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.

项目摘要-核心C 数据管理和分析核心(核心C)的主要目标是向项目调查员提供 提供量化专业知识的集中资源。统计和方法方面的问题将完全得到解决 阶段：从实验室分析和观察性研究的初始设计到数据质量的维护， 对复杂数据的分析。为支持这一目标，核心C的具体目标是：目标1.合作 与项目调查人员合作，使研究设计和分析与研究问题保持一致。核心C将提供帮助 处于设计阶段的研究人员：制定生物学、免疫学、流行病学和社会学 将问题作为可检验的统计假设；设计有效的研究设计并将这些设计与适当的 统计模型；确定在控制类型I错误的同时确保高功率所需的样本量。 调查的制定将利用适当的方法(例如，认知访谈或试点测试)来确保 它们在心理测量学上是合适的。核心人员将确保：选定的设计和调查避免 偏向和减少测量误差；选定的主要结果衡量标准将回答具体研究 问题；统计分析计划将使用数据来回答现实和 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、安全性和可获得性；加工和质量控制；跨项目共享； 并建立所有调查仪器、分析程序和结果的中央储存库。至 促进跨项目的数据集成，Core C将为所有安全的数据创建一个集中的存储库 并且调查人员可以接触到。量化数据将被清理、合并和去标识。失踪和离群索居 将向调查人员提供数据报告。已开发的调查将进行内容验证并进一步 通过适当的心理测量学分析进行验证。定性数据将遵循严格的转录和编码 进程。为了确保统计建模的高度数据完整性和可用性，Core C将严格执行 质量控制和处理程序，使用标准化、过滤和可视化等方法。 目标3.为所有项目的数据分析和解释阶段提供服务和支助。核心C 服务将包括：(I)实验室、流行病学和定性数据的正式假设检验，以确保 强有力的结论，(2)导致新的或改进的假设的探索性分析，(3)统计建模和 对复杂数据的敏感性分析，以及(Iv)澄清结论的直观显示。根据关键字 根据科学中严谨和可重复性的原则，Core C生物统计学家将与研究人员密切合作 制定全面的分析计划，区分正式测试和探索性分析。 定性数据(例如，认知访谈)将按照标准化的编码过程进行分析。