Core C: Data Management and Statistics Core p293-309

核心 C：数据管理和统计核心 p293-309

• 批准号: 8590423
• 负责人: PAMELA I CLARK
• 金额: $ 58.1万
• 依托单位: UNIV OF MARYLAND, COLLEGE PARK
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-18 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8590423
• 关键词: Bioinformatics; Biological Markers; Biometry; Collaborations; Consultations; Data; Data Analyses; Data Collection; Data Quality; Databases; Development; Electroencephalography; Elements; Ensure; Epidemiology; Goals; Housing; Human Resources; Individual; Institutes; Lead; Maryland; Measures; Modeling; Play; Postdoctoral Fellow; Protocols documentation; Quality Control; Questionnaires; Recording of previous events; Regulation; Reporting; Research; Research Design; Research Infrastructure; Research Personnel; Research Project Grants; Resources; Risk; Role; Sample Size; Secure; Services; Statistical Data Interpretation; Techniques; Time; Tobacco; Tobacco Control Research; Training; Training Support; Universities; Work; base; college; computer science; computing resources; cost; cost effective; data management; data sharing; database design; design; doctoral student; experience; metagenome; programs; public health research; response; statistics

The Data Management & Statistical Core, "Data Core C", will be a key element serving the FDA CTP by ensuring the integrity of data collection, storage, and transfer among the Research Projects, Cores, and the Developmental/Pilot Research Projects. The overarching goal of the Data Core C is to cost-effectively support the Center research by producing the highest quality data and applying state-of-the-art data analysis and statistical expertise to inform tobacco product regulation. Specifically, it provides the Center investigators with support for procedural consistency and data quality control, data management, database design and development, biostatistical expertise for study design and data analysis, bioinformatics support, and data interpretation and reporting. The Co-Directors, Dr. Lee, UMCP Biostatistics and Risk Analysis Center (BRAC), and Dr. Varshney, UMCP Institute for Advanced Computer Sciences, have experience successfully managing large teams of statisticians and database managers to support multiple, simultaneously-ongoing public health research projects. Data Core C will also play an important role in developing the proposals for the Developmental/Pilot and time-sensitive research component by providing post-doctoral students in Training Core D and other investigators with cost effective statistical resources and support for data analysis in individual research projects. Examples of services include statistical consultation and guidance on issues such as study design, sample size calculations, data analysis plans, statistical analysis techniques, and interpretation and reporting of results. The Core will design, develop, populate, and administer an interactive, secure, privilege-based database that will house all Center data, including subject recruitment, questionnaire and demographic data, HPHC exposure and biomarker levels, tobacco product HPHC emissions and content levels, EEG measures associated with product acceptability, and bacterial metagenome data. All data will undergo a consistent, rigorous cleaning protocol, conducted by the Core in close collaboration with the Research Project PI, prior to approved entry into the Center database.

数据管理和统计核心，“数据核心C”，将是服务于FDA CTP的关键要素， 确保数据收集、存储和研究项目、核心和 开发/试点研究项目。Data Core C的首要目标是经济高效地 通过产生最高质量的数据和应用最先进的数据分析来支持中心的研究 和统计专业知识，为烟草产品监管提供信息。具体而言，它提供了中心 支持程序一致性和数据质量控制、数据管理、数据库的研究者 设计和开发，研究设计和数据分析的生物统计学专业知识，生物信息学支持， 以及数据解释和报告。UMCP生物统计学和风险分析联席总监Lee博士 中心（BRAC）和Varshney博士，UMCP高级计算机科学研究所，有经验 成功管理大型统计人员和数据库管理员团队， 正在进行的公共卫生研究项目。数据核心C也将在以下方面发挥重要作用： 制定发展/试点和时间敏感的研究组成部分的建议， 培训核心D的博士后学生和其他具有成本效益的统计资源的研究人员， 支持个别研究项目的数据分析。服务示例包括统计咨询 和指导问题，如研究设计，样本量计算，数据分析计划， 分析技术，以及结果的解释和报告。核心将设计，开发，填充， 管理一个交互式、安全、基于数据库的数据库，该数据库将存储所有中心数据，包括受试者 招募、问卷调查和人口统计学数据、HPHC暴露和生物标志物水平、烟草制品 HPHC排放和含量水平，与产品可接受性相关的EEG测量，以及细菌 宏基因组数据。所有数据都将经过一致、严格的清理方案，由核心在 在批准进入中心数据库之前，与研究项目PI密切合作。