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（由适用提供）：该SBIR项目的目标是开发一种创建算法生成的控制组的方法。这些将由具有两个特征的合成案例组成：（1）与任何预防治疗组可能出现的预测相似性，以及（2）随着时间的推移的变化模式，这些模式紧密地模仿了饮酒和药物使用的正常过程。酒精和药物预防的领域已经大大成熟，并且具有丰富的数据资源，可以在此目的进行。值得注意的是，许多学校和社区效率研究都在随机对照试验中工作了对照组。其他病因和流行病学研究收集了相似的数据。鉴于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。我们建议从以前的研究中收集数据，并开发可用于预测酒精和药物使用的静态模型。将开发一种算法，该算法将创建集成的控制案例，以匹配治疗组的人口统计数据和预测的变量分数，然后估算未来的药物使用。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。具体而言，该方法将提供一种评估酒精和药物预防计划的有效性的方法，这些计划在无法随机化时被传播。这种方法还将通过减少招募，主题保留和现场数据收集和预测试的非等价性的挑战来使评估新的和改编的程序更加容易，这些挑战通常是在将L教室，学校和社区分配到条件时。