Compton P, Cole B, Stuart M, Egan G
Ann Clin Biochem. 1984 Nov;21 ( Pt 6):498-503. doi: 10.1177/000456328402100611.
The calculation of within-laboratory imprecision in quality-assessment (QA) programmes normally involves combining data from different analyte concentrations to calculate an average standard deviation (SD) or coefficient of variation. However, for immunoassay neither of these parameters is concentration independent. This paper describes a method of calculating within-laboratory imprecision in QA programmes by assuming a linear relationship between SD and analyte concentration. This method is used in programmes conducted by the Australian Joint Working Party for Quality Control in Immunoassay to calculate imprecision at the limits of the reference range. Results from these programmes show that this method better represents the differences in imprecision between analytes, methods and laboratories than the calculation of a single imprecision parameter. The method is trivial for a computer and its robustness has been validated by Monte Carlo simulation. It is suggested that major differences in laboratory performance between different QA programmes may be due to inappropriate calculation of single imprecision parameters.
质量评估(QA)程序中实验室内不精密度的计算通常涉及合并来自不同分析物浓度的数据,以计算平均标准差(SD)或变异系数。然而,对于免疫测定而言,这些参数均不独立于浓度。本文描述了一种在QA程序中计算实验室内不精密度的方法,该方法假定标准差与分析物浓度之间呈线性关系。此方法用于澳大利亚免疫测定质量控制联合工作小组开展的程序中,以计算参考范围极限处的不精密度。这些程序的结果表明,与计算单个不精密度参数相比,该方法能更好地体现分析物、方法和实验室之间不精密度的差异。该方法在计算机上很容易实现,并且其稳健性已通过蒙特卡洛模拟得到验证。有人认为,不同QA程序之间实验室性能的重大差异可能是由于单个不精密度参数计算不当所致。