Comparison of four indirect (data mining) approaches to derive within-subject biological variation

Rui Zhen Tan, Corey Markus, Samuel Vasikaran, Tze Ping Loh, for the APFCB Harmonization of Reference Intervals Working Group

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Objectives: Within-subject biological variation (CVi) is a fundamental aspect of laboratory medicine, from interpretation of serial results, partitioning of reference intervals and setting analytical performance specifications. Four indirect (data mining) approaches in determination of CVi were directly compared. Methods: Paired serial laboratory results for 5,000 patients was simulated using four parameters, d the percentage difference in the means between the pathological and non-pathological populations, CVi the within-subject coefficient of variation for non-pathological values, f the fraction of pathological values, and e the relative increase in CVi of the pathological distribution. These parameters resulted in a total of 128 permutations. Performance of the Expected Mean Squares method (EMS), the median method, a result ratio method with Tukey's outlier exclusion method and a modified result ratio method with Tukey's outlier exclusion were compared. Results: Within the 128 permutations examined in this study, the EMS method performed the best with 101/128 permutations falling within ±0.20 fractional error of the 'true' simulated CVi, followed by the result ratio method with Tukey's exclusion method for 78/128 permutations. The median method grossly under-estimated the CVi. The modified result ratio with Tukey's rule performed best overall with 114/128 permutations within allowable error. Conclusions: This simulation study demonstrates that with careful selection of the statistical approach the influence of outliers from pathological populations can be minimised, and it is possible to recover CVi values close to the 'true' underlying non-pathological population. This finding provides further evidence for use of routine laboratory databases in derivation of biological variation components.

Original languageEnglish
Pages (from-to)636-644
Number of pages9
JournalClinical Chemistry and Laboratory Medicine
Issue number4
Publication statusPublished - 1 Mar 2022


  • biological variation
  • data mining
  • indirect approach
  • outlier


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