Emerging Issues in Analyzing Group-Based Treatment Data under Open Enrollment

描述（由申请人提供）：毫无疑问，大多数用于治疗酒精滥用和依赖的心理社会干预措施都是以小组治疗的形式提供的。在许多情况下，治疗组使用开放注册或“滚动”入院来维持和补充，由此患者在不同时间进入正在进行的治疗组，而其他成员离开（由于毕业或辍学）。随着时间的推移，治疗组成员的这种会话到会话的变化过程（通常以非随机的方式发生）为希望解释和理解开放入组治疗组所产生的依赖性以及在此背景下正确建模治疗效果的研究者带来了独特的分析挑战。在酒精治疗试验数据分析中，处理组成员的会话间变化的困难被认为是许多研究人员避免开放招募数据分析挑战的主要原因之一。为了避免这些困难，研究人员（a）忽略了滚动治疗组产生的依赖性（导致有偏的参数估计和/或标准误），（B）设计了以基于个体的干预措施为中心的研究（而不是组），或（c）设计了限制一定时期后招募的研究（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）。 面对许多这些分析和设计挑战，进一步开发和传播在酒精治疗试验背景下处理开放招募数据的方法（即，多成员模型、模式混合模型）是非常需要的。本探索性-发展性R21项目的目的是探索分析框架的效用，这些分析框架可作为处理开放入组数据的基础，用于解决以下三个特定设计和/或分析问题： (a)合并到普通酒精治疗试验设计中，（B）在开放招募的试验中对治疗作用机制进行建模，和（c）对各种基于组的酒精治疗设计（例如，基于组的tx A与基于组的tx B，组tx与个体tx）。这项探索性/发展性资助申请将使用统计模拟建模和正在进行的酒精治疗试验的数据相结合，最终目标是为酒精治疗研究人员提供关于如何分析此类数据的明确教学示例，并为开放式招募试验设计提供样本量估计指南。

DESCRIPTION (provided by applicant): There is little question that most psychosocial interventions used to treat alcohol abuse and dependence are delivered in a group therapy format. In many settings, therapy groups are sustained and replenished using open enrollment, or "rolling" admission, whereby patients enter ongoing therapy groups at various times, while other members leave (due to graduation or dropout). This process of session-to-session change in therapy group membership over time (usually occurring in a nonrandom fashion) creates unique analytic challenges for investigators who wish to account for and understand the dependencies created by open enrollment into therapy groups and for the proper modeling of treatment effects in this context. Difficulties in handling session-to-session changes in group membership in the analysis of alcohol treatment trial data have been cited as one of the major reasons that many investigators avoid the analytic challenges of open enrollment data. To avoid these difficulties, investigators have (a) ignored dependencies created by rolling treatment groups (leading to biased parameter estimation and/or standard errors), (b) designed studies that center on individual-based interventions (as opposed to groups), or (c) designed 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]). In the face of many of these analytic and design challenges, further development and dissemination of approaches to handle open enrollment data in the context of alcohol treatment trials (i.e., multiple membership models, pattern mixture models) are sorely needed. The purpose of this exploratory- developmental R21 project is to explore the utility of analytic frameworks that may serve as the foundation for handling open enrollment data for three specific design and/or analytic problems: (a) incorporation into common alcohol treatment trial designs, (b) modeling of mechanisms of treatment action in trials with open enrollment, and (c) statistical power analysis for various group-based alcohol treatment designs (e.g., group-based tx A versus group-based tx B, group tx versus individual tx) under open enrollment. This exploratory/developmental grant application will use a combination of statistical simulation modeling and data from an ongoing alcohol treatment trial with the ultimate goal of providing alcohol treatment researchers with clear pedagogical examples on how to analyze such data and providing guidelines for sample size estimation specifically for open enrollment trial designs.