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）的主要目标是为项目调查人员提供 定量专业知识的集中资源。统计和方法论问题将完全解决 阶段：从实验室评估和观察性研究的最初设计到维持数据质量， 分析复杂数据。为了支持这一目标，核心C的具体目标是：目标1。 与项目调查人员一起，将研究设计和分析与研究问题相结合。核心将有助于 设计阶段的研究人员通过：制定生物学，免疫学，流行病学和社会学 作为可检验的统计假设的问题；设计有效的研究设计并与适当的 统计模型；确定所需的样本量，以确保在控制I型错误时高功率。 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 它们在心理上是适当的。核心人员将确保：选择的设计和调查避免 偏见并减少测量误差；选定的主要结果指标将回答特定的研究 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，安全性和可访问性；处理和质量控制；跨项目共享； 并创建所有调查工具，分析程序和结果的集中存储库。到 促进跨项目的数据集成，核心C将创建一个将是安全的所有数据的集中存储库 并且调查人员可以访问。定量数据将被清洁，合并和去识别。缺失和外部 数据报告将提供给调查人员。开发的调查将得到验证，进一步验证 通过心理测量分析验证。定性数据将遵循严格的转录和编码 过程。为了确保高数据完整性和用于统计建模的可用性，核心C将实施严格 质量控制和处理程序，使用诸如归一化，过滤和可视化之类的方法。 目标3。为所有项目的数据分析和解释阶段提供服务和支持。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测试，以确保 强烈的结论，（ii）导致新假设或精制假设的探索性分析，（iii）统计建模和 复杂数据的敏感性分析以及（iv）的视觉显示，以阐明结论。按照批判性 科学中严格和可重复性的原则，核心生物统计学家将与研究人员紧密合作 制定综合分析计划，以区分正式的测试和探索性分析。 定性数据（例如，认知访谈）将在标准化的编码过程后进行分析。