Data linkage in pharmacoepidemiology: A call for rigorous evaluation and reporting. Issue 1 (17th November 2019)

• Record Type: Journal Article
• Authors: Pratt, Nicole L.; Mack, Christina D.; Meyer, Anne Marie; Davis, Kourtney J.; Hammill, Bradley G.; Hampp, Christian; Setoguchi, Soko; Raman, Sudha R.; Chun, Danielle S.; Stürmer, Til; Lund, Jennifer L.
• Is Part Of: Pharmacoepidemiology and drug safety. Volume 29:Issue 1(2020)
• Page Start: 9
• Page End: 17
• Publication Date: 2019-11-17
• Subjects: data linkage -- data quality -- pharmacoepidemiology -- reporting -- research quality; Pharmacoepidemiology -- Periodicals; Chemotherapy -- Periodicals; Epidemiology -- Periodicals; 615.705
• DOI: 10.1002/pds.4924
• Languages: English
• ISSNs: 1053-8569

Abstract

Abstract: Purpose: The purpose of this paper is to provide guidance on the evaluation of data linkage quality through the development of a checklist for reporting key elements of the linkage process. Methods: Responding to a call for manuscripts from the International Society for Pharmacoepidemiology (ISPE), a working group including international representation from the academic, industry, and contract research, and regulatory sectors was formed to develop a checklist for evaluation of data linkage performance and reporting data linkage specifically for pharmacoepidemiologic research. This checklist expands on the reporting of studies conducted using observational routinely collected health data specific to pharmacoepidemiology (RECORD‐PE) guidelines. Results: A key aspect of data linkage evaluation for pharmacoepidemiology is to articulate how a linkage process was performed and its accuracy in terms of validation and verification of the resulting linked data. This study generates a checklist, which covers domains including data sources, linkage variables, linkage methods, linkage results, and linkage evaluation. For each domain, specific recommendations provide a clear and transparent assessment of the linkage process. Conclusions: Linking data sources can help to enrich analytic databases to more accurately define study populations, enable adjustment for confounding, and improve the capture of health outcomes. Clear and transparent reporting of data linkage processes will help to increase confidence in the evidence generated from these data by allowing researchers and end users to critically assess the potential for bias owing to the data linkage process.