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Pharmacoepidemiol Drug Saf. 2020 Jan;29(1):9-17. doi: 10.1002/pds.4924. Epub 2019 Nov 17.

Data linkage in pharmacoepidemiology: A call for rigorous evaluation and reporting.

Author information

1
Quality Use of Medicines and Pharmacy Research Centre, University of South Australia, Adelaide, South Australia, Australia.
2
Real-World and Analytic Solutions, IQVIA, Research Triangle Park, Durham, NC, USA.
3
Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.
4
Global Epidemiology, Janssen R&D, Titusville, NJ, 08650.
5
Department of Population Health Sciences, School of Medicine, Duke University, Durham, NC, USA.
6
Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Rockville, Maryland.
7
Institute for Health, Health Care Policy and Aging Research, RWJ Medical School, Center for Pharmacoepidemiology and Treatment Science, Rutgers University, New Brunswick, NJ, USA.

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.

KEYWORDS:

data linkage; data quality; pharmacoepidemiology; reporting; research quality

PMID:
31736248
DOI:
10.1002/pds.4924

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