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

开放招募下基于群体的治疗数据分析中出现的新问题

• 批准号: 7357502
• 负责人: ANTONIO A MORGAN-LOPEZ
• 金额: $ 13.54万
• 依托单位: RESEARCH TRIANGLE INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-02-15 至 2010-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7357502
• 关键词: Accounting; Address; Admission activity; Alcohol abuse; Applications Grants; Area; Boxing; Communities; Condition; Consultations; Counseling; Data; Data Analyses; Dependence; Dependency; Development; Doctor of Philosophy; Dropout; Economics; Enrollment; Environment; Exploratory/Developmental Grant; Face; Foundations; Goals; Group Therapy; Guidelines; Individual; Intervention; Lead; Left; Life; Literature; Mediator of activation protein; Methods; Modeling; Nature; Outcome; Participant; Patients; Pattern; Price; Process; Provider; Psychologist; Purpose; Research; Research Design; Research Personnel; Sample Size; Standards of Weights and Measures; Statistical Methods; Stifle joint; Therapeutic; Therapeutic Studies; Time; Treatment Efficacy; alcohol abuse therapy; base; cost; data modeling; design; efficacy trial; member; model development; models and simulation; multilevel analysis; programs; psychosocial; response; sound; therapy design; tool; treatment effect; treatment program; treatment trial

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.

描述(由申请人提供)：毫无疑问，大多数用于治疗酗酒和依赖的心理社会干预都是以团体治疗的形式提供的。在许多情况下，治疗小组是通过开放注册或“滚动”入院来维持和补充的，即患者在不同的时间进入正在进行的治疗小组，而其他成员则离开(由于毕业或辍学)。随着时间的推移(通常以非随机的方式发生)，治疗组成员的这种会议到会议的变化过程为研究者带来了独特的分析挑战，他们希望解释和理解开放登记到治疗小组中所产生的依赖关系，以及在这种情况下治疗效果的适当建模。在酒精治疗试验数据分析中处理组成员的逐期变化的困难被认为是许多研究人员避免公开登记数据的分析挑战的主要原因之一。为了避免这些困难，研究人员(A)忽略了滚动治疗组造成的依赖(导致有偏见的参数估计和/或标准误差)，(B)设计了以个体干预为中心的研究(而不是以组为中心)，或(C)设计了在一定时期后限制登记的研究(即“封闭”组)。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。 面对许多这样的分析和设计挑战，迫切需要进一步开发和传播在酒精治疗试验背景下处理开放登记数据的方法(即多成员模型、模式混合模型)。这个探索性开发的R21项目的目的是探索分析框架的效用，这些框架可以作为处理三个特定设计和/或分析问题的开放注册数据的基础： (A)纳入普通酒精治疗试验设计，(B)在开放登记的试验中建立治疗作用机制的模型，以及(C)在开放登记下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)进行统计功率分析。这一探索性/发展性拨款申请将结合统计模拟建模和正在进行的酒精治疗试验的数据，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范例，并为专门针对开放登记试验设计的样本量估计提供指南。