An Integrative Approach to Control Group Creation for Prevention Research

预防研究控制组创建的综合方法

• 批准号: 9045107
• 负责人: William B. Hansen
• 金额: $ 27.05万
• 依托单位: PREVENTION STRATEGIES, LLC
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2017-12-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9045107
• 关键词: Address; Adolescent; Affect; Alcohol consumption; Alcohols; Algorithms; Archives; Area; Big Data; Biomedical Computing; Businesses; Characteristics; Communities; Control Groups; Data; Data Collection; Data Science; Data Set; Databases; Development; Drug Prevention Program; Drug usage; Effectiveness; Effectiveness of Interventions; Elements; Epidemiologic Studies; Funding Opportunities; Future; Goals; Informatics; Intervention; Knowledge; Measures; Mediating; Methodology; Methods; Modeling; Participant; Pattern; Pharmaceutical Preparations; Phase; Prevention; Prevention Research; Prevention approach; Prevention program; Preventive Intervention; Randomized; Randomized Controlled Trials; Research; Research Personnel; Research Project Grants; Resources; Schools; Small Business Innovation Research Grant; Statistical Models; Technology; Testing; Time; alcohol prevention; case control; cost; course development; data integration; demographics; models and simulation; programs; psychosocial; public health relevance; research study; response; therapy development; treatment group

 DESCRIPTION (provided by applicant): The goal of this SBIR project is to develop a method for creating algorithmically generated control groups. 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.

 描述（由申请人提供）：该 SBIR 项目的目标是开发一种创建算法生成的对照组的方法，这些对照组将由具有两个特征的合成案例组成：（1）与可能出现的任何预防治疗组的预测试相似性。 (2) 随着时间的推移，与酒精和毒品使用发展的正常过程密切相关的变化模式 酒精和毒品预防领域已经非常成熟，并且拥有可用于这一目的的丰富数据资源。大量的学校和社区效能研究在随机对照试验中采用了对照组。鉴于有大量数据可供分析，我们建议收集数据。将开发一种算法，该算法将创建与治疗组的人口统计数据和预测试中介变量分数相匹配的综合控制案例，然后估计未来的药物使用情况。 .的几个好处预计酒精和毒品预防研究人员和从业人员将能够使用这种方法来测试传播干预措施的有效性，并快速评估新的和替代预防干预措施的潜力。在无法进行随机化的情况下传播的酒精和​​毒品预防计划的有效性该方法还可以通过减少招募、受试者和现场数据保留收集以及预测试不等同性等方面的挑战，使评估新的和改编的计划变得更加容易。教室、学校和社区是分配给条件。