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.