Analyzing Data Generated from Therapy Groups with Rolling Admissions

描述（由申请人提供）： 在临床和成本考虑的驱动下，绝大多数药物滥用患者在社区方案中的治疗是在团体治疗环境中提供的。此外，在社区方案中维持团体的最常见方法是使用“滚动”入院，即病人在不同时间进入团体，而其他成员离开（由于毕业或更可能的是辍学）。随着时间的推移，组结构不断变化的过程（以非随机的方式）给研究者带来了独特的分析挑战，研究者希望解释和理解通过将入院患者滚动到治疗组所产生的依赖性，并在此背景下对治疗效果进行适当的建模。这个棘手的分析问题对药物滥用治疗中的团体治疗研究产生了抑制作用。许多研究者通过以下方式避免了对滚动入院进行建模的分析挑战：（a）忽略滚动治疗组产生的依赖性（导致有偏参数估计和/或标准误），（B）设计以基于个体的干预措施为中心的研究（与组相反），或（c）设计在一定时期后限制入组的研究（即，“封闭”群体）。这三种方法中每一种的主要成本是关于治疗效果的不正确推断（即，I型和/或II型错误）（条件[a]下）以及如何进行疗效试验与如何在社区环境中进行治疗之间的脱节（条件[B]和[c]下）。大多数研究者完全避免了团体治疗研究，因为还没有一个框架来妥善解决这些挑战，特别是在赠款和文章提交。这个探索性/发展性的R21项目的目的是：（a）证明在多重隶属建模和不可重复缺失数据（NIMD）建模领域的统计发展实际上可以为滚动组数据的分析提供理论上合理的框架，（B）说明目前采用的滚动分组办法的后果（与NIMD方法相比）通过真实的数据实例和模拟建模，以及（c）为物质滥用治疗研究人员提供关于如何分析这种数据的明确指导，沿着来自流行统计包的必要编程代码（例如，SAS、STATA、MPIus）进行这些分析。

DESCRIPTION (provided by applicant): Driven by both clinical and cost considerations, the vast majority, of treatment for substance-abusing patients in community-based programs is delivered in a group therapy milieu. Moreover, the most common approach to sustain groups in community programs is the use of "rolling" admission, whereby patients enter groups at various times, while other members leave (due to graduation or, more likely, dropout). This process of continual change in group structure over time (and in a nonrandom fashion) creates unique analytic challenges to investigators who wish to account for and understand the dependencies created by rolling admission into therapy groups and for the proper modeling of treatment effects in this context. This thorny analytic problem has had a stifling effect on group therapy research in drug abuse treatment. Many investigators avoid the analytic challenges of modeling rolling admissions by (a) ignoring dependencies created by rolling treatment groups (leading to biased parameter estimation and/or standard errors), (b) designing studies that center on individual-based interventions (as opposed to groups), or (c) designing studies that restrict enrollment after a certain period (i.e., "closed" groups). The major costs of each of these three approaches are incorrect inferences concerning treatment effects (i.e., Type I and/or Type II errors) (under condition [a]) and a disconnect between how treatment efficacy trials are conducted and how treatment takes place in community settings (under conditions [b] and [c]). Most investigators have avoided group therapy research altogether because there has not been a framework to properly address these challenges, particularly in grant and article submissions. The purpose of this exploratory/developmental R21 project is to (a) demonstrate that statistical developments in the area of multiple membership modeling and non-ignorable missing data (NIMD) modeling may actually provide theoretically sound frameworks for the analysis of rolling group data, (b) demonstrate the consequences associated with current approaches to rolling groups (as compared to NIMD methods) through real data examples and simulation modeling and (c) provide substance abuse treatment researchers with clear guidelines on how to analyze such data, along with requisite programming code from popular statistical packages (e.g., SAS, STATA, MPIus) to carry out these analyses.