Paddock Susan M, Savitsky Terrance D
RAND Corporation, 1776 Main Street, Box 2138, Santa Monica, CA 90401-2138 USA.
J R Stat Soc Ser A Stat Soc. 2013 Jun 1;176(3). doi: 10.1111/j.1467-985X.2012.12002.x.
There are several challenges to testing the effectiveness of group therapy-based interventions in alcohol and other drug use (AOD) treatment settings. Enrollment into AOD therapy groups typically occurs on an open (rolling) basis. Changes in therapy group membership induce a complex correlation structure among client outcomes, with relatively small numbers of clients attending each therapy group session. Primary outcomes are measured post-treatment, so each datum reflects the effect of all sessions attended by a client. The number of post-treatment outcomes assessments is typically very limited. The first feature of our modeling approach relaxes the assumption of independent random effects in the standard multiple membership model by employing conditional autoregression (CAR) to model correlation in random therapy group session effects associated with clients' attendance of common group therapy sessions. A second feature specifies a longitudinal growth model under which the posterior distribution of client-specific random effects, or growth parameters, is modeled non-parametrically. The Dirichlet process prior helps to overcome limitations of standard parametric growth models given limited numbers of longitudinal assessments. We motivate and illustrate our approach with a data set from a study of group cognitive behavioral therapy to reduce depressive symptoms among residential AOD treatment clients.
在酒精及其他药物使用（AOD）治疗环境中，测试基于团体治疗的干预措施的有效性存在若干挑战。AOD治疗组的入组通常是开放式（滚动式）的。治疗组成员的变化会在客户治疗结果之间引发复杂的相关结构，每个治疗组会议的参会客户数量相对较少。主要结果在治疗后进行测量，因此每个数据反映了客户参加的所有会议的效果。治疗后结果评估的次数通常非常有限。我们建模方法的第一个特点是通过采用条件自回归（CAR）对与客户参加共同团体治疗会议相关的随机治疗组会议效果中的相关性进行建模，从而放宽了标准多重成员模型中独立随机效应的假设。第二个特点是指定一个纵向增长模型，在该模型下，对特定客户的随机效应或增长参数的后验分布进行非参数建模。狄利克雷过程先验有助于克服在纵向评估数量有限的情况下标准参数增长模型的局限性。我们用一项关于团体认知行为疗法以减轻住院AOD治疗客户抑郁症状的研究数据集来推动并阐释我们的方法。
