October 30, 2017
When I worked at Corning Medical (later Ciba Corning), we worked in cubicles and everyone had a whiteboard. These whiteboards were used often and sometimes a section of the whiteboard would be marked so that the contents of that section would remain as in DO NOT ERASE! I was pleased to note that on a few whiteboards, something that I had said was in the do not erase section.
But there was one person who dominated the do not erase section, even after he had long left the company. This was David Simmons, who acted as in in-house psychologist. This was one measure of his influence.
October 16, 2017
A recent article (subscription required) suggests how to estimate measurement uncertainty for an assay to satisfy the requirements of ISO 15189.
As readers may know, I am neither a fan of ISO nor measurement uncertainty. The formal document, GUM – The Guide to the Expression of Uncertainty in Measurement will make most clinical chemists heads spin. Let’s review how to estimate uncertainty according to GUM.
- Identify each item in an assay that can cause uncertainty and estimate its imprecision. For example a probe picks up some patient sample. The amount of sample taken varies due to imprecision of the sampling mechanism.
- Any bias found must be eliminated. There is imprecision in the elimination of the bias. Hence bias has been transformed into imprecision.
- Combine all sources of imprecision into a BHE (big hairy equation – my term, not GUMs).
- The final estimate of uncertainty is governed by a coverage factor. Thus, an uncertainty interval for 99% is wider than one for 95%. Remember that an uncertainty interval for 100% is minus infinity to plus infinity.
The above Clin Chem Lab Med article calculates uncertainty by mathematically summing imprecision of controls and bias from external surveys. This is of course light years away from GUM. The fact that the authors call this measurement uncertainty could confuse some to think that this is the same as GUM.
Remember that in the authors’ approach, there are no patient samples. Thus, the opportunity for errors due to interferences has been eliminated. Moreover, patient samples can have errors that controls do not. Measurement uncertainty must include errors from the entire measurement process, not just the analytical error.
Perhaps the biggest problem is that a clinician may look at such an uncertainty interval as truth, when the likely true interval will be wider and sometimes much wider.