## An appropriate use of a measurement uncertainty model

April 20, 2018

As readers are probably aware, I am not a fan of measurement uncertainty (1). This is not because there is something wrong with measurement uncertainty but rather because it is proposed to model actual results of an assay (as in a method evaluation). As I described earlier, there is no reason to model actual results, since one can simply directly obtain the distribution of differences between an assay and reference.

Where a measurement uncertainty model is appropriate would be for an assay under development. In this case, there are no results, only a proposed collection of reagents at various concentrations and a proposed physical system to measure a response output.

References

1. Krouwer JS A Critique of the GUM Method of Estimating and Reporting Uncertainty in Diagnostic Assays 2003 Clin Chem 49:1818-1821.

## A simple improvement to total error and measurement uncertainty

January 15, 2018

There has been some recent discussion about the differences between total error and measurement uncertainty, regarding which is better and which should be used. Rather than rehash the differences, let’s examine some similarities:

1.       Both specifications are probability based.
2.       Both are models

Being probability based is the bigger problem. If you specify limits for a high percentage of results (say 95% or 99%), then either 5% or 1% of results are unspecified. If all of the unspecified results caused problems this would be a disaster, when one considers how many tests are performed in a lab. There are instances of medical errors due to lab test error but these are (probably?) rare (meaning much less than 5% or 1%). But the point is probability based specifications cannot account for 100% of the results because the limits would include minus infinity to plus infinity.

The fact that both total error and measurement uncertainty are models is only a problem because the models are incorrect. Rather than rehash why, here’s a simple solution to both problems.

Add to the specification (either total error or measurement uncertainty) the requirement that zero results are allowed beyond a set of limits. To clarify, there are two sets of limits, an inner set to contain 95% or 99% of results and an outer set of limits for which no results should exceed.

Without this addition, one cannot claim that meeting either a total error or measurement uncertainty specification will guarantee quality of results, where quality means that the lab result will not lead to a medical error.

## Calculating measurement uncertainty and GUM

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.

1. 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.
2. Any bias found must be eliminated. There is imprecision in the elimination of the bias. Hence bias has been transformed into imprecision.
3. Combine all sources of imprecision into a BHE (big hairy equation – my term, not GUMs).
4. 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.