Comparison of different approaches to evaluate External Quality Assessment Data

Coucke, Wim; China, Bernard; Delattre, Isabelle; Lenga, Yolande; Blerk, Marjan Van; Campenhout, Christel Van; Walle, P. Van de; Vernelen, Kris; Albert, Adelin

doi:10.1016/j.cca.2011.11.030

Cited by 18 publications

(18 citation statements)

References 23 publications

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“…For relative small data series (N < 15), a higher value of alpha could be adopted. Recently, the Hampel and Grubbs tests have been proposed as preferable in comparison to the Dixon test ( 23 – 25 ) with the Grubbs test able to handle also small data series, from six data points on ( 25 ). …”

Section: Outlier Removalmentioning

confidence: 99%

Demystifying EQA statistics and reports

Coucke

Soumali

2017

Biochem Med

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Reports act as an important feedback tool in External Quality Assessment (EQA). Their main role is to score laboratories for their performance in an EQA round. The most common scores that apply to quantitative data are Q- and Z-scores. To calculate these scores, EQA providers need to have an assigned value and standard deviation for the sample. Both assigned values and standard deviations can be derived chemically or statistically. When derived statistically, different anomalies against the normal distribution of the data have to be handled. Various procedures for evaluating laboratories are able to handle these anomalies. Formal tests and graphical representation techniques are discussed and suggestions are given to help choosing between the different evaluations techniques. In order to obtain reliable estimates for calculating performance scores, a satisfactory number of data is needed. There is no general agreement about the minimal number that is needed. A solution for very small numbers is proposed by changing the limits of evaluation. Apart from analyte- and sample-specific laboratory evaluation, supplementary information can be obtained by combining results for different analytes and samples. Various techniques are overviewed. It is shown that combining results leads to supplementary information, not only for quantitative, but also for qualitative and semi-quantitative analytes.

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Section: Outlier Removalmentioning

confidence: 99%

Demystifying EQA statistics and reports

Coucke

Soumali

2017

Biochem Med

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“…The estimated SD of the characteristic function may act as a helpful estimate of the SD to be expected from the future reported EQA results. Subsequently, the estimated SD may be used to assess laboratories' performance on the basis of z-scores and may be preferred when the SD of the reported results is not reliable, for example, when there are not enough data available (21 ) or when results were reported near the limit of quantification. Koch and Magnusson (10 ) already mentioned how to compare methods with the characteristic function.…”

Section: Discussionmentioning

confidence: 99%

Application of the Characteristic Function to Evaluate and Compare Analytical Variability in an External Quality Assessment Scheme for Serum Ethanol

et al. 2015

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BACKGROUND:As a cornerstone of quality management in the laboratory, External Quality Assessment (EQA) schemes are used to assess laboratory and analytical method performance. The characteristic function is used to describe the relation between the target concentration and the EQA standard deviation, which is an essential part of the evaluation process. The characteristic function is also used to compare the variability of different analytical methods.

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“…Based on the assumption that 25th and 75th percentiles are generally not influenced by outliers, this nonparametric approach has the advantage to be more realistic in the interpretation of EQAS results (small number of method users, non-Gaussian distribution) [15,16]. Measures of central tendency and dispersion were evaluated by selecting methods used by 6 laboratories or more [15].…”

Section: Analysis and Data Processingmentioning

confidence: 99%

“…After the closing date, statistics were performed in order to derive descriptive statistics (i.e. inter-and intramethod medians with standard deviations) based on the Tukey's robust approach used for Belgian EQAS [15]. Based on the assumption that 25th and 75th percentiles are generally not influenced by outliers, this nonparametric approach has the advantage to be more realistic in the interpretation of EQAS results (small number of method users, non-Gaussian distribution) [15,16].…”

Section: Analysis and Data Processingmentioning

confidence: 99%