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

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.

Linking electronic health data in pharmacoepidemiology: Appropriateness and feasibility

“…When researchers use data from a single interventional trial or observational study, the maximum follow‐up period may inherently be truncated by the data source and, therefore, may not be extensive enough to capture long‐term effects or rare outcomes required to answer pivotal research questions. Data linkage can offer a more comprehensive longitudinal evaluation of health status and healthcare utilization before, during, and after key healthcare events . For example, randomized controlled trial data linked with United Kingdom (U.K.) administrative health data extended follow‐up time from five to fifteen years; this linked data enabled researchers to determine long‐term cardiovascular outcomes among patients treated with pravastatin .…”

Assessing the suitability of general practice electronic health records for clinical prediction model development: a data quality assessment

“…In this study we followed established guidelines for assessing data quality and the data linkage process [ 17 , 23 ]. Our assessment was based on a large sample of EHRs which provides a true representation of how data are recorded in general practice EHRs.…”

“…The same approach was used to assess the accuracy of recording of year of death. The data linkage process used to link the NPS MedicineWise EHR data set to the AOANJRR and NDI was assessed using the checklist developed by Pratt et al [ 17 ]. Assessment of external validity of EHR data The external validity of the EHR data set was assessed by comparing socio-demographics and clinical characteristics of our cohort with that of OA patients aged 45 years and over from the 2014–2015 National Health Survey (NHS) [ 18 ] carried out by the ABS.…”

“…The same approach was used to assess the accuracy of recording of year of death. The data linkage process used to link the NPS MedicineWise EHR data set to the AOANJRR and NDI was assessed using the checklist developed by Pratt et al [ 17 ].…”

The Certainty Framework for Assessing Real‐World Data in Studies of Medical Product Safety and Effectiveness

“…Numerous groups, including regulatory agencies, have published guidance and best practices on the conduct of observational studies in support of or for regulatory purposes 36–44 . Others have made clear the need for transparency in studies to the degree that they can be replicated (e.g., design diagrams with very clear documentation, code lists, covariate definitions, the assessment windows used for baseline characteristics, and risk windows) 45,46 .…”