Hierarchical Modeling of Alcohol Treatment Outcomes of Group Therapy

 描述(由申请人提供)：针对问题酒精使用的分组治疗旨在反映治疗环境的实际情况。一个这样的考虑是，参与者经常在开放或滚动的基础上加入治疗组，因为治疗组中有空间可用。测试这种干预措施的一个障碍是小组成员经验的相互关联性。开放入学小组(OEGs)中不同成员在不同时间进入和离开的过程导致了小组成员结果之间的复杂关联。未能解释这种相关性可能会导致对治疗效果的不正确统计测试，破坏我们对治疗有效性得出结论的能力。在我们的上一个项目中，我们通过创新地将OEG会议概念化为空间相关，并利用丰富的现有统计方法使用条件自回归(CAR)进行空间数据分析，解决了这种相关性。我们展示了CAR在分析积极治疗阶段或治疗后收集的数据方面的多功能性。尽管有这些进展，酒精治疗研究人员仍然需要新的统计方法来模拟OEG参与者结果之间的相关性。这一续签申请回应了NIAAA对“进一步开发、改进、验证和创造性地实施”酒精使用障碍治疗的新统计方法的必要性的认识(PA-13-160)。关于如何检查在基于群体的酒精治疗研究中普遍存在的多模式结果，需要进一步的指导。放松标准汽车方法的一些方面可能会产生更适合的车型。我们目前的方法允许人们评估被实验分配到基于OEG的干预的参与者的主要结果的因果治疗效果，而不是当参与者没有跨研究臂交互作用时的比较。然而，越来越多的酒精治疗研究侧重于测试非随机因素对结果的影响。当感兴趣的因素在治疗组内的个体之间不同，导致个体之间的干扰时，因果推断是具有挑战性的。具体目标：(1)开发创新的方法来分析团体治疗研究的多模式或半连续结果；(2)探索条件自回归的替代方案，以灵活地对会话与会话的相关性进行建模；以及(3)采用最先进的因果推理框架，适当地解释基于团体的酒精治疗中参与者之间的干扰。这些具体目标的实现将为酒精治疗研究界提供创新的统计方法，协同解决OEG参与者结果之间的建模相关性，同时提高现场测试感兴趣的治疗因素的有效性的能力。

 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.