Comparison of two (data mining) indirect approaches for between-subject biological variation determination

Between-subject biological variation (CV_g) is an important parameter in several aspects of laboratory practice, including setting of analytical performance specification, delta checks and calculation of index of individuality. Using simulations, we compare the performance of two indirect (data mining) approaches for deriving CV_g.

The expected mean squares (EMS) method was compared against that proposed by Harris and Fraser. Using numerical simulations, d the percentage difference in the mean between the non-pathological and pathological populations, CV_i the within-subject coefficient of variation of the non-pathological distribution, f the fraction of pathological values, and e the relative increase in CV_i of the pathological distribution were varied for a total of 320 conditions to examine the impact on the relative fractional of error of the recovered CV_g compared to the true value.

Comparing the two methods, the EMS and Harris and Fraser's approaches yielded similar performance of 158 conditions and 157 conditions within ± 0.20 fractional error of the true underlying CV_g, for the normal and lognormal distributions, respectively. It is observed that both EMS and Harris and Fraser's method performed better using the calculated CV_i rather than the actual ('presumptive') CV_i. The number of conditions within 0.20 fractional error of the true underlying CV_g did not differ significantly between the normal and lognormal distributions. The estimation of CV_g improved with decreasing values of f, d and CV_iCV_g.

The two statistical approaches included in this study showed reliable performance under the simulation conditions examined.