I was happy to see an editorial which IMHO states the required error components that need to be understood to ensure the clinical usefulness of an assay. Of course bias and imprecision are mentioned. But in addition, the author mentions freedom from interferences and pre and post analytical errors.
One can ask don’t interferences and pre and post analytical errors cause bias? Since the answer is yes, then why do these terms need to be mentioned if it was already stated that bias is to be measured. The reason is the way bias is measured in many cases will fail to detect the biases from interferences and pre and post analytical errors.
For example, if regression is used, average bias will be estimated, not the individual biases that can occur from interferences.
If σ is estimated, this usually involves bias measured from either regression or from quality control samples so again interference biases don’t get counted.
Finally, most of the studies are done in ways in which pre and post analytical errors have been minimized – the studies are performed outside of the routine way of processing patient samples. Hence, to ensure the clinical usefulness of an assay, one must construct protocols that measure all of the error components mentioned in the first paragraph.