Smith F A, Kroft S H
Department of Pathology, Northwestern University Medical School, Chicago, Illinois, USA.
Am J Clin Pathol. 1997 Sep;108(3):254-68. doi: 10.1093/ajcp/108.3.254.
We recently described the performance characteristics of the exponentially adjusted moving mean (EAMM), a patient-data, moving block mean procedure, which is a generalized algorithm that unifies Bull's algorithm and the classic average of normals (AON) procedure. Herein we describe the trend EAMM (TEAMM), a continuous signal analog of the EAMM procedure related to classic trend analysis. Using computer simulation, we have compared EAMM and TEAMM over a range of biases for various sample sizes (N or equivalent smoothing factor alpha) and exponential parameters (P) under conditions of equivalent false rejection (fixed on a per patient sample basis). We found optimal pairs of N and P for each level of bias by determination of minimum mean patient samples to rejection. Overall optimal algorithms were determined through calculation of undetected lost medical utility (ULMU), a novel function that quantifies the medical damage due to analytic bias. The ULMU function was calculated based on lost test specificity in a normal population. We found that optimized TEAMM was superior to optimized EAMM for all levels of analytic bias. If these observations hold true for non-Gaussian populations, TEAMM procedures are the method of choice for detecting bias using patient samples or as an event gauge to trigger use of known-value control materials.
我们最近描述了指数调整移动均值（EAMM）的性能特征，这是一种基于患者数据的移动块均值程序，是一种统一了布尔算法和经典正态均值（AON）程序的广义算法。在此，我们描述趋势EAMM（TEAMM），它是与经典趋势分析相关的EAMM程序的连续信号模拟。通过计算机模拟，我们在等效假拒绝（固定在每个患者样本基础上）的条件下，针对各种样本量（N或等效平滑因子α）和指数参数（P），在一系列偏差范围内比较了EAMM和TEAMM。我们通过确定最小平均患者样本至拒绝来为每个偏差水平找到N和P的最佳组合。通过计算未检测到的医疗效用损失（ULMU）确定总体最佳算法，ULMU是一种量化分析偏差导致的医疗损害的新函数。ULMU函数基于正常人群中测试特异性的损失来计算。我们发现，对于所有分析偏差水平，优化后的TEAMM优于优化后的EAMM。如果这些观察结果对于非高斯人群也成立，那么TEAMM程序是使用患者样本检测偏差或作为触发使用已知值对照材料的事件指标的首选方法。
