Iterative Outlier Removal: A Method for Identifying Outliers in Laboratory Recalibration Studies

BACKGROUND Extreme values that arise for any reason, including those through nonlaboratory measurement procedure-related processes (inadequate mixing, evaporation, mislabeling), lead to outliers and inflate errors in recalibration studies. We present an approach termed iterative outlier removal (IOR) for identifying such outliers. METHODS We previously identified substantial laboratory drift in uric acid measurements in the Atherosclerosis Risk in Communities (ARIC) Study over time. Serum uric acid was originally measured in 1990–1992 on a Coulter DACOS instrument using an uricase-based measurement procedure. To recalibrate previous measured concentrations to a newer enzymatic colorimetric measurement procedure, uric acid was remeasured in 200 participants from stored plasma in 2011–2013 on a Beckman Olympus 480 autoanalyzer. To conduct IOR, we excluded data points >3 SDs from the mean difference. We continued this process using the resulting data until no outliers remained. RESULTS IOR detected more outliers and yielded greater precision in simulation. The original mean difference (SD) in uric acid was 1.25 (0.62) mg/dL. After 4 iterations, 9 outliers were excluded, and the mean difference (SD) was 1.23 (0.45) mg/dL. Conducting only one round of outlier removal (standard approach) would have excluded 4 outliers [mean difference (SD) = 1.22 (0.51) mg/dL]. Applying the recalibration (derived from Deming regression) from each approach to the original measurements, the prevalence of hyperuricemia (>7 mg/dL) was 28.5% before IOR and 8.5% after IOR. CONCLUSIONS IOR is a useful method for removal of extreme outliers irrelevant to recalibrating laboratory measurements, and identifies more extraneous outliers than the standard approach.