Reference Laboratory, Autobio Diagnostics Co., Ltd, 199 Fifteenth Street, National Eco &Tech Development Zone, Zhengzhou, 450016, Henan, China.
Department of Laboratory Medicine, Affiliated Hospital of Nantong University, Nantong, 226001, Jiangsu, China.
Anal Bioanal Chem. 2024 Aug;416(19):4427-4434. doi: 10.1007/s00216-024-05380-z. Epub 2024 Jun 19.
The measurement uncertainty is a crucial quantitative parameter for assessing the reliability of the result. The study aimed to propose a new budget for uncertainty evaluation of a reference measurement procedure for the determination of total testosterone in human serum. The adaptive Monte Carlo method (aMCM) was used for the propagation of probability distributions assigned to various input quantities to determine the uncertainty of the testosterone concentration. The basic principles of the propagation and the statistical analysis were described based on the experimental results of the quality control serum sample. The analysis of the number of Monte Carlo trials was discussed. The procedure of validation of the GUM uncertainty framework using the aMCM was also provided. The number of Monte Carlo trials was 2.974 × 10 when the results had stabilized. The total testosterone concentration was 16.02 nmol/L, and the standard uncertainty was 0.30 nmol/L. The coverage interval at coverage probability of 95% was 15.45 to 16.62 nmol/L, while the probability distribution for testosterone concentration was approximately described by a Gaussian distribution. The validation of results was not passed as the expanded uncertainty result obtained by the aMCM was slightly lower, about 7%, than that by the GUM uncertainty framework with consistent results of the concentration.
测量不确定度是评估结果可靠性的一个关键定量参数。本研究旨在为评估人血清总睾酮参考测量程序的不确定度提出一个新的预算。自适应蒙特卡罗方法(aMCM)用于传播分配给各种输入量的概率分布,以确定睾酮浓度的不确定度。基于质控血清样本的实验结果,描述了传播和统计分析的基本原理。讨论了蒙特卡罗试验次数的分析。还提供了使用 aMCM 验证 GUM 不确定度框架的程序。当结果稳定时,蒙特卡罗试验次数为 2.974×10。总睾酮浓度为 16.02 nmol/L,标准不确定度为 0.30 nmol/L。在 95%的覆盖概率下,覆盖区间为 15.45 至 16.62 nmol/L,而睾酮浓度的概率分布近似由高斯分布描述。由于通过 aMCM 获得的扩展不确定度结果略低(约 7%),与浓度的 GUM 不确定度框架的结果不一致,因此验证结果未通过。