Biostatistics Data Analysis Core

生物统计学数据分析核心

基本信息

项目摘要

The Biostatistical Design and Analysis Core (BDAC) was established in year 3 of the first funding cycle in response to user surveys which expressed a strong need for sound and efficient biostatistical expertise and resource support. The overall aims of BDAC are to provide data management and biostatistical resources and expertise to promote best statistical practices; and to facilitate the development of function-promoting therapies through research and the training of biomedical and data scientists committed to aging research. BDAC activities are directed toward the three mutually reinforcing Specific Aims: (1) To provide rigorous, secure and comprehensive biostatistical and data management support to OAIC projects. (2) To conduct novel applied and methodologic research aligned with the OAIC initiatives, with emphasis on conduct of intervention trials and translational research in FPTs. (3) To insure rigorous training in research design and data analysis of clinical and quantitative scientists, with emphasis on the epidemiology and treatment of loss of physical function in aging. The BDAC's outstanding leadership has substantial expertise in clinical trials, observational studies, experimental design, multivariate analysis, causal inference, statistical genetics, graphical data display, and missing data, and is seasoned in the leadership of data science teams. The Core is led by Thomas Travison, PhD (Core Leader), a biostatistician and translational researcher with 15 years' experience in the design and analysis of clinical studies in aging; and Ralph D'Agostino, PhD (Core Co-Leader), an internationally recognized leader in clinical trials design, observational studies, and comparative effectiveness research; and Paola Sebastiani, Ph.D., an internationally recognized statistical geneticist. Core personnel maintain active and highly productive collaborative relationships with OAIC biomedical investigators, as well as leadership in independent and collaborative research programs with allied scientists. In addition, the BDAC provides key venues for dynamic mentoring, collaboration and peer support for OAIC investigators and trainees through its interactive methods workshop and seminar series, consultative services, and the continual interaction between Core personnel and OAIC-affiliated scientists and physicians. The BDAC has embedded continuous innovation in its operations; examples of this innovation include the novel risk scoring algorithms developed by the BDAC biostatisticians for mobility disability and cardiometabolic illnesses in older populations; collaborative work with OAIC-affiliated biostatisticians at Yale and Wake Forest universities in designing novel randomization schemes for pragmatic cluster-randomized trials; and the development of novel and integrated web-enabled tools in the developmental project, which enhance the replicability and reproducibility of scientific findings. The BDAC is therefore a key contributor to the Boston OAIC's outstanding record of success.
生物统计设计和分析核心(BDAC)成立于2010年第一个供资周期的第3年。 对用户调查的答复,这些调查表示强烈需要健全和有效的生物统计专门知识, 资源支持。BDAC的总体目标是提供数据管理和生物统计资源, 促进最佳统计实践的专业知识;并促进功能促进疗法的发展 通过研究和培训致力于老龄化研究的生物医学和数据科学家。BDAC 活动是针对三个相互加强的具体目标:(1)提供严格的,安全的, 为非洲信息中心项目提供全面的生物统计和数据管理支持。(2)进行新的应用 与OAIC倡议一致的方法学研究,重点是干预试验的进行 和FPT中的翻译研究。(3)确保在研究设计和数据分析方面的严格培训, 临床和定量科学家,重点是流行病学和物理损失的治疗 在衰老中的作用 BDAC的杰出领导在临床试验、观察性研究、 实验设计,多变量分析,因果推理,统计遗传学,图形数据显示, 缺少数据,并在数据科学团队的领导方面经验丰富。核心由托马斯·崔维森领导 博士(核心负责人),生物统计学家和翻译研究员,在设计和 老龄化的临床研究分析;和Ralph D 'Agostino博士(核心联合负责人),国际 临床试验设计、观察性研究和比较有效性研究领域公认的领导者; 保拉蒂亚尼博士,国际公认的统计遗传学家 核心人员与OAIC生物医学保持积极和高效的合作关系 研究人员,以及领导独立和协作研究计划与盟军科学家。 此外,BDAC为OAIC提供了动态指导、协作和同行支持的重要场所 通过互动方法讲习班和系列研讨会,咨询服务, 以及核心人员与OAIC附属科学家和医生之间的持续互动。的 BDAC在其运营中不断创新;这种创新的例子包括新颖的 由BDAC生物统计学家开发的用于移动性残疾和心脏代谢的风险评分算法 老年人的疾病;与耶鲁大学和维克森林的OAIC附属生物统计学家合作 大学在设计新的随机化方案的实用集群随机试验;和 在发展项目中开发新的综合网络工具, 科学发现的可复制性和再现性。因此,BDAC是波士顿的主要贡献者。 审计和调查办公室出色的成功记录。

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)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Thomas Glenn Travison其他文献

Thomas Glenn Travison的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Thomas Glenn Travison', 18)}}的其他基金

Data Management and Statistical Analysis Core (Core C)
数据管理和统计分析核心(核心C)
  • 批准号:
    10405116
  • 财政年份:
    2018
  • 资助金额:
    $ 18.96万
  • 项目类别:
Biostatistics and Data Science Core
生物统计学和数据科学核心
  • 批准号:
    10293914
  • 财政年份:
    2008
  • 资助金额:
    $ 18.96万
  • 项目类别:
Biostatistics and Data Science Core
生物统计学和数据科学核心
  • 批准号:
    10470358
  • 财政年份:
    2008
  • 资助金额:
    $ 18.96万
  • 项目类别:
Biostatistics and Data Science Core
生物统计学和数据科学核心
  • 批准号:
    10678847
  • 财政年份:
    2008
  • 资助金额:
    $ 18.96万
  • 项目类别:

相似海外基金

CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
  • 批准号:
    2337776
  • 财政年份:
    2024
  • 资助金额:
    $ 18.96万
  • 项目类别:
    Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
  • 批准号:
    2338816
  • 财政年份:
    2024
  • 资助金额:
    $ 18.96万
  • 项目类别:
    Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
  • 批准号:
    2338846
  • 财政年份:
    2024
  • 资助金额:
    $ 18.96万
  • 项目类别:
    Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
  • 批准号:
    2348261
  • 财政年份:
    2024
  • 资助金额:
    $ 18.96万
  • 项目类别:
    Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
  • 批准号:
    2348346
  • 财政年份:
    2024
  • 资助金额:
    $ 18.96万
  • 项目类别:
    Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
  • 批准号:
    2348457
  • 财政年份:
    2024
  • 资助金额:
    $ 18.96万
  • 项目类别:
    Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
  • 批准号:
    2404989
  • 财政年份:
    2024
  • 资助金额:
    $ 18.96万
  • 项目类别:
    Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
  • 批准号:
    2339310
  • 财政年份:
    2024
  • 资助金额:
    $ 18.96万
  • 项目类别:
    Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
  • 批准号:
    2339669
  • 财政年份:
    2024
  • 资助金额:
    $ 18.96万
  • 项目类别:
    Continuing Grant
DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
  • 批准号:
    EP/Y029089/1
  • 财政年份:
    2024
  • 资助金额:
    $ 18.96万
  • 项目类别:
    Research Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了