Cembrowski G S, Westgard J O
Am J Clin Pathol. 1985 Mar;83(3):337-45. doi: 10.1093/ajcp/83.3.337.
Bull's algorithm has been evaluated by computer simulation studies. Varying amounts of systematic analytic error were simulated in either hemoglobin (Hgb), red blood cell count (RBC), or mean corpuscular volume (MCV) with the resulting red blood cell indices averaged in batches of 20 using Bull's algorithm. The number of average indices outside the limits of 0.97 means and 1.03 means (means = stable patient mean index) was tabulated and plotted against the size of the systematic shift, expressed in multiples of the long-term analytic standard deviation (SD). The resulting plots, called power functions, show that Bull's algorithm can detect large shifts effectively and that its power increases with increasing batch number. Shifts less than 2 SD rarely are detected. The minimum error that is detected 50% of the time after nine consecutive batches is shown below: (Formula: see text) The simulation of populations with outlying indices, e.g., neonates and oncology patients, resulted in both decreased and increased power, depending on the proportion of outliers averaged, the index averaged, and the direction of the shift.
布尔算法已通过计算机模拟研究进行了评估。在血红蛋白（Hgb）、红细胞计数（RBC）或平均红细胞体积（MCV）中模拟了不同程度的系统分析误差，并使用布尔算法对每20个批次的红细胞指数结果进行平均。将超出0.97倍均值和1.03倍均值范围（均值=稳定患者的平均指数）的平均指数数量制成表格，并针对以长期分析标准差（SD）倍数表示的系统偏移量进行绘制。所得的图称为幂函数，表明布尔算法能够有效地检测到大的偏移，并且其效能随着批次数量的增加而提高。小于2 SD的偏移很少被检测到。连续九个批次后50%的时间能检测到的最小误差如下：（公式：见原文）对具有异常指数的人群（如新生儿和肿瘤患者）进行模拟，根据平均异常值的比例、平均的指数以及偏移方向，会导致效能降低或提高。
