional autoregression for flexibly modeling session-to-session correlation; and (3) employ a state-of-the-art causal inference framework that appropriately accounts for the interference among participants in group-based alcohol treatment. Fulfillment of these Specific Aims will provide the alcohol treatment research community innovative statistical methods that synergistically address modeling correlation among OEG participant outcomes while advancing the ability of the field to test the effectiveness of treatment factors of interest.

 描述（由应用程序提供）：基于群体的饮酒问题的基于群体的治疗方法旨在反映治疗环境的实用现实。一种考虑因素是，随着治疗组的空间可用，参与者通常会以公开或滚动的基础入学。测试这种干预措施的障碍是小组成员经验的相互关系。在开放式申请小组（OEGS）不同时间，不同小组参与者的持续进入和离开会影响小组成员成果之间的复杂相关性。不考虑这种相关性可能会导致治疗效果的统计检验不正确，从而破坏了我们得出有关治疗有效性的结论的能力。在我们以前的项目中，我们通过将OEG会话概念化为空间相关，并利用有条件自动化（CAR）的空间数据分析来解决这种相关性。我们证明了在主动治疗阶段或处理后收集的分析数据的汽车多功能性。尽管有这些进展，但酒精治疗研究人员仍需要新的统计方法来模拟OEG参与者结果之间的相关性。这种更新的应用对NIAAA对“进一步开发，完善，验证和创造性地实施治疗酒精使用障碍的新型统计方法的认识”的响应（PA-13-160）。关于如何检查基于小组的酒精治疗研究中无处不在的多模式结局，还需要进一步的指导。标准汽车方法的放松方面可能会导致更合适的模型。我们当前的方法允许人们估计主要结果的因果治疗效应，这些参与者是通过实验分配给基于OEG的干预措施的参与者，而当参与者不在研究臂上相互作用时进行比较。但是，越来越多的酒精治疗研究重点是测试非随机因素对结果的影响。当一种兴趣因素在一个治疗组中的个体之间有所不同时，因果推断就会受到挑战，从而导致个体之间的干扰。具体目的：（1）开发创新的方法来分析小组治疗研究的多模式或半连续结果； （2）探索有条件自动化的替代方案，以进行灵活的建模会话与会议相关性； （3）雇员是一个最新的因果推理框架，适当地解释了参与者在基于小组酒精治疗中的干扰。实现这些特定目标将为酒精治疗研究社区创新的统计方法提供协同解决OEG参与者结果之间的建模相关性，同时促进该领域测试感兴趣治疗因素的有效性的能力。