A guide to evaluating linkage quality for the analysis of linked data. (7th September 2017)

• Record Type: Journal Article
• Authors: Harron, Katie L; Doidge, James C; Knight, Hannah E; Gilbert, Ruth E; Goldstein, Harvey; Cromwell, David A; van der Meulen, Jan H
• Is Part Of: International journal of epidemiology. Volume 46:Number 5(2017)
• Page Start: 1699
• Page End: 1710
• Publication Date: 2017-09-07
• Subjects: Record linkage -- linkage error -- bias -- hospital records -- data accuracy -- sensitivity and specificity -- selection bias -- data linkage -- administrative data; Epidemiology -- Periodicals; 614.4
• DOI: 10.1093/ije/dyx177
• Languages: English
• ISSNs: 0300-5771

Abstract

Abstract: Linked datasets are an important resource for epidemiological and clinical studies, but linkage error can lead to biased results. For data security reasons, linkage of personal identifiers is often performed by a third party, making it difficult for researchers to assess the quality of the linked dataset in the context of specific research questions. This is compounded by a lack of guidance on how to determine the potential impact of linkage error. We describe how linkage quality can be evaluated and provide widely applicable guidance for both data providers and researchers. Using an illustrative example of a linked dataset of maternal and baby hospital records, we demonstrate three approaches for evaluating linkage quality: applying the linkage algorithm to a subset of gold standard data to quantify linkage error; comparing characteristics of linked and unlinked data to identify potential sources of bias; and evaluating the sensitivity of results to changes in the linkage procedure. These approaches can inform our understanding of the potential impact of linkage error and provide an opportunity to select the most appropriate linkage procedure for a specific analysis. Evaluating linkage quality in this way will improve the quality and transparency of epidemiological and clinical research using linked data.