February 18, 2018
To recall, total analytical error was proposed by Westgard in 1974. It made a lot of sense to me and I proposed to CLSI that a total analytical error standard should be written. This proposal was approved and I formed a subcommittee which I chaired and in 2003, the CLSI standard EP21-A, which is about total analytical error was published.
When it was time to revise the standard – all standards are considered for revision – I realized that the standard had some flaws. Although the original Westgard article was specific to total analytical error, it seemed that to a clinician, any error that contributed to the final result was important regardless of its source. And for me, who often worked in blood gas evaluations, user error was an important contribution to total error.
Hence, I suggested the revision to be about total error, not total analytical error and EP21-A2 drafts had total error in the title. There were some people within the subcommittee and particularly one or two people not on the subcommittee but in CLSI management, who hated the idea, threw me off my own subcommittee and ultimately out of CLSI.
But recently (in 2018) a total error task force published an article which contained the statement, to which I have previously referred:
“Lately, efforts have been made to expand the TAE concept to the evaluation of results of patient samples, including all phases of the total testing process.” (I put in the bolding).
Hence, I’m hoping that the next revision, EP21-A3 will be about total error, not total analytical error.
February 16, 2018
Here’s an article about flash glucose monitoring, a way for diabetic patients to avoid finger sticks and glucose monitors. Now I can understand why other glucose meter companies are trying to get out of the business. This product sounds like a game changer.
February 14, 2018
The 11th International Conference on Advanced Technologies and Treatments for Diabetes (ATTD) is underway in Vienna, Austria. The abstracts from the conference are available here. Here’s an interesting observation: I searched for the term MARD and it was found 48 times whereas the term error grid was found only 10 times. I published a paper describing problems with the MARD statistic and offered alternatives.
February 5, 2018
There is a recent article which says that measurement uncertainty should contain a term for biological variation. The rationale is that diagnostic uncertainty is caused in part by biological variation. My concerns are with how biological variation is turned into goals.
On the Westgard web site, there are some formulas on how to convert biological variation into goals and on another page, there is a list of analytes with biological variation entries and total error goals.
Here are my concerns:
- There are three basic uses of diagnostic tests: screening, diagnosis, and monitoring. It is not clear to me what the goals refer to.
- Monitoring is an important use of diagnostic tests. It makes no sense to construct a total error goal for monitoring that takes between patient biological variation into account. The PSA total error goal is listed at 33.7%. Example: For a patient tested every 3 months after undergoing radiation therapy, a total error goal of 33.7% is too big. Thus, for values of 1.03, 0.94, 1.02, and 1.33, the last value is within goals but in reality would be cause for alarm.
- The web site listing goals has only one goal per assay. Yet, goals often depend on the analyte value, especially for monitoring. For example the glucose goal is listed at 6.96%. But if one examples a Parkes glucose meter error grid, at 200 mg/dL, the error goal to separate harm from no harm is 25%. Hence, the biological goal is too small.
- The formulas on the web site are hard to believe. For example, I < 0.5 * within person biological variation. Why 0.5, and why is it the same for all analytes?
- Biological variation can be thought to have two sources of variation – explained and unexplained – much like in a previous entry where the measured imprecision could be not just random error, but inflated with biases. Thus, PSA could rise due to asymptomatic prostatitis (a condition that by definition that has no symptoms and could be part of a “healthy” cohort). Have explained sources of variation been excluded from the databases? And there can be causes of explained variation other than diseases. For example, exercise can cause PSA to rise in an otherwise healthy person.
- Biological variation makes no sense for a bunch of analytes. For example, blood lead measures exposure to lead. Without lead in the environment, the blood lead would be zero. Similar arguments apply to drugs of abuse and infectious diseases.
- The goals are based on 95% limits from a normal distribution. This leaves up to 5% of results as unspecified. Putting things another way, up to 5% of results could cause serious problems for an assay that meets goals.
January 31, 2018
In clinical chemistry, one often hears that there are two contributions to error – systematic error and random error. Random error is often estimated by taking the SD of a set of observations of the same sample. But does the SD estimate random error? And are repeatability and reproducibility forms of random error? (Recall that repeatability = within run imprecision and reproducibility = long term (or total) imprecision.
Example 1 – An assay with linear drift with 10 observations run one after the other.
The SD of these 10 observations = 1.89. But if one sets up a regression with Y=drift + error, the error term is 0.81. Hence, the real random error is much less than the estimated SD random error because the observations are contaminated with a bias (namely drift). So here is a case where repeatability doesn’t measure random error by taking the SD, one has to investigate further.
Example 2 – An assay with calibration (drift) bias using the same figure as above (Ok I used the same numbers but this doesn’t matter).
Assume that in the above figure, each N is the average of a month of observations, corresponding to a calibration. Each subsequent month has a new calibration.
Clearly, the same argument applies. There is now calibration bias which inflates the apparent imprecision so once again, the real random error is much less than what one measures by taking the SD.
January 25, 2018
Anyone who has even briefly ventured into the realm of statistics has seen the standard setup. One states a hypothesis, plans a protocol, collects and analyzes data and finally concludes that the hypothesis is true or false.
Yet a typical lab medicine evaluation will state the importance of the assay, present data about precision, bias, and other parameters and then launch into a discussion.
What’s missing is the hypothesis, or in terms that we used in industry – the specifications. For example, assay A should have a CV of 5% or less in the range of XX to YY. After data analysis, the conclusion is that assay A met (or didn’t meet) the precision specification.
These specifications are rarely if ever present in evaluation publications. Try to find a specification the next time you read an evaluation paper. And without specifications, there are usually no meaningful conclusions.
January 17, 2018
Doing my infrequent journal scan, I came across the following paper – “The use of error and uncertainty methods in the medical laboratory” available here. Ok, another sentence floored me (it’s in the abstract)… “Performance specifications for diagnostic tests should include the diagnostic uncertainty of the entire testing process.” It’s a little hard to understand what “diagnostic uncertainty” means. The sentence would be clearer if it read Performance specifications for diagnostic tests should include the entire testing process. But isn’t this obvious? Does this need to be stated as a principle in 2018?