TY - JOUR AU - Harron, Katie AU - Wade, Angie AU - Gilbert, Ruth AU - Muller-Pebody, Berit AU - Goldstein, Harvey PY - 2014 DA - 2014/03/05 TI - Evaluating bias due to data linkage error in electronic healthcare records JO - BMC Medical Research Methodology SP - 36 VL - 14 IS - 1 AB - 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. SN - 1471-2288 UR - https://doi.org/10.1186/1471-2288-14-36 DO - 10.1186/1471-2288-14-36 ID - Harron2014 ER -