Measurement Uncertainty Vs Total Error
Measurement Uncertainty Vs Total Error
In a recent article, Error Methods Are More Practical, But Uncertainty Methods May Still Be Preferred, James Westgard comments on the latest developments in the debate on the use of analytical total error (TE) and measurement uncertainty (MU), a debate which has been regularly revisited for the last twenty years. This blog aims to briefly explore the benefits of MU and TE and attempt to draw a conclusion on which is most beneficial in the clinical laboratory.
Many things can undermine a measurement. Measurements are never made under perfect conditions and in a laboratory, errors and uncertainties can come from (Good Practice Guide No. 11, 2012):
- The measuring instrument – instruments can suffer from errors including bias, changes due to ageing, wear, poor readability, and noise.
- The item being measured – the sample may be unstable.
- The measurement process – the analyte may be difficult to measure
- ‘Imported’ uncertainties – calibration of the instrument.
- User error – skill and judgement of the operator can affect the accuracy of a measurement.
- Sampling issues – the measurements you make must be properly representative of the process you are trying to assess. I.e. not using fully commutable controls will mean your quality control process is not reflective of a true patient sample.
Random and systematic errors
The effects that give rise to uncertainty in a measurement can be either random or systematic, below are some examples of these in a laboratory.
- Random – bubbles in reagent, temperature fluctuation, poor operator technique.
- Systematic – sample handling, reagent change, instrument calibration (bias), inappropriate method.
Total Error (TE) or Total Analytical Error (TAE) represents the overall error in a test result that is attributed to imprecision (%CV) and inaccuracy (%Bias), it is the combination of both random and systematic errors. The concept of error assumes that the difference between the measured result and the ‘true value’, or reference quantity value, can be calculated (Oosterhuis et al., 2017).
TE is calculated using the below formula:
TE = %BIAS + (1.96 * %CV)
Measurement Uncertainty is the margin of uncertainty, or doubt, that exists about the result of any measurement.
There is always margin of doubt associated with any measurement as well as the confidence in that doubt, which states how sure we are that the ‘true value’ is within that margin. Both the significance, or interval, and the confidence level are needed to quantify an uncertainty.
For example, a piece of string may measure 20 cm plus or minus 1 cm with a 95% confidence level, so we are 95% sure that the piece of string is between 19 cm and 21 cm in length (Good Practice Guide No. 11, 2012).
Standards such as ISO 15189 require that laboratories must determine uncertainty for each test. Measurement Uncertainty is specifically mentioned in section 18.104.22.168:
“The laboratory shall determine measurement uncertainty for each measurement procedure in the examination phases used to report measured quantity values on patients’ samples. The laboratory shall define the performance requirements for the measurement uncertainty of each measurement procedure and regularly review estimates of measurement uncertainty.”
Uncertainty is calculated using the below formula:
u = √A2+B2
U = 2 x u
A = SD of the Intra-assay precision
B = SD of the Inter-assay precision
u = Standard Uncertainty
U = Uncertainty of Measurement
Error methods, compared with uncertainty methods, offer simpler, more intuitive and practical procedures for calculating measurement uncertainty and conducting quality assurance in laboratory medicine (Oosterhuis et al., 2018).
It is important not to confuse the terms ‘error’ and ‘uncertainty’.
- Error is the difference between the measured value and the ‘true value’.
- Uncertainty is a quantification of the doubt about the measurement result.
Whenever possible we try to correct for any known errors: for example, by applying corrections from calibration certificates. But any error whose value we do not know is a source of uncertainty (Good Practice Guide No. 11, 2012).
While Total Error methods are firmly rooted in laboratory medicine, a transition to the Measurement Uncertainty methods has taken place in other fields of metrology. TE methods are commonly intertwined with quality assurance, analytical performance specifications and Six Sigma methods. However, Total Error and Measurement Uncertainty are different but very closely related and can be complementary when evaluating measurement data.
Whether you prefer Measurement Uncertainty, Total Error, or believe that they should be used together, Randox can help. Our interlaboratory QC data management software, Acusera 24•7, automatically calculates both Total Error and Measurement Uncertainty. This makes it easier for you to meet the requirements of ISO:15189 and other regulatory bodies.
This is an example of the type of report generated by the 247 software. MU is displayed for each test and each lot of control in use therefore eliminating the need for manual calculation and multiple spreadsheets.
Fig. A and Fig. B above are examples of report generated by the 24•7 software. Fig.A shows how MU is displayed for each test and each lot of control in use therefore eliminating the need for manual calculation and multiple spreadsheets. Fig. B shows TE displayed for each test.
Acusera Third Party Controls
The Importance of ISO 15189
Good Practice Guide No. 11. (2012). Retrieved from http://publications.npl.co.uk/npl_web/pdf/mgpg11.pdf
Hill, E. (2017). Improving Laboratory Performance Through Quality Control.
Oosterhuis, W., Bayat, H., Armbruster, D., Coskun, A., Freeman, K., & Kallner, A. et al. (2017). The use of error and uncertainty methods in the medical laboratory. Clinical Chemistry and Laboratory Medicine (CCLM), 56(2). http://dx.doi.org/10.1515/cclm-2017-0341
Westgard, J. (2018). Error Methods Are More Practical, But Uncertainty Methods May Still Be Preferred. Clinical Chemistry, 64(4), 636-638. http://dx.doi.org/10.1373/clinchem.2017.284406