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.


Antwerp talk about total error

March 12, 2017

Looking at my blog stats, I see that a lot of people are reading the total analytical error vs. total error post. So, below are the slides from a talk that I gave at a conference in Antwerp in 2016 called The “total” in total error. The slides have been updated. Because it is a talk, the slides are not as effective as the talk.




Published – my one man Milan Conference

March 23, 2016


Having read the consensus statement and all the papers from the Milan conference (available without subscription), I prepared my version of this for the Antwerp conference. This talk contained the following:

  • A description of why the Westgard model for total error is incomplete (with of course Jim Westgard sitting in the audience)
  • A description of why expanded total error models are nevertheless also incomplete
  • A critique of Boyd and Bruns’ glucose meter performance simulations using the Westgard model
  • A critique of the ISO and CLSI glucose meter specifications, both based on total error
  • A description of what the companies with most of the market share in glucose meters did, when they started to lose market share
  • How Ciba Corning specified and evaluated performance
  • What I currently recommend

I submitted a written version of this talk to Clin Chem and Lab Medicine, with recommended reviewers being Milan authors with whom I disagreed. (The journal asks authors to recommend reviewers). Now I don’t know who the reviewers were, but suffice it to say that they didn’t like my paper at all. So after several revisions, I scaled back my paper to its current version, which is here (subscription required).

Whining rewarded?

August 29, 2014


Looking at the table of contents of Clinical Chemistry for September, there is a list of the most downloaded point / counterpoint articles and I am number one on this list for my discussion of GUM (The guide to the expression of uncertainty in measurement):

Why GUM will never be enough

April 7, 2014


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.

AACC 2012

July 19, 2012

I went early to AACC 2012 on a consulting assignment which ended two days before AACC. So the highlight of my trip was a helicopter tour of LA in an R22, a tour of Warner Bros. studios, and a walk on the Santa Monica pier.

At the meeting, I got to hear two plenary lectures, both on genomics. They were very interesting talks (Eric Green and Robert Roberts) and it was humbling to realize how little I know about genomics.

I also attended the Evaluations Protocol Area Committee, although I think it is now called a consensus committee. There were two projects that I had started EP27 (error grids) and the revision to EP21A2 (total error) – I proposed and completed EP21A. As of January, I had been unexpectedly and rather unceremoniously kicked off both projects. There was little discussion about EP27 – it should be available around September 2012 and little changed. It will be interesting to see who the authors will be.

There was more discussion about EP21, including a proposal to drop it completely in favor of a GUM uncertainty analysis, which is a CLSI document (C51). This is about when I had enough and bolted from the meeting. Yet, I did have the comfort in knowing that the financial way projects are valued is something I put in place a while back and probably unknown to this current group. 

I went to a talk about medical error. One thing that is always missing from these talks is a measure of overall patient harm – maybe the subject of a future blog entry.

I also talked with a very nice Spanish lady about her poster. She assayed a sample on each of two analyzers of the same type and compared the results to various total error goals including biological variation, CLIA, and others. The results were often outside of goals, especially biological variation goals which makes one wonder if such goals are meaningful.

CLSI C51 – measurement uncertainty – or the classic comic version of GUM

February 29, 2012

GUM (Guide to the Expression of Uncertainty in Measurement) for laboratories (and manufacturers) is what CLSI C51 is all about.  (GUM was originally used to provide information about reference materials). I have previously commented that I didn’t think that GUM was a good idea for laboratories (1). I was also initially on the C51 subcommittee but since I couldn’t convince anyone about my point of view, I bailed.

To recall some of the problems with GUM …

  1. bias is not allowed – it must be corrected. But you could ignore big, rare biases (outliers) as well as real small biases.
  2. To obtain the standard deviations or bias corrections applied by manufacturers was impractical if not impossible for laboratories as in … Let’s set up a fixture and measure the variability of 10 pumps we just bought for this experiment.
  3. The math required to put together an estimate will make most people’s head spin.

In the C51 version of GUM, there is only 1 example – that of measuring a bunch of controls. This is not GUM! and will not provide an uncertainty estimate for patient samples since controls do not estimate the non specificity assay errors in patient samples.


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