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Krouwer Consulting
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More on GUM I have critiqued the use of GUM (Guide to the expression of uncertainty in measurement) for commercial diagnostic assays (1) and also commented on a Letter about GUM (2). To review why I don’t favor the use of GUM for commercial diagnostic assays:
Having said all this, I am still onboard for use of GUM for reference materials. This essay is about another GUM article for which I published a Letter (4), which prompted a reply from the authors (5). Their article was about use of GUM for serological assays (6). What follows was sent as an eLetter to Clinical Chemistry. I appreciate the response by Dr. Dimech and understand that analyzing real data is never easy. Of course, I was unaware of Dr. Dimech’s response - I can only react to the words on the paper, not material that is omitted for whatever reason - thus my Letter. Here is my response to Dr. Dimech’s reply to my Letter combined with his original paper. Right after the statement to exclude outliers comes the advice: "It is suggested that results reported by each laboratory are checked for normality by use of a bar graph (See Fig. 1 in the online Data Supplement) or a statistical method such as Grubbs test." Normality is usually tested graphically with histograms and / or normal probability plots, not bar graphs. Grubb's test is not a test for normality - it is a test for outliers and requires normal data! Statistical tests for normality include the Shapiro-Wilk, Kolmogorov-Smirnov, and Anderson-Darling tests. Perhaps more importantly, consider the authors’ first sentence in the paper: ”Most regulatory authorities that use International Organization for Standardization (ISO) Standards to assess laboratory competence require an estimate of the uncertainty of measurement (MU) of assay test results.” At best this sentence is ambiguous. Perhaps the authors mean that one of the components of laboratory competence is an uncertainty interval but one could also interpret this sentence to equate an uncertainty interval with laboratory competence, even though to a clinician, laboratory competence would suggest an acceptable rate of errors from all sources. In the case of laboratory data, the distribution of errors can be of any shape and can contain large errors, which may or may not be detached from the rest of the error distribution. To a clinician, wrong answers are dangerous, regardless of their source. So, blunders such as the typographical error are part of the population of interest to a clinician. Now for certain purposes, one can define a subset of the population of errors that contain only analytical error sources and exclude pre- and post- analytical error sources. However, this subset can be quickly confused with the total population and the first sentence in this paper will add to this confusion. References
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