...
首页> 外文期刊>Clinical Chemistry: Journal of the American Association for Clinical Chemists >Setting Performance Goals and Evaluating Total Analytical Error for Diagnostic Assays
【24h】

Setting Performance Goals and Evaluating Total Analytical Error for Diagnostic Assays

机译:设定性能目标并评估诊断分析的总分析误差

获取原文
   

获取外文期刊封面封底 >>

       

摘要

Background: Total analytical error has been a useful metric both to assess laboratory assay quality and to set goals. It is often estimated by combining imprecision (SD) and average bias in the equation: total analytical error = bias + 1.65 × imprecision. This indirect estimation model (referred to as the simple combination model) leads to different estimates of total analytical error than that of a direct estimation method (referred to as the distribution-of-differences method) or of simulation.Methods: A review of the literature was undertaken to reconcile the different estimation approaches.Results: The simple combination model can underestimate total analytical error by neglecting random interference bias and by not properly treating other error sources such as linear drift and outliers. A simulation method to estimate total analytical error is outlined, based on the estimation and combination of total analytical error source distributions. Goals for each total analytical error source can be established by allocation of the total analytical error goal. Typically, the allocation is cost-based and uses the probability of combinations of error sources. The distribution-of-differences method, simple combination model, and simulation method to evaluate total analytical error are compared. Outlier results can profoundly influence quality, but their rates are seldom reported.Conclusions: Total analytical error should be estimated either directly by the distribution-of-differences method or by simulation. A systems engineering approach that uses allocation of the total analytical error goal into error source goals provides a cost-effective approach to meeting total analytical error. Because outliers can cause serious laboratory error, the inclusion of outlier rate estimates from large studies (e.g., those conducted by manufacturers) would be helpful in assessing assay quality.
机译:背景:总分析误差一直是评估实验室分析质量和设定目标的有用指标。通常可以通过将不精确度(SD)和平均偏差结合起来计算公式:总分析误差=偏差+ 1.65×不精确度。与直接估计方法(称为差异分布方法)或模拟方法相比,这种间接估计模型(称为简单组合模型)导致的总分析误差估计有所不同。结果:简单的组合模型可以通过忽略随机干扰偏差和不适当处理其他误差源(例如线性漂移和离群值)来低估总分析误差。基于对总分析误差源分布的估计和组合,概述了一种估算总分析误差的仿真方法。可以通过分配总分析误差目标来确定每个总分析误差源的目标。通常,分配基于成本,并使用错误源组合的概率。比较了差异分布法,简单组合模型和评估总分析误差的仿真方法。异常结果会严重影响质量,但很少报告其发生率。结论:总分析误差应直接通过差值分布法或通过仿真来估计。一种使用将总分析误差目标分配到误差源目标中的系统工程方法,可以提供一种经济高效的方法来满足总分析误差。由于异常值可能会导致严重的实验室错误,因此将大型研究(例如由制造商进行的研究)中的异常值估计包括在内将有助于评估测定质量。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号