Limits...
Demystifying EQA statistics and reports

View Article: PubMed Central - PubMed

ABSTRACT

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.

No MeSH data available.


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Mentions: Box plots are based on three different percentiles: the 25th (P25), the 50th (which is equivalent to the median) and the 75th (P75). A rectangle is drawn from P25 to the P75 percentile and lines extend the rectangle as far as values are not outliers. The outlier exclusion rule is simple and it states that all values lower than P25 - 1.5 (P75 – P25) and higher than P75 + 1.5 (P75 – P25) are considered as outliers (Figure 1). Eventually, outliers can be added as separate dots on the graph. Box plots inform about the location, scale and symmetry of the different groups, and for each group individually, show the presence - or absence - of outliers (44). Box plots adapted for EQA could be created by showing a box plot of all the data next to a box plot of the method group, with an indication of the individual laboratory result. Coloured or shaded rectangles can be used to indicate the area of acceptance according to different scoring systems. Box plots have the advantage of keeping their visual power even when they are reduced to small size and hence, they are ideal candidates for putting in reports containing results for multiple parameters.


Demystifying EQA statistics and reports
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC5382862&req=5

Mentions: Box plots are based on three different percentiles: the 25th (P25), the 50th (which is equivalent to the median) and the 75th (P75). A rectangle is drawn from P25 to the P75 percentile and lines extend the rectangle as far as values are not outliers. The outlier exclusion rule is simple and it states that all values lower than P25 - 1.5 (P75 – P25) and higher than P75 + 1.5 (P75 – P25) are considered as outliers (Figure 1). Eventually, outliers can be added as separate dots on the graph. Box plots inform about the location, scale and symmetry of the different groups, and for each group individually, show the presence - or absence - of outliers (44). Box plots adapted for EQA could be created by showing a box plot of all the data next to a box plot of the method group, with an indication of the individual laboratory result. Coloured or shaded rectangles can be used to indicate the area of acceptance according to different scoring systems. Box plots have the advantage of keeping their visual power even when they are reduced to small size and hence, they are ideal candidates for putting in reports containing results for multiple parameters.

View Article: PubMed Central - PubMed

ABSTRACT

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.

No MeSH data available.