Clinica Chimica Acta：什么是评价质量控制预测性能的指标？

2019-09-21 gladiator MedSci原创

QC计划是使用平均运行长度(ARL)和信号时间(TTS)来评估的，然而大多数实验室都不熟悉这些指标。质量控制(QC)策略通常使用功率曲线来进行设计。这类分析从原因(检测结果的变化)到结果(QC监控过程的信号)进行推导。最终用户面临一个不同的问题：他们必须从结果(QC信号)推理到原因。因此，如果有一个从最终用户的角度评估QC策略的度量标准将会很有帮助。

QC计划是使用平均运行长度(ARL)和信号时间(TTS)来评估的，然而大多数实验室都不熟悉这些指标。质量控制(QC)策略通常使用功率曲线来进行设计。这类分析从原因(检测结果的变化)到结果(QC监控过程的信号)进行推导。最终用户面临一个不同的问题：他们必须从结果(QC信号)推理到原因。因此，如果有一个从最终用户的角度评估QC策略的度量标准将会很有帮助。

本研究在分析误差分类的基础上，建立了一个简单的二分类模型。根据临界偏移量(Sc)将误差分为重要和不重要两类。使用该方案，研究人员展示了如何使用常见的准确性指标(如敏感性、特异性、阳性预测值(PPV)和阴性预测值(NPV)来分析QC策略。探讨了设计选择(QC限制，重复次数)在许多不同环境下对这些性能度量的影响。

研究显示PPV的变化很大(1%到100%)，并取决于研究背景。NPV也会变化(40%到100%)，但与PPV相比，受背景影响较小。在许多情况下，QC策略具有较低的预测值。在这种情况下，性能(PPV, NPV)可以通过调整QC限制或每次QC事件的重复次数来提高。

研究表明，鉴于实施质量控制政策的环境，可以提高质量控制的有效性。使用简单的假设，可以使用常见的精度指标来评估QC策略的性能。

原始出处：

Robert L. Schmidt，Lauren N. Pearson，Quality control optimization part I: Metrics for evaluating predictive performance of quality control

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2020-07-07 zhouqu_8

#质量控制#

48 0
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2019-10-18 windight

#CTA#

34 0
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2019-11-08 heli0118

#预测性能#

0 0
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2019-09-23 huagfeg

#评价#

30 0
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2019-09-22 飛歌

学习了，很有用不错

57 0

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