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Imprecision calculations – Evaluations commonly reported total imprecision as less than within-run imprecision. Correct calculations are explained.
How to Improve Estimates of Imprecision Clin. Chem., 30, 290-292 (1984)
Total error models – Modeling total error by adding imprecision to bias is popular but fails to account for several other error sources. These articles (and others) provide alternative models.
Estimating Total Analytical Error and Its Sources: Techniques to Improve Method Evaluation Arch Pathol Lab Med., 116, 726-731 (1992)
Setting Performance Goals and Evaluating Total Analytical Error for Diagnostic Assays Clin. Chem., 48: 919-927 (2002)
Too optimistic project completion schedules – Project managers would forecast completion dates that were never met. The article shows how to get better completion estimates using past data.
Beware the Percent Completion Metric Research Technology Management, 41, 13-15, (1998)
GUM – The guide to the expression of uncertainty in measurement was suggested to be performed by hospital labs. There’s no way a hospital lab could carry out this work.
A Critique of the GUM Method of Estimating and Reporting Uncertainty in Diagnostic Assays Clin. Chem., 49:1818-1821 (2003)
ISO 9001 – There have been many valuable quality initiatives. In the late 80s, ISO 9001 was a program to certify that companies that passed had high quality. But it was nothing more than documentation – it did nothing to improve quality. Maybe the lab equivalent ISO 15189 is the same.
ISO 9001 has had no effect on quality in the in-vitro medical diagnostics industry Accred. Qual. Assur., 9: 39-43 (2004)
Bland-Altman plots – Bland-Altman plots (difference plots) suggest plotting the difference of y-x vs. (y+x)/2 in order to prevent spurious correlations. But the article below shows that if x is a reference method, following Bland and Altman’s advice will produce a spurious correlation. The difference of y-x vs x should be plotted when x is a reference method.
Why Bland-Altman plots should use X, not (Y+X)/2 when X is a reference method Statistics in Medicine, 27 778-780 (2008)
Six Sigma – This metric is often presented as a sole quality measure but it basically measures only average bias and imprecision. As this article shows there can be severe problems with an assay even when it has a high sigma.
Six Sigma can be dangerous to your health Accred Qual Assur 14 49-52 (2009)
Glucose standards – The glucose meter standard ISO 15197 has flaws. This letter pointed out what the experts missed in a question and answer forum.
Wrong thinking about glucose standards Clin Chem, 56 874-875 (2010)
POCT12-A3 – The article explains flaws in this CLSI glucose standard
The new glucose standard POCT12-A3 misses the mark Journal of Diabetes Science and Technology, September 7 1400–1402 (2013)
Regulatory approval evaluations – The performance of assays during regulatory evaluations is often quite better than when the assays are in the field. The articles gives some reasons why.
Biases in clinical trials performed for regulatory approval Accred Qual Assur, 20:437-439 (2015)
MARD – This metric to classify glucose meter quality leaves a lot to be desired. The article below suggests an alternative
Improving the Glucose Meter Error Grid with the Taguchi Loss Function Journal of Diabetes Science and Technology, 10 967-970 (2016)
Interferences – Motivated by a recent paper where interferences were treated almost as a new discovery (and given a new name), this paper discusses how specifications and analyses methods can be improved by accounting for interferences. And I also mention how the CLSI EP7 standard reports interferences incorrectly and could cause problems for labs.
Interferences, a neglected error source. Accred. Qual. Assur. 23(3):189-192 (2018).