An Integrative Approach to Control Group Creation for Prevention Research

预防研究控制组创建的综合方法

基本信息

批准号：
9754730
负责人：
William B. Hansen
金额：
$ 48.66万
依托单位：
PREVENTION STRATEGIES, LLC
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-01 至 2021-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9754730
关键词：
Adolescent Adopted Affect Alcohol consumption Alcohols Algorithms Archives Area Characteristics Charge Cigarette Code Communities Control Groups Data Data Collection Data Set Databases Development Drug Prevention Program Drug usage Effectiveness Effectiveness of Interventions Elements Ensure Etiology Funding Future Goals Health Intervention Knowledge Marijuana Measures Mediating Methods Modeling Online Systems Outcome Participant Pattern Pharmaceutical Preparations Phase Prevention Prevention Research Preventive Intervention Process Program Effectiveness Provider Randomized Research Research Personnel Research Project Grants Scheme Schools Services Small Business Innovation Research Grant Social Problems Statistical Models System Technology Testing Time Work adolescent drug use alcohol prevention alcohol research case control cigarette smoking control trial cost course development data resource demographics design efficacy study epidemiology study marijuana use programs psychosocial recruit research study substance use prevention tool treatment group virtual

项目摘要

PROJECT ABSTRACT The goal of this Phase II SBIR project is to develop a method for creating algorithmically generated control groups – virtual controls. These will be composed of synthetic cases that have two characteristics: (1) pretest similarity to whatever prevention treatment group may present itself and (2) patterns of change over time that closely mimic the normal course of alcohol and drug use development. The field of alcohol and drug prevention is one that has significantly matured and has rich data resources that can be employed to this end. Notably, numerous school and community efficacy studies have employed control groups in randomized control trials. Additional etiological and epidemiological studies have collected similar data. Given the large amount of data available for analysis, it is now possible to model the onset of alcohol and drug use among adolescents. We propose to gather data from previous studies and develop statistical models that can be used to predict the onset of alcohol and drug use. An algorithm will be developed that will create integrative control cases to match to a treatment group’s demographics and pretest mediating variable scores and then estimate future drug use. Several benefits of this method are anticipated. Alcohol and drug prevention researchers and practitioners will be able to use this approach to test the effectiveness of disseminated interventions and to quickly evaluate the potential of new and alternative prevention interventions. Specifically, this method will provide a means to evaluate the effectiveness of alcohol and drug prevention programs that are disseminated when randomization cannot occur. This method will also make evaluating new and adapted programs easier by reducing the challenges of recruitment, subject retention, and onsite data collection and pretest non-equivalence that often occur when l classrooms, schools and communities are assigned to condition. In this project, we will (1) gather data from previous longitudinal drug and alcohol research projects focused on adolescents; (2) harmonize these data to match a predefined coding scheme and include them in an integrated referential database; (3) refine and validate statistical models developed during Phase I to ensure algorithms used to generate virtual control cases are valid and replicable; (4) prepare a web-based system that will allow virtual control group technology to be maximally automated; and (5) conduct de novo field trials of disseminated interventions to demonstrate the viability of the system for providing meaningful comparisons.

项目摘要该阶段II SBIR项目的目标是开发一种创建算法生成控制的方法组 - 虚拟控件。这些将由具有两个特征的合成案例组成：（1）预测试与任何预防治疗组可能出现的相似性，以及（2）随着时间的推移的变化模式密切模仿酒精和药物使用的正常过程。酒精和药物预防领域是已经大量成熟并拥有丰富的数据资源的人可以执行的。尤其，许多学校和社区效率研究都在随机对照试验中雇用了对照组。其他病因和流行病学研究收集了相似的数据。给定大量数据可用于分析，现在可以对青少年的酒精和药物使用进行建模。我们提议从以前的研究中收集数据并开发可用于预测的统计模型酒精和吸毒的发作。将开发一种算法，将创建集成控制案例以匹配进行治疗组的人口统计学和预测的介导可变分数，然后估计未来的药物使用。预计该方法的几个好处。酒精和药物预防研究人员和从业者将能够使用这种方法来测试传播干预措施的有效性，并快速评估新的预防干预措施的潜力。具体来说，这种方法将提供一种手段评估随机化时散布的酒精和药物预防计划的有效性无法发生。此方法还将通过降低来评估新的和改编的程序。招聘，主题保留和现场数据收集和预测试的挑战通常当L教室，学校和社区被分配到条件时发生。在这个项目中，我们将（1）聚集以前的纵向药物和酒精研究项目的数据集中在青少年上；（2）和谐这些数据以匹配预定义的编码方案，并将其包含在集成的参考数据库中；（3）在第一阶段开发的完善和验证统计模型，以确保用于生成的算法虚拟控制案例是有效且可复制的；（4）准备基于网络的系统，该系统将允许虚拟控制团体技术将被最高自动化；（5）进行传播干预措施的从头野外试验证明系统提供有意义的比较的可行性。