Standards and tools for data monitoring in observational studies

观察性研究中数据监测的标准和工具

• 批准号: 315057723
• 负责人: Professor Dr. Martin Dugas
• 金额: --
• 依托单位: Leibniz-Institut für Präventionsforschung und Epidemiologie - BIPS GmbH
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/315057723?language=en
• 关键词: Standards; tools; data; monitoring; observational

The first funding period has stimulated an extensive exchange between representatives of major German cohort studies on the conduct and optimization of data quality assessments. The ground has been laid for the first data quality assessment approach in epidemiology linking a data quality framework with generic statistical implementations. This comprised (1) an assessment of the German TMF guideline on data quality, which resulted in a revised data quality concept; (2) statistical implementations in R and Stata; (3) the extension of a web application for data quality assessments. Several public workshops were conducted and a web portal was established to disseminate project results. Work packages in the second funding period target expanded analysis tools, cross-disciplinary standards and guidance materials to foster the sustainable and widespread use of our developments on harmonized data quality analyses in cohort studies and observational health research. The first objective improves the scope and methodology of data quality assessments. We will improve transdisciplinary exchange where we exploit the fact that epidemiology and the social sciences share in common many methods. GESIS will contribute their expertise to reveal important yet uncovered issues in our data quality concept such as adverse response behaviours. Vice versa, no comparable data quality framework exists in the social sciences. Our data quality concept may be of substantial use for observational studies in this field. Second, we will derive methods for the automated grading of data quality issues with a focus on observer-, device- and centre-effects as well as time-trends. The second objective targets the FAIRness – findability, accessibility, interoperability, and reusability - of data quality assessments. Our first goal within this objective is to overcome the limited transferability of data quality-related metadata between studies. Harmonized metadata standards will be developed in cooperation with the worlds’ largest repository on medical forms (MDM) along import and export functionalities to increase their reusability. The second goal is to ease the application of our tools. Since many persons responsible for data quality assessments are non-statisticians, interactive front-ends will enable report generation and analysis without programming skills. The third goal is to set up an e-learning environment with an open online course to teach and train digital skills in the field of data quality assessments. Our fourth goal is to improve the visibility of results from data quality assessments in scientific papers by developing a reporting guideline in cooperation with the STRATOS initiative and the EQUATOR network. Our integration in various national and international networks will increase the outreach of our project results.

第一个供资期促使德国主要队列研究的代表就数据质量评估的进行和优化问题进行了广泛交流。已经为流行病学领域的第一个数据质量评估方法奠定了基础，该方法将数据质量框架与通用统计实施联系起来。这包括(1)对德国TMF数据质量指南的评估，这导致了修订的数据质量概念；(2) R和Stata的统计实现；(3)扩展数据质量评估的网络应用程序。举办了几次公开讲习班，并设立了一个门户网站来传播项目成果。第二个供资期的一揽子工作目标是扩大分析工具、跨学科标准和指导材料，以促进可持续和广泛地利用我们在队列研究和观察性健康研究中统一数据质量分析方面的发展。第一个目标是改进数据质量评估的范围和方法。我们将加强跨学科交流，利用流行病学和社会科学在许多方法上的共同之处。GESIS将贡献他们的专业知识，揭示我们的数据质量概念中重要但尚未发现的问题，如不良反应行为。反之亦然，在社会科学中不存在可比的数据质量框架。我们的数据质量概念可能对该领域的观察性研究有很大的用处。其次，我们将推导出数据质量问题的自动分级方法，重点关注观察者、设备和中心效应以及时间趋势。第二个目标是数据质量评估的公平性——可查找性、可访问性、互操作性和可重用性。我们在这个目标中的第一个目标是克服研究之间数据质量相关元数据的有限可移植性。将与世界上最大的医疗表格存储库（MDM）合作，制定统一的元数据标准，以及导入和导出功能，以提高其可重用性。第二个目标是简化我们工具的应用程序。由于许多负责数据质量评估的人员不是统计人员，交互式前端将使无需编程技能即可生成和分析报告。第三个目标是建立一个电子学习环境，提供开放的在线课程，以教授和培训数据质量评估领域的数字技能。我们的第四个目标是通过与STRATOS倡议和EQUATOR网络合作制定报告准则，提高科学论文中数据质量评估结果的可见性。我们在各种国家和国际网络中的整合将增加我们项目成果的外延性。