Optimizing Brief Alcohol Interventions for Young Adults via Computational Methods

通过计算方法优化年轻人的短暂酒精干预

基本信息

批准号：
10223741
负责人：
Eun-Young Mun
金额：
$ 13.48万
依托单位：
UNIVERSITY OF NORTH TEXAS HLTH SCI CTR
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-01 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10223741
关键词：
Administrator Adolescent and Young Adult Attention Behavioral Mechanisms Big Data Big Data to Knowledge Biomedical Research Clinical Data Clinical Research Clinical Trials Cohort Effect Collaborations Comparative Effectiveness Research Computing Methodologies Data Data Aggregation Data Analyses Data Science Data Set Data Sources Databases Development Plans Dimensions Educational workshop Ensure Environment Family Future Goals Grant Health Sciences Healthy People 2020 Heavy Drinking Heterogeneity Independent Scientist Award Individual Intervention Intervention Studies Knowledge Link Liquid substance Medical Research Meta-Analysis Methods Mission NIH Program Announcements National Institute on Alcohol Abuse and Alcoholism Outcome Paper Participant Peer Review Population Prevention Problem Solving Publications Publishing Race Reading Recording of previous events Reporting Research Research Personnel Sampling Schools Sex Differences Statistical Methods Strategic Planning Sum Technology Testing Time Training United States National Institutes of Health Update Visit Work adaptive intervention addiction alcohol intervention alcohol research behavior change brief alcohol intervention brief motivational intervention career career development cohort comparative effectiveness data integration data sharing experience improved innovation insight intervention effect learning strategy machine learning algorithm meetings multiple data sources personalized intervention precision medicine programs racial difference research and development sex skills symposium synergism tool underage drinking underage drinking prevention university student uptake young adult

项目摘要

Project Summary The goal of this Independent Scientist Award (K02) is to seek “protected time” for a period of intensive research focus to enhance the Candidate’s career development and to conduct research on the comparative effectiveness of competing brief alcohol interventions and their mechanisms. The proposed research strategy represents an ongoing, NIAAA-supported independent research program (R01 AA019511: Estimating Comparative Effectiveness of Alcohol Interventions for Young Adults, received 3rd percentile score). This K02 extends the R01 by (1) including an additional 25 data sets of individual participant data from brief alcohol interventions conducted between 2013 and 2020, and (2) embracing machine learning algorithms and other state-of-the-art statistical methods to help identify mechanisms of behavior change and to integrate and synthesize them across heterogeneous studies. The proposed protected time will be critical to update and enrich my research program in this current, rapidly changing clinical research environment. With accumulating high-quality clinical data and real-world data in the context of emerging data science capabilities, there exist opportunities as well as challenges. I am well suited to tackle emerging challenges for unprecedented opportunities, given my work in statistical data integration and broad experience in alcohol research. I want to create a large database where one can simultaneously examine secular/cohort effects, intervention effects, and effect heterogeneity at the most granular data level by developing and using new tools. My career goal is to become “one of the major hubs,” connecting disparate networks of researchers into a large connected network, promoting fluid interactions. The synergy from the large, interdisciplinary network would be helpful toward creating a “value” proposition of Big Data – the sum of the knowledge and insights from the collaboration by investigators is greater than isolated pursuits by each. Toward this goal, career development activities will be to (1) train in computational machine learning algorithms and real-world large-dimensional data analysis to facilitate collaboration with addiction researchers and statisticians; (2) enhance my understanding of technology-assisted adaptive interventions and passively collected outcomes data; (3) update my knowledge in basic health sciences related to addiction; and (4) submit at least two publications and two grants per year using newly acquired knowledge and skills proposed under K02. I have identified six highly active, nationally acclaimed experts in their respective fields. I will have individual and group research meetings with all six collaborators (three new collaborators for this K02) to develop innovative projects. In addition, I will attend workshops and conferences for training, carry out online training, and have guided reading and discussion with collaborators. I will visit collaborators for in-depth discussions and problem-solving. Improving big data capabilities for biomedical and clinical research has been one of the major strategic plans of the NIH, and I am motivated and prepared to help meet that critical mission.

项目概要该独立科学家奖（K02）的目标是寻求一段时间密集研究的“受保护时间” 研究重点是促进候选人的职业发展并对比较进行研究竞争性短期酒精干预措施的有效性及其机制。代表一项正在进行的、NIAAA 支持的独立研究计划（R01 AA019511：估计年轻人酒精干预措施的比较效果，获得第三个百分位分数）。通过 (1) 扩展 R01，包括来自短暂酒精的个人参与者数据的额外 25 个数据集 2013 年至 2020 年期间进行的干预措施，以及 (2) 采用机器学习算法和其他最先进的统计方法有助于识别行为改变的机制并整合和跨异构研究综合它们对于更新和更新至关重要。在当前快速变化的临床研究环境中丰富我的研究计划。在新兴数据科学能力的背景下，存在高质量的临床数据和真实世界数据我非常适合应对前所未有的新挑战。鉴于我在统计数据整合方面的工作和酒精研究方面的丰富经验，我希望有机会。创建一个大型数据库，可以同时检查/群组效应、干预效应、通过开发和使用新工具在最细粒度的数据级别上影响异构性我的职业目标是。成为“主要枢纽之一”，将不同的研究人员网络连接成一个大型的互联网络网络，促进大型跨学科网络的协同作用将有所帮助。创造大数据的“价值”主张——来自大数据的知识和见解的总和为了实现这一目标，研究人员的合作大于各自的单独追求。活动将是（1）训练计算机器学习算法和现实世界的大维数据分析以促进与成瘾研究人员和统计学家的合作；（2）增强我的理解；（3）更新我的与成瘾相关的基础健康科学知识；以及 (4) 提交至少两篇出版物和两份资助每年，我都会使用 K02 中提出的新获得的知识和技能来确定六种高度活跃的、我将与各自领域的全国知名专家举行个人和小组研究会议。所有六位合作者（本次 K02 的三位新合作者）都将开发创新项目。参加培训研讨会和会议，开展在线培训，并进行指导阅读和指导与合作者讨论。我会拜访合作者进行深入讨论和解决问题。生物医学和临床研究的大数据能力一直是NIH的重大战略计划之一，我有动力并准备好帮助完成这一重要使命。