L.L. Thurstone Psychometric Laboratory, Department of Psychology, University of North Carolina, Chapel Hill, NC 27599, USA.
J Subst Abuse Treat. 2011 Jan;40(1):3-17. doi: 10.1016/j.jsat.2010.07.010. Epub 2010 Sep 15.
Modeling turnover in group membership has been identified as a key barrier contributing to a disconnect between the manner in which behavioral treatment is conducted (open-enrollment groups) and the designs of substance abuse treatment trials (closed-enrollment groups, individual therapy). Latent class pattern mixture models (LCPMMs) are emerging tools for modeling data from open-enrollment groups with membership turnover in recently proposed treatment trials. The current article illustrates an approach to conducting power analyses for open-enrollment designs based on the Monte Carlo simulation of LCPMM models using parameters derived from published data from a randomized controlled trial comparing Seeking Safety to a Community Care condition for women presenting with comorbid posttraumatic stress disorder and substance use disorders. The example addresses discrepancies between the analysis framework assumed in power analyses of many recently proposed open-enrollment trials and the proposed use of LCPMM for data analysis.
群组成员流动的建模已被确定为一个关键障碍，导致行为治疗的实施方式（开放式招募小组）与药物滥用治疗试验的设计（封闭式招募小组、个体治疗）之间脱节。潜在类别模式混合模型 (LCPMM) 是用于对有成员流动的开放式招募小组数据进行建模的新兴工具，这些数据来自最近提出的治疗试验。本文使用从一项比较寻求安全与社区护理条件的随机对照试验中得出的参数，通过对潜在类别模式混合模型进行蒙特卡罗模拟，说明了一种针对开放式招募设计进行功效分析的方法，该试验是为同时患有创伤后应激障碍和物质使用障碍的女性进行的。该示例解决了许多最近提出的开放式招募试验的功效分析中假设的分析框架与 LCPMM 用于数据分析的建议之间的差异。
