Your results may vary: the imprecision of medical measurements

Clinicians and patients need to interpret a multitude of medical measurements. These are often central to monitoring health and informed decision making. Has the serum cholesterol concentration come down since starting a statin? Have vitamin D levels gone up? Is the dose of thyroid medication correct? An understanding of the imprecision of medical measurements is essential to answer any of these questions. Even when laboratory and industry scientists have optimised their diagnostic testing processes to minimise inaccuracies, there always remains an error in any clinical measurement due to unavoidable, naturally occurring variability.

This BMJ article explains the nature of measurement errors and offers a practical guide to both estimating the confidence interval of a single result and deciding if changes between serial laboratory tests reflect true changes or simply fluctuations based on analytical or biological variation. (BMJ

What you need to know

  • Inherent in every medical measurement is a degree of uncertainty: you must have a rough idea of the magnitude of that uncertainty to correctly interpret any reported measurement

  • The greater the uncertainty, the greater the difference that needs to be observed between two measurements before you can be confident that a true change has occurred

  • The “reference change value” (RCV) allows you to decide whether a change in two serial lab results is likely due to chance alone. The required change may be as small as 2% and as large as 50% depending on the test

  • Biological variation is typically the largest contributor to the RCV. For analytical variation, your local lab director can tell you the measurement error of any test you are interested in


TABLE -Approximate variability estimates for routine medical measurements

The first data column of the table conveys the actual analytical confidence interval or error due to the measurement process alone, without any consideration of biological effects. This column answers an immediate question, “What is the 95% confidence interval of a numerical result reported from the lab for a single analysis?”

The second data column provides the combined analytical and biological variation. It answers the question, “What is the 95% confidence interval of a single measurement for estimating the long term biological set point of the blood test?”

The last column of the table provides information to help you decide if a difference between two serial measurements reflects a real change or just fluctuation due to biological and analytical processes. It answers the question, “By how much does a test result need to change from the prior measurement in order to be considered different with 95% confidence?”

There is an interactive app-like, web-based table, that will give you all of these values with the values that your patient’s pre and post-test results. Check this out.

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