Hierarchical Modeling of Alcohol Treatment Outcomes of Group Therapy

 描述（由申请人提供）：基于组的问题酒精使用治疗的目的是反映治疗环境的务实现实。一个这样的考虑是，参与者通常在开放或滚动的基础上被登记到治疗组中，因为治疗组中的空间变得可用。测试这种干预措施的一个障碍是小组成员的经历之间的相互关联。在开放式群体中，不同群体参与者在不同时间的持续进入和离开导致了群体成员结果之间的复杂相关性。如果不考虑这种相关性，可能会导致对治疗效果的不正确的统计检验，从而削弱我们得出治疗有效性结论的能力。在我们以前的项目中，我们通过创新地将OEG会议概念化为空间相关，并利用大量现有的统计方法，使用条件自回归（CAR）进行空间数据分析，来解决这种相关性。我们证明了CAR用于分析在积极治疗阶段或治疗后收集的数据的多功能性。尽管取得了这些进展，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。NIAAA认识到需要“进一步开发，完善，验证和创造性地实施”用于治疗酒精使用障碍的新统计方法（PA-13-160）。需要进一步指导如何检查多模式的结果，是普遍存在的基于组的酒精治疗研究。放松标准CAR方法的各个方面可能会产生更好的拟合模型。我们目前的方法允许人们估计一个因果治疗效果的主要结果的参与者谁是实验分配到一个基于OEG的干预与比较时，参与者不相互作用的研究臂。然而，越来越多的酒精治疗研究集中在测试非随机因素对结果的影响上。因果推理是具有挑战性的，当感兴趣的因素在治疗组内的个体之间变化时，导致个体之间的干扰。具体目标：（1）开发创新的方法来分析多模态或非连续性的结果，从团体治疗研究;（2）探索替代条件自回归灵活建模会话会话相关性;（3）采用最先进的因果推理框架，适当地解释参与者之间的干扰，以小组为基础的酒精治疗。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，同时提高该领域测试感兴趣的治疗因素有效性的能力。

 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.