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

ABSTRACT: 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.

SUBMITTER: Parrinello CM

PROVIDER: S-EPMC4927349 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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

Parrinello Christina M CM Grams Morgan E ME Sang Yingying Y Couper David D Wruck Lisa M LM Li Danni D Eckfeldt John H JH Selvin Elizabeth E Coresh Josef J

Clinical chemistry 20160519 7

<h4>Background</h4>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.<h4>Methods</h4>We previously identified substantial laboratory drift in uric acid measurements in the Atherosclerosis Risk in Communities (ARIC) Study over tim ...[more]

PMID: 27197675

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

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

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