Jansen R T
Ann Clin Biochem. 1983 Jan;20 Pt 1:41-51. doi: 10.1177/000456328302000107.
Routine analytical methods for seven serum analytes (calcium, chloride, cholesterol, glucose, inorganic phosphate, urate, and urea) are assessed using data from the Netherlands coupled external/internal quality control program. From the results of a trial each method can be described by four features: measures of bias, between-day precision, tendency to give erroneous results, interlaboratory variance. These four features of each trial determine a vectorpoint in the four-dimensional space for a particular method. From 12 trials a maximum of 12 vectorpoints per analytical method was obtained. Pattern recognition techniques allowed the detection of clusters of vectorpoints. Analytical methods having vectorpoints classified in different clusters perform differently. The mean feature values of the vectorpoints forming a cluster determine the quality of that cluster. A weighting procedure reveals the importance of the respective features for discriminating the clusters. For all of the seven analytes, clusters of vectorpoints were found. Different features appeared to contain discriminatory power for different analytes. For six analytes (calcium, chloride, cholesterol, glucose, inorganic phosphate, and urea) an analytical method was found to classify predominantly in the qualitative best cluster. One analytical method for the determination of chloride and one for glucose, inorganic phosphate, and urea did not cluster at all.
利用来自荷兰外部/内部联合质量控制项目的数据，对七种血清分析物（钙、氯、胆固醇、葡萄糖、无机磷酸盐、尿酸盐和尿素）的常规分析方法进行了评估。从一项试验的结果来看，每种方法都可以通过四个特征来描述：偏差度量、日间精密度、给出错误结果的倾向、实验室间差异。每次试验的这四个特征为特定方法在四维空间中确定了一个向量点。从12次试验中，每种分析方法最多获得12个向量点。模式识别技术能够检测向量点的聚类。向量点分类在不同聚类中的分析方法表现不同。构成一个聚类的向量点的平均特征值决定了该聚类的质量。一种加权程序揭示了各个特征对于区分聚类的重要性。对于所有七种分析物，都发现了向量点的聚类。不同的特征对于不同的分析物似乎具有区分能力。对于六种分析物（钙、氯、胆固醇、葡萄糖、无机磷酸盐和尿素），发现有一种分析方法主要归类于质量最佳的聚类中。有一种测定氯的分析方法以及一种测定葡萄糖、无机磷酸盐和尿素的分析方法根本没有聚类。
