Harron, KL;
Doidge, JC;
Knight, HE;
Gilbert, RE;
Goldstein, H;
Cromwell, DA;
van der Meulen, JH;
(2017)
A guide to evaluating linkage quality for the analysis of linked data.
Int J Epidemiol
, 46
(5)
pp. 1699-1710.
10.1093/ije/dyx177.
Preview |
Text
Gilbert_dyx177.pdf - Published Version Download (460kB) | Preview |
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.
| Type: | Article |
|---|---|
| Title: | A guide to evaluating linkage quality for the analysis of linked data. |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/ije/dyx177 |
| Publisher version: | https://doi.org/10.1093/ije/dyx177 |
| Language: | English |
| Additional information: | © The Author 2017. Published by Oxford University Press on behalf of the International Epidemiological Association. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Record linkage, administrative data, bias, data accuracy, data linkage, hospital records, linkage error, selection bias, sensitivity and specificity |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Population, Policy and Practice Dept |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10036697 |
Archive Staff Only
 |
View Item |
