Evaluating bias due to data linkage error in electronic healthcare records.
Katie Harron, Angie Wade, Ruth Gilbert, Berit Muller-Pebody, Harvey Goldstein.
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.