Open AccessDevelopment and validation of reporting guidelines for studies involving data linkage
Author(s) -
Bohensky Megan A.,
Jolley Damien,
Sundararajan Vijaya,
Evans Sue,
Ibrahim Joseph,
Brand Caroline
Publication year - 2011
Publication title -
australian and new zealand journal of public health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.946
H-Index - 76
eISSN - 1753-6405
pISSN - 1326-0200
DOI - 10.1111/j.1753-6405.2011.00741.x
Subject(s) - linkage (software) , data quality , computer science , quality (philosophy) , delphi , delphi method , process (computing) , data science , data mining , psychology , information retrieval , engineering , metric (unit) , biochemistry , chemistry , operations management , philosophy , epistemology , artificial intelligence , gene , operating system
Objective:Data or record linkage is commonly used to combine existing data sets for the purpose of creating more comprehensive information to conduct research. Linked data may create additional concerns about error if cases are not linked accurately. It is important that factors compromising the quality of studies using linked data be reported in a clear and consistent way that allows readers and researchers to accurately appraise the results. The aim of this study was to develop and test reporting guidelines for evaluating the methodological quality of studies using linked data.Method:The development process included a literature review, a Delphi process and a validation process. Participants in the process were all Australian and included biostatisticians, epidemiologists, registry administrators, academic clinicians and a peer‐reviewed journal editor.Results:The final guidelines included four domains and 14 reporting items. These included: data sources (six items), research selected variables (four items), linkage technology and data analysis (three items), and ethics, privacy and data security (one item).Conclusion:This study is the first to develop guidelines for appraising the quality of reported data linkage studies.Implications:These guidelines will assist authors to report their results in a consistent, high‐quality manner. They will also assist readers to interpret the quality of results derived from data linkage studies.