Harron, K;
Wade, A;
Gilbert, R;
Muller-Pebody, B;
Goldstein, H;
(2014)
Evaluating bias due to data linkage error in electronic healthcare records.
BMC Med Res Methodol
, 14
(1)
, Article 36. 10.1186/1471-2288-14-36.
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Abstract
Linkage of electronic healthcare records is becoming increasingly important for research purposes. However, linkage error due to mis-recorded or missing identifiers can lead to biased results. We evaluated the impact of linkage error on estimated infection rates using two different methods for classifying links: highest-weight (HW) classification using probabilistic match weights and prior-informed imputation (PII) using match probabilities.
| Type: | Article |
|---|---|
| Title: | Evaluating bias due to data linkage error in electronic healthcare records. |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1186/1471-2288-14-36 |
| Publisher version: | http://dx.doi.org/10.1186/1471-2288-14-36 |
| Additional information: | © 2014 Harron et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. |
| Keywords: | Data linkage; Routine data; Bias; Electronic health records; Evaluation; Linkage quality; |
| 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/1422631 |
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