Core C: Data Management and Analysis

核心C：数据管理与分析

• 批准号: 10222409
• 负责人: Soledad A Fernandez
• 金额: $ 58.34万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-18 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10222409
• 关键词: Address; Biological; Biological Assay; COVID-19 pandemic; Clinical Trials Data Monitoring Committees; Code; Data; Data Analyses; Data Collection; Data Reporting; Development; Ensure; Epidemiology; Factor Analysis; Genetic Transcription; Goals; Human Resources; Immunologics; Knowledge; Laboratories; Lead; Maintenance; Measurement; Methodology; Methods; Observational Study; Outcome Measure; Participant; Phase; Procedures; Process; Psychometrics; Quality Control; Reporting; Reproducibility; Research; Research Design; Research Personnel; Resources; Sample Size; Science; Secure; Security; Serologic tests; Services; Sociology; Standardization; Statistical Data Interpretation; Statistical Models; Statistical sensitivity; Surveys; Testing; Visual; Visualization; Work; base; cognitive interview; complex data; coronavirus disease; data integration; data integrity; data management; data quality; design; improved outcome; meetings; primary outcome; repository

Project Summary – Core C The principal objective of the Data Management and Analysis Core (Core C) is to provide project investigators a centralized resource for quantitative expertise. Statistical and methodological issues will be addressed at all stages: from the initial design of laboratory assays and observational studies to the maintenance of data quality, to the analysis of complex data. In support of this objective, Specific Aims of Core C are: Aim 1. Collaborate with project investigators to align study design and analysis with research questions. Core C will assist researchers at the design stage by: formulating biological, immunological, epidemiological, and sociological questions as testable statistical hypotheses; devising efficient study designs and matching these with appropriate statistical models; determining sample sizes necessary to ensure high power while controlling Type I error. Survey development will utilize appropriate methods (e.g., cognitive interviewing or pilot testing) to ensure that they are psychometrically appropriate. Core personnel will ensure that: the chosen designs and surveys avoid biases and decrease measurement errors; the selected primary outcome measures will answer specific research questions; and the statistical analysis plans will use data to answer the research questions under realistic and transparent assumptions. Aim 2. Provide services and support for data collection and management to ensure: integrity, security and accessibility; processing and quality control; sharing across projects; and creating a centralized repository of all survey instruments, analysis procedures and results. To promote data integration across projects, Core C will create a centralized repository of all data that will be secure and accessible to investigators. Quantitative data will be cleaned, merged and de-identified. Missing and outlying data reports will be provided to the investigators. Developed surveys will be content validated and further validated by psychometric analyses as appropriate. Qualitative data will follow a rigorous transcription and coding process. To ensure high data integrity and availability for statistical modeling, Core C will implement rigorous quality control and processing procedures, using approaches such as normalization, filtering, and visualization. Aim 3. Provide services and support for data analysis and interpretation phases of all projects. Core C services will include: (i) formal hypothesis tests for laboratory, epidemiological, and qualitative data that ensure strong conclusions, (ii) exploratory analyses that lead to new or refined hypotheses, (iii) statistical modeling and sensitivity analyses of complex data, and (iv) visual displays that clarify conclusions. In accordance with critical principles of rigor and reproducibility in science, Core C biostatisticians will work closely with investigators to develop comprehensive analysis plans that distinguish between formal testing and exploratory analyses. Qualitative data (e.g., cognitive interviews) will be analyzed following a standardized coding process.

项目总结 – 核心 C 数据管理和分析核心（核心 C）的主要目标是为项目研究人员提供 定量专业知识的集中资源。统计和方法问题将得到解决 阶段：从实验室分析和观察研究的初始设计到数据质量的维护， 到复杂数据的分析。为了支持这一目标，核心 C 的具体目标是： 目标 1. 协作 与项目研究人员合作，使研究设计和分析与研究问题保持一致。核心C将协助 研究人员在设计阶段通过：制定生物学、免疫学、流行病学和社会学 问题作为可检验的统计假设；设计有效的研究设计并将其与适当的相匹配 统计模型；确定确保高功效所需的样本量，同时控制 I 类错误。 调查开发将利用适当的方法（例如认知访谈或试点测试）来确保 它们在心理测量上是适当的。核心人员将确保： 所选择的设计和调查避免 偏差并减少测量误差；选定的主要结果指标将回答具体的研究 问题;统计分析计划将使用数据来回答现实和现实的研究问题 透明的假设。目标 2. 为数据收集和管理提供服务和支持 确保：完整性、安全性和可访问性；加工和质量控制；跨项目共享； 创建所有调查工具、分析程序和结果的集中存储库。到 促进跨项目的数据集成，Core C 将创建一个安全的所有数据的集中存储库 并可供调查人员使用。定量数据将被清理、合并和去识别化。失踪和偏远 数据报告将提供给调查人员。开发的调查将进行内容验证并进一步 适当时通过心理测量分析进行验证。定性数据将遵循严格的转录和编码 过程。为了确保统计建模的高数据完整性和可用性，Core C 将实施严格的 使用标准化、过滤和可视化等方法进行质量控制和处理程序。 目标3.为所有项目的数据分析和解释阶段提供服务和支持。核心C 服务将包括： (i) 对实验室、流行病学和定性数据进行正式的假设检验，以确保 强有力的结论，(ii) 探索性分析，得出新的或改进的假设，(iii) 统计模型和 复杂数据的敏感性分析，以及 (iv) 澄清结论的视觉显示。根据关键 遵循科学严谨性和可重复性的原则，核心 C 生物统计学家将与研究人员密切合作， 制定区分正式测试和探索性分析的全面分析计划。 定性数据（例如认知访谈）将按照标准化编码流程进行分析。