Institute for Social Medicine and Health Systems Research (ISMHSR), Medical Faculty, Otto von Guericke University Magdeburg, 39120 Magdeburg, Germany.
Department of Social Work, Health and Media, Magdeburg-Stendal University of Applied Sciences, 39114 Magdeburg, Germany.
Int J Environ Res Public Health. 2020 Oct 27;17(21):7852. doi: 10.3390/ijerph17217852.
The data linkage of different data sources for research purposes is being increasingly used in recent years. However, generally accepted methodological guidance is missing. The aim of this article is to provide methodological guidelines and recommendations for research projects that have been consented to across different German research societies. Another aim is to endow readers with a checklist for the critical appraisal of research proposals and articles. This Good Practice Data Linkage (GPD) was already published in German in 2019, but the aspects mentioned can easily be transferred to an international context, especially for other European Union (EU) member states. Therefore, it is now also published in English. Since 2016, an expert panel of members of different German scientific societies have worked together and developed seven guidelines with a total of 27 practical recommendations. These recommendations include (1) the research objectives, research questions, data sources, and resources; (2) the data infrastructure and data flow; (3) data protection; (4) ethics; (5) the key variables and linkage methods; (6) data validation/quality assurance; and (7) the long-term use of data for questions still to be determined. The authors provide a rationale for each recommendation. Future revisions will include new developments in science and updates of data privacy regulations.
近年来,为研究目的对不同数据源进行数据链接的做法越来越多。然而,目前缺乏被普遍认可的方法学指导。本文的目的是为已获得多个德国研究学会同意的研究项目提供方法学指南和建议。另一个目的是为读者提供一份用于批判性评估研究提案和文章的核对清单。本《良好实践数据链接(GPD)》已于 2019 年以德文出版,但其中提到的方面很容易被转移到国际背景下,特别是对于其他欧盟(EU)成员国。因此,现在也以英文出版。自 2016 年以来,不同德国科学学会的成员专家小组共同努力,制定了 7 条准则,共包含 27 条实用建议。这些建议包括:(1)研究目标、研究问题、数据源和资源;(2)数据基础设施和数据流;(3)数据保护;(4)伦理学;(5)关键变量和链接方法;(6)数据验证/质量保证;以及(7)为尚未确定的问题长期使用数据。作者为每条建议提供了基本原理。未来的修订将包括科学新进展和数据隐私法规的更新。