Smith F A, Kroft S H
Department of Pathology, Northwestern University Medical School, Chicago, Illinois, USA.
Am J Clin Pathol. 1996 Jan;105(1):44-51. doi: 10.1093/ajcp/105.1.44.
The idea of using patient samples as the basis for control procedures elicits a continuing fascination among laboratorians, particularly in the current environment of cost restriction. Average of normals (AON) procedures, although little used, have been carefully investigated at the theoretical level. The performance characteristics of Bull's algorithm have not been thoroughly delineated, however, despite its widespread use. The authors have generalized Bull's algorithm to use variably sized batches of patient samples and a range of exponential factors. For any given batch size, there is an optimal exponential factor to maximize the overall power of error detection. The optimized exponentially adjusted moving mean (EAMM) procedure, a variant of AON and Bull's algorithm, outperforms both parent procedures. As with any AON procedure, EAMM is most useful when the ratio of population variability to analytical variability (standard deviation ratio, SDR) is low.
将患者样本用作控制程序基础的想法一直吸引着实验室工作人员，尤其是在当前成本受限的环境下。“正常均值”（AON）程序虽然很少使用，但已在理论层面进行了深入研究。然而，尽管布尔算法被广泛使用，但其性能特征尚未得到全面描述。作者对布尔算法进行了推广，使其能够使用大小可变的患者样本批次和一系列指数因子。对于任何给定的批次大小，都有一个最佳指数因子，可使错误检测的整体效能最大化。优化后的指数调整移动均值（EAMM）程序是AON和布尔算法的一种变体，其性能优于这两种原始程序。与任何AON程序一样，当总体变异性与分析变异性之比（标准差比，SDR）较低时，EAMM最为有用。
