Optimizing Brief Alcohol Interventions for Young Adults via Computational Methods
Optimizing Brief Alcohol Interventions for Young Adults via Computational Methods
批准号:
10632007
负责人:
Eun-Young Mun
金额:
$13.43万
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
未结题
起止时间:
2021-06-01 至 2026-05-31
关键词:
AdministratorAdolescent and Young AdultAttentionBehavioral MechanismsBig DataBig Data to KnowledgeBiomedical ResearchClinical DataClinical ResearchClinical TrialsCohort EffectCollaborationsComparative Effectiveness ResearchComputing MethodologiesDataData AggregationData AnalysesData ScienceData SetData SourcesDatabasesDevelopment PlansDimensionsDisparateEducational workshopEligibility DeterminationEnsureEnvironmentFamilyFutureGoalsGrantHealth SciencesHealthy People 2020Heavy DrinkingHeterogeneityIndependent Scientist AwardIndividualInterventionIntervention StudiesKnowledgeKnowledge acquisitionLinkLiquid substanceMedical ResearchMeta-AnalysisMethodsMissionNIH Program AnnouncementsNational Institute on Alcohol Abuse and AlcoholismOutcomePaperParticipantPeer ReviewPopulationPreventionProblem SolvingPublicationsPublishingRaceReadingRecording of previous eventsReportingResearchResearch PersonnelSamplingSchoolsSex DifferencesStatistical MethodsStrategic PlanningSumTechnologyTestingTimeTrainingUnited States National Institutes of HealthUpdateVisitWorkadaptive interventionaddictionalcohol interventionalcohol researchbehavior changebrief alcohol interventionbrief motivational interventioncareercareer developmentcohortcomparative effectivenessdata integrationdata sharingexperienceimprovedinnovationinsightintervention effectlearning strategymachine learning algorithmmeetingsmultiple data sourcespersonalized interventionprecision medicineprogramsracial differenceresearch and developmentsexskillssymposiumsynergismtoolunderage drinkingunderage drinking preventionuniversity studentuptakeyoung adult
中文摘要
项目摘要
这一独立科学家奖(K02)的目标是在一段密集的时间内寻求“保护时间”
研究重点是提升应聘者的职业发展水平,并进行比较研究
相互竞争的短暂酒精干预的有效性及其机制。建议的研究策略
代表NIAAA支持的持续独立研究计划(R01 AA019511:估计
青少年酒精干预的有效性比较,得分为3百分位数)。这个K02
将R01扩展(1),包括来自简要酒精的个人参与者数据的额外25个数据集
2013至2020年间进行的干预,以及(2)采用机器学习算法和其他
最先进的统计方法,帮助识别行为变化的机制,并整合和
在各种不同的研究中综合它们。建议的保护时间将对更新和
在当前这个快速变化的临床研究环境中丰富我的研究计划。随着积累
高质量的临床数据和真实世界的数据在新兴数据科学能力的背景下,存在
机遇与挑战并存。我非常适合应对前所未有的新挑战
机会,考虑到我在统计数据整合方面的工作和在酒精研究方面的广泛经验。我也想
创建一个大型数据库,在其中可以同时检查长期/队列效应、干预效应
并通过开发和使用新工具在最细粒度的数据级别实现异构性。我的职业目标是
成为“主要的枢纽之一”,将不同的研究人员网络连接成一个大型的互联网络
网络,促进流体互动。来自大型跨学科网络的协同效应将是有帮助的
迈向大数据的“价值”主张--来自
调查人员的合作比各自单独的追求更重要。朝着这个目标,事业发展
活动将是:(1)培训计算机器学习算法和真实世界的大维数据
分析以促进与成瘾研究人员和统计学家的合作;(2)增进我的理解
技术辅助的适应性干预和被动收集的结果数据;(3)更新我的
与上瘾有关的基础健康科学知识;及(4)提交至少两份出版物和两份补助金
每年使用K02中建议的新获得的知识和技能。我已经确认了六个高度活跃的,
在各自领域获得全国赞誉的专家。我将举行个人和小组研究会议,与
所有六位合作者(本次K02的三位新合作者)共同开发创新项目。另外,我会
参加研讨会和会议进行培训,开展在线培训,并有指导阅读和
与合作者讨论。我将拜访合作者,深入讨论和解决问题。改进
生物医学和临床研究的大数据能力一直是美国国立卫生研究院的主要战略计划之一,
我有动力和准备帮助完成这一关键任务。
英文摘要
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.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Optimizing Brief Alcohol Interventions for Young Adults via Computational Methods
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批准号:10403667
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项目类别:
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资助金额:$13.81万
-
财政年份:2021
-
负责人:Eun-Young Mun
-
依托单位:
Optimizing Brief Alcohol Interventions for Young Adults via Computational Methods
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批准号:10223741
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项目类别:
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资助金额:$13.48万
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财政年份:2021
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负责人:Eun-Young Mun
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依托单位:
Innovative Analyses of Alcohol Intervention Trials for College Students
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批准号:8242784
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项目类别:
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资助金额:$56.55万
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财政年份:2010
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负责人:Eun-Young Mun
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依托单位:
Innovative Analyses of Alcohol Intervention Trials for College Students
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批准号:7866131
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项目类别:
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资助金额:$55.09万
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财政年份:2010
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负责人:Eun-Young Mun
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依托单位:
Innovative Analyses of Alcohol Intervention Trials for College Students
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批准号:8319934
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项目类别:
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资助金额:$5.71万
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财政年份:2010
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负责人:Eun-Young Mun
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依托单位:
ESTIMATING COMPARATIVE EFFECTIVENESS OF ALCOHOL INTERVENTIONS FOR YOUNG ADULTS
-
批准号:9604545
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项目类别:
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资助金额:$37.66万
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财政年份:2010
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负责人:Eun-Young Mun
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依托单位:
ESTIMATING COMPARATIVE EFFECTIVENESS OF ALCOHOL INTERVENTIONS FOR YOUNG ADULTS
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批准号:9303572
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项目类别:
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资助金额:$13.38万
-
财政年份:2010
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负责人:Eun-Young Mun
-
依托单位:
Innovative Analyses of Alcohol Intervention Trials for College Students
-
批准号:8451999
-
项目类别:
-
资助金额:$53.02万
-
财政年份:2010
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负责人:Eun-Young Mun
-
依托单位:
ESTIMATING COMPARATIVE EFFECTIVENESS OF ALCOHOL INTERVENTIONS FOR YOUNG ADULTS
-
批准号:9915818
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项目类别:
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资助金额:$45.29万
-
财政年份:2010
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负责人:Eun-Young Mun
-
依托单位:
Innovative Analyses of Alcohol Intervention Trials for College Students
-
批准号:8064767
-
项目类别:
-
资助金额:$51.47万
-
财政年份:2010
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负责人:Eun-Young Mun
-
依托单位:
海外基金