Engineering Cluster, Singapore Institute of Technology, Singapore.
Flinders University International Centre for Point-of-Care Testing, South Australia, Australia.
Clin Biochem. 2022 Jul-Aug;105-106:57-63. doi: 10.1016/j.clinbiochem.2022.04.015. Epub 2022 Apr 27.
Between-subject biological variation (CV) 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.
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 the within-subject coefficient of variation of the non-pathological distribution, f the fraction of pathological values, and e the relative increase in CV 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 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, 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 rather than the actual ('presumptive') CV. The number of conditions within 0.20 fractional error of the true underlying CV did not differ significantly between the normal and lognormal distributions. The estimation of CV improved with decreasing values of f, d and CVCV.
The two statistical approaches included in this study showed reliable performance under the simulation conditions examined.
组间生物学变异（CV）是实验室实践的几个方面的重要参数，包括分析性能规范的设置、差值检查和个体指数的计算。我们使用模拟来比较两种间接（数据挖掘）方法推导 CV 的性能。
比较了预期均方（EMS）方法与 Harris 和 Fraser 提出的方法。使用数值模拟，改变非病理性和病理性人群之间的均值的百分比差异（d）、非病理性分布的个体内变异系数（CV）、病理性值的分数（f）和病理性分布的 CV 相对增加（e），共 320 种情况，以检查对恢复的 CV 与真实值的相对误差分数的影响。
比较两种方法，EMS 和 Harris 和 Fraser 的方法在正态和对数正态分布下分别有 158 种和 157 种条件的相对误差在 0.20 以内，性能相似。观察到 EMS 和 Harris 和 Fraser 的方法使用计算出的 CV 而不是实际的（“假定的”）CV 表现更好。在 0.20 以内的真实潜在 CV 的相对误差的条件数量在正态和对数正态分布之间没有显著差异。CV 的估计随着 f、d 和 CV 的值的降低而改善。
本研究中包含的两种统计方法在检查的模拟条件下表现出可靠的性能。
