使用条件自回归先验分析滚动组治疗数据
Analysis of rolling group therapy data using conditionally autoregressive priors
摘要：
团体治疗是一种治疗行为健康障碍的中心治疗方式，如酒精和其他药物使用（AOD）和抑郁症。团体治疗通常是在滚动（或开放）招生政策下提供的，在空间允许的情况下，新客户不断加入一个团体。滚动招生政策导致客户结果之间的复杂相关结构。尽管滚动录取在实践中无处不在，但很少有关于分析此类数据的指导。我们讨论了以前提出的方法的局限性，在一项研究的背景下，提供团体认知行为治疗抑郁症的住宅药物滥用治疗的客户端。我们改进了以前的滚动组分析方法，通过使用分层贝叶斯模型对客户抑郁症状评分的相关性进行全面建模，该模型假设会话级随机效应的条件自回归先验。我们使用我们的方法来估计模型参数的方差，并增强了学习滚动治疗组参与者之间复杂相关结构的能力，从而提高了性能。我们的方法广泛适用于任何团体治疗设置，其中团体有不断变化的客户组成。它将导致更有效地分析客户端的数据，并提高团体治疗研究社区的能力，以了解滚动组的动态如何导致客户端的结果。
英文摘要：
Group therapy is a central treatment modality for behavioral health disorders such as alcohol and other drug use (AOD) and depression. Group therapy is often delivered under a rolling (or open) admissions policy, where new clients are continuously enrolled into a group as space permits. Rolling admissions policies result in a complex correlation structure among client outcomes. Despite the ubiquity of rolling admissions in practice, little guidance on the analysis of such data is available. We discuss the limitations of previously proposed approaches in the context of a study that delivered group cognitive behavioral therapy for depression to clients in residential substance abuse treatment. We improve upon previous rolling group analytic approaches by fully modeling the interrelatedness of client depressive symptom scores using a hierarchical Bayesian model that assumes a conditionally autoregressive prior for session-level random effects. We demonstrate improved performance using our method for estimating the variance of model parameters and the enhanced ability to learn about the complex correlation structure among participants in rolling therapy groups. Our approach broadly applies to any group therapy setting where groups have changing client composition. It will lead to more efficient analyses of client-level data and improve the group therapy research community's ability to understand how the dynamics of rolling groups lead to client outcomes.
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