I occasionally come across articles that describe a method evaluation using GUM (Guide to the expression of Uncertainty in Measurement). These papers can be quite impressive with respect to the modeling that occurs. However, there is often a statement that relates the results to clinical acceptability. Here’s why there is a problem.
Clinically acceptability is usually not defined but often implied to be a method’s performance that will not cause patient harm due to assay error.
A GUM analysis usually specifies the location for 95% of the results. But if the analysis shows that the assay just meets limits, then 5% of the results will cause patient harm. Now according to GUM models, the 5% will be close to limits because the data are assumed to be Gaussian so this is a minor problem.
A bigger problem is that GUM analysis often ignores rare but large errors such as a rare interference or something more insidious such a user error that results in a large assay error. (Often GUM analyses don’t assess user error at all). These large errors, while rare, are associated with major harm or death.
The remedy is to conduct a FMEA or fault tree in addition to GUM to try to brainstorm how large errors could occur and whether mitigations are in place to reduce their likelihood. Unless risk analysis is added to GUM, talking about clinical acceptability is misleading.