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推动下，绝大多数社区计划中的药物滥用患者的治疗都是在集体治疗环境中进行的。此外，在社区项目中维持小组的最常见方法是使用“滚动”入院，即患者在不同的时间进入小组，而其他成员则离开(由于毕业或更有可能是辍学)。随着时间的推移(以非随机方式)组结构的这种持续变化的过程给研究人员带来了独特的分析挑战，他们希望解释和理解将入院纳入治疗组所产生的依赖关系，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集体治疗研究产生了扼杀的影响。许多研究人员通过(A)忽略滚动治疗组产生的相关性(导致有偏见的参数估计和/或标准误差)，(B)设计以个体干预为中心的研究(而不是以组为中心)，或(C)设计在一定时期后限制登记的研究(即“封闭”组)，来避免建立滚动入院模型的分析挑战。这三种方法的主要代价都是关于治疗效果(即第一类和/或第二类错误)的错误推断(在条件[a]下)，以及(在条件[b]和[c]下)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。大多数研究人员完全避免了团体治疗研究，因为还没有一个框架来适当地解决这些挑战，特别是在拨款和文章提交方面。这一探索性/发展性R21项目的目的是：(A)证明多重成员建模和不可忽视缺失数据(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可靠的框架；(B)通过实际数据示例和模拟建模，展示当前滚动组方法(与NIMD方法相比)相关的后果；(C)为药物滥用治疗研究人员提供关于如何分析此类数据的明确指南，以及进行这些分析所需的常用统计程序包(如SAS、STATA、MPIUS)中的编程代码。

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.