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

<p dir="ltr"><b>Objectives</b></p><p dir="ltr">Within-subject biological variation (CV i ) 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 CV i were directly compared.</p><p dir="ltr"><b>Methods</b></p><p dir="ltr">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, CV i the within-subject coefficient of variation for non-pathological values, f the fraction of pathological values, and e the relative increase in CV i 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.</p><p dir="ltr"><b>Results</b></p><p dir="ltr">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 CV i , followed by the result ratio method with Tukey’s exclusion method for 78/128 permutations. The median method grossly under-estimated the CV i . The modified result ratio with Tukey’s rule performed best overall with 114/128 permutations within allowable error.</p><p dir="ltr"><b>Conclusions</b></p><p dir="ltr">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 CV i 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.</p>