Core--Data Coordination and Statistics

核心——数据协调与统计

• 批准号: 6939098
• 负责人: Douglas W Dockery
• 金额: $ 43.73万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-04-01 至 2010-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6939098
• 关键词: air pollution; biomedical facility; data management; statistics /biometry

The primary goal of the Data Coordination and Statistical Core (DCSC) is to provide a coherent and consistent approach to field procedures and data management, with sharing of statistical methodology across all Projects and Cores. By standardizing protocols and centralizing data management, we not only insure data integrity and realize individual study goals, but we also facilitate inter-project scientific collaboration amongst Project and Core investigators. The Data Management and Coordination Component will provide centralized expertise and resources for management, quality control, quality assurance, storage and access to Project and Core data. The DCSC will provide centralized data tracking, processing, quality assurance procedures and documentation management. Finally the DCSC will ensure that data are accessible to Program investigators in a timely fashion, through a centralized, shared-access study database with regular back-up and archiving. The Environmental Statistics Component will serve as a centralized resource for understanding and implementing the statistical methods needed for the projects, including analysis of panel data and case-crossover studies, as well as expertise in nonparametric smoothing, including within the context of mixed models, repeated measures, structural equation models, and risk assessment.

数据协调和统计核心（DCSC）的主要目标是为现场程序和数据管理提供一致，一致的方法，并在所有项目和内核中共享统计方法。通过标准化协议和集中数据管理，我们不仅确保数据完整性并实现个人研究目标，而且还促进了项目和核心研究人员之间的项目间科学协作。数据管理和协调组件将为管理，质量控制，质量保证，存储以及对项目和核心数据的访问提供集中的专业知识和资源。 DCSC将提供集中的数据跟踪，处理，质量保证程序以及 文档管理。最终，DCSC将通过集中的，共享的学习数据库及时访问计划调查员的数据，并定期备份和归档。环境统计组件将作为理解和实施项目所需的统计方法的集中资源，包括对面板数据和案例分解研究的分析，以及非参数平滑的专业知识，包括在混合模型，重复测量，结构方程，结构方程模型和风险评估的背景下。