Data integration for causal inference in behavioral health
行为健康因果推理的数据集成
基本信息
- 批准号:10393600
- 负责人:
- 金额:$ 26.26万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-07-01 至 2025-06-30
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Behavioral health, broadly defined to include mental health and substance use, includes many of the most
pressing public health problems of our time. The transition to a data-rich, web-interconnected society has
generated an opportunity to generate solutions, but it necessitates a paradigm shift in workforce training in
data analytics. The goal of this training program is to train scholars to become leaders in the use of advanced
computational methods and designs to estimate causal effects in behavioral health. To accomplish this goal,
we will provide rigorous training and high-quality mentorship in: 1) the science of behavioral health; 2)
computational and analytic tools to manage, analyze, and integrate complex data sources; and 3) causal
inference methods to take full advantage of these data. Trainees will receive interdisciplinary team-based
training and will acquire a deep understanding of all three areas. This training program will capitalize on the
rich resources for behavioral health, analytic and computational methods, and biostatistics at the Johns
Hopkins Bloomberg School of Public Health (JHSPH) and the broader University. The program will be housed
in the Department of Mental Health but the 5 trainees per year will come from any of the four social science
oriented departments at JHSPH: 1) Mental Health; 2) Health Behavior & Society; 3) Health Policy &
Management; and 4) Population Family & Reproductive Health. Further, the training grant will leverage close
connections with data scientists, statisticians, and computer scientists from across the University. Trainees will
obtain the skills and experiences needed to lead multi-disciplinary, collaborative research teams. Trainees will
undertake a rigorous program of coursework in the core domains of public health and behavioral health
including behavioral and social science, epidemiology, biostatistics, data science, population health
informatics, causal inference, and research ethics. In addition, each trainee will take additional elective courses
in social and behavioral perspectives on mental health and substance use, informatics and computational skills,
and causal and statistical inference. Trainees will participate in a year-long seminar on analytics for behavioral
health, a bi-weekly seminar to discuss research in progress and professional development, ongoing mentored
research projects, and integrative activities to complement their didactic curriculum. The focus area of the
program builds on strengths within JHSPH; these areas also are highlighted as priorities by OBSSR, NIMH,
and NIDA. The trainees will be supported by an experienced group of 21 core faculty and each trainee will be
co-advised by one of 9 affiliated faculty with methodological expertise. The training program director, Dr.
Elizabeth Stuart, is a national leader in analytic tools for behavioral health, and will be supported by a 4-
member internal Executive Committee and a 5-member external Advisory Committee. The overarching aim
of the program is to identify and train scholars who will become leaders in using a diversity of advanced
analytic tools and data to answer key questions in behavioral health.
行为健康,广义上包括心理健康和物质使用,包括许多最重要的
我们这个时代最紧迫的公共卫生问题。向数据丰富、网络互联的社会过渡,
产生了一个产生解决方案的机会,但它需要在劳动力培训方面进行范式转变,
数据分析这个培训计划的目标是培养学者成为领导者在使用先进的
计算方法和设计,以估计行为健康的因果影响。为了实现这一目标,
我们将提供严格的培训和高质量的指导:1)行为健康科学; 2)
用于管理、分析和整合复杂数据源的计算和分析工具;以及3)因果关系
充分利用这些数据的推理方法。学员将获得跨学科的团队为基础的
培训,并将获得对所有三个领域的深刻理解。该培训计划将利用
丰富的资源,行为健康,分析和计算方法,以及生物统计学在约翰
霍普金斯布隆伯格公共卫生学院(JHSPH)和更广泛的大学。该计划将被安置在
但每年5名受训人员将来自四个社会科学部门中的任何一个。
在JHSPH面向部门:1)心理健康; 2)健康行为与社会; 3)健康政策和
(4)人口、家庭和生殖健康。此外,培训补助金将利用
与来自整个大学的数据科学家,统计学家和计算机科学家的联系。学员将
获得领导多学科合作研究团队所需的技能和经验。学员将
在公共卫生和行为健康的核心领域进行严格的课程计划
包括行为和社会科学、流行病学、生物统计学、数据科学、人口健康
信息学、因果推理和研究伦理。此外,每名受训人员还将选修其他课程
在心理健康和物质使用的社会和行为观点,信息学和计算技能,
以及因果和统计推断。学员将参加为期一年的行为分析研讨会,
保健,每两周一次的研讨会,讨论正在进行的研究和专业发展,持续辅导
研究项目和综合活动,以补充他们的教学课程。的重点领域
计划建立在JHSPH的优势之上;这些领域也被OBSSR,NIMH,
和NIDA。学员将得到一个由21名核心教师组成的经验丰富的小组的支持,每个学员将
由9个具有方法论专业知识的附属学院之一共同提供建议。培训计划主任,博士。
伊丽莎白·斯图尔特是行为健康分析工具的全国领导者,她将得到一个4-
一个内部执行委员会和一个5人外部咨询委员会。首要目标
该计划的目的是确定和培养学者谁将成为领导者在使用先进的多样性
分析工具和数据,以回答行为健康的关键问题。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Elizabeth A. Stuart其他文献
The Lancet Psychiatry Commission: transforming mental health implementation research.
柳叶刀精神病学委员会:转变心理健康实施研究。
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:64.3
- 作者:
E. Mcginty;Margarita Alegria;R. Beidas;Jeffrey Braithwaite;Lola Kola;Douglas L Leslie;Nathalie Moise;Bernardo Mueller;H. A. Pincus;Rahul Shidhaye;Kosali Simon;Sara J Singer;Elizabeth A. Stuart;Matthew D Eisenberg - 通讯作者:
Matthew D Eisenberg
The association between cortisol and neighborhood disadvantage in a U.S. population-based sample of adolescents
- DOI:
10.1016/j.healthplace.2013.11.001 - 发表时间:
2014-01-01 - 期刊:
- 影响因子:
- 作者:
Kara E. Rudolph;Wand Gary S.;Elizabeth A. Stuart;Thomas A. Glass;Andrea H. Marques;Roman Duncko;Kathleen R. Merikangas - 通讯作者:
Kathleen R. Merikangas
Assets and depression in U.S. adults during the COVID-19 pandemic: a systematic review
- DOI:
10.1007/s00127-023-02565-2 - 发表时间:
2023-10-15 - 期刊:
- 影响因子:3.500
- 作者:
Catherine K. Ettman;Maya Subramanian;Alice Y. Fan;Gaelen P. Adam;Salma M. Abdalla;Sandro Galea;Elizabeth A. Stuart - 通讯作者:
Elizabeth A. Stuart
Efectos de la Exposición de los Adolescentes a la Violencia en la Comunidad: El Proyecto MORE
社区暴力对青少年的影响:El Proyecto 更多
- DOI:
10.5093/in2011v20n2a2 - 发表时间:
2011 - 期刊:
- 影响因子:4.8
- 作者:
Michele Cooley;Tanya J. Quille;Rob Griffin;Elizabeth A. Stuart;Catherine P. Bradshaw;D. Furr - 通讯作者:
D. Furr
Using Potential Outcomes to Understand Causal Mediation Analysis: Comment on Maxwell, Cole, and Mitchell (2011)
使用潜在结果来理解因果中介分析:评论麦克斯韦、科尔和米切尔 (2011)
- DOI:
- 发表时间:
2013 - 期刊:
- 影响因子:0
- 作者:
K. Imai;Booil Jo;Elizabeth A. Stuart - 通讯作者:
Elizabeth A. Stuart
Elizabeth A. Stuart的其他文献
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{{ truncateString('Elizabeth A. Stuart', 18)}}的其他基金
Combining data sources to identify effect moderation for personalized mental health treatment
结合数据源来确定个性化心理健康治疗的效果调节
- 批准号:
10629398 - 财政年份:2021
- 资助金额:
$ 26.26万 - 项目类别:
Combining data sources to identify effect moderation for personalized mental health treatment
结合数据源来确定个性化心理健康治疗的效果调节
- 批准号:
10471956 - 财政年份:2021
- 资助金额:
$ 26.26万 - 项目类别:
Combining data sources to identify effect moderation for personalized mental health treatment
结合数据源来确定个性化心理健康治疗的效果调节
- 批准号:
10269293 - 财政年份:2021
- 资助金额:
$ 26.26万 - 项目类别:
Data integration for causal inference in behavioral health
行为健康因果推理的数据集成
- 批准号:
10649426 - 财政年份:2020
- 资助金额:
$ 26.26万 - 项目类别:
Data integration for causal inference in behavioral health
行为健康因果推理的数据集成
- 批准号:
10164866 - 财政年份:2020
- 资助金额:
$ 26.26万 - 项目类别:
Mental Health Services and Systems Training Program
心理健康服务和系统培训计划
- 批准号:
10624522 - 财政年份:2017
- 资助金额:
$ 26.26万 - 项目类别:
Using propensity scores for causal inference with covariate measurement error
使用倾向得分进行带有协变量测量误差的因果推断
- 批准号:
9102249 - 财政年份:2013
- 资助金额:
$ 26.26万 - 项目类别:
Using propensity scores for causal inference with covariate measurement error
使用倾向得分进行带有协变量测量误差的因果推断
- 批准号:
8576817 - 财政年份:2013
- 资助金额:
$ 26.26万 - 项目类别:
Using propensity scores for causal inference with covariate measurement error
使用倾向得分进行带有协变量测量误差的因果推断
- 批准号:
8690155 - 财政年份:2013
- 资助金额:
$ 26.26万 - 项目类别:
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