Main Article Content
Increasingly in pharmacoepidemiology, linking is required to enrich analytic data to more accurately define study populations, enable adjustment for confounding, and improve capture of health outcomes. When creating such novel linked datasets, researchers should consider their suitability to meet research objectives, assess source data completeness and population coverage, and ensure well-defined data governance standards and protections exist. Additionally, while the RECORD-PE guidelines assist in the reporting of studies using observational health data specific to pharmacoepidemiology, they do not address the unique requirements for transparent evaluation and reporting of the data linkage process.
Objectives and Approach
We aimed to 1) provide guidance on data linkage appropriateness and feasibility to plan purposeful and sustainable new linkages that advance pharmacoepidemiological research and 2) generate a checklist with specific recommendations to assist researchers in providing clear and transparent assessment of the linkage process. To develop these guidelines, a working group comprised of members of the International Society of harmacoepidemiology was formed. Recommendations were open for comment by Society members and endorsed by the Society.
Guidance for feasibility assessment was categorized into five domains: (1) research objectives and justification; (2) data quality and completeness; (3) the linkage process; (4) data ownership and governance; and (5) overall value added by linkage. A checklist for evaluation and reporting of data-linkage processes covered five domains including; (1) data sources; (2) linkage variables; (3) linkage methods; (4) linkage results; and (5) linkage evaluation, including validation and verification of the resulting linked data.
Our guidelines for data linkage feasibility assessment and reporting can be used to inform the design of sustainable linked data resources and for transparent communication of linkage processes. Together, these guidelines will help various stakeholders to critically assess the potential for bias in research based on linked data and help generate actionable evidence.
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