Abstract
Uncertainty is an inseparable part of all types of measurement. Recently, the International Organization for Standardization (ISO) released a new standard (ISO 20914) on how to calculate measurement uncertainty (MU) in laboratory medicine. This standard can be regarded as the beginning of a new era in laboratory medicine. Measurement uncertainty comprises various components and is used to calculate the total uncertainty. All components must be expressed in standard deviation (SD) and then combined. However, the characteristics of these components are not the same; some are expressed as SD, while others are expressed as a ± b, such as the purity of the reagents. All non-SD variables must be transformed into SD, which requires a detailed knowledge of common statistical distributions used in the calculation of MU. Here, the main statistical distributions used in MU calculation are briefly summarized.
Highlights
Uncertainty is defined as a “non-negative parameter characterizing the dispersion of the quantity values being attributed to a measurand based on the information used” (VIM 2.26) [1]
The International Organization for Standardization (ISO) standard 15189 states that “The laboratory shall determine the uncertainty of results, where relevant and possible” [2]
1000 ± 5 mL, and if we know that there is a central tendency, i.e. the probability of the volume being 1000 mL is more likely than 995 mL or 1005 mL, in this case, it is better to use a triangular distribution than uniform distribution
Summary
Uncertainty is defined as a “non-negative parameter characterizing the dispersion of the quantity values being attributed to a measurand based on the information used” (VIM 2.26) [1]. The top-down approach is preferred to calculate the MU of test results. This manuscript is appropriate for the new ISO standard, which mainly considers the normal distribution and the “top down” approach In both the top-down and bottom-up approaches, uncertainty is expressed in terms of standard deviation (SD). There are various distribution types, only a few are sufficient to calculate the MU in laboratory medicine (Figure 1). We use the same sample, method, instrument and reagents the results of even repeated measurements are not always the same. When these data are examined visually on a graph, it can be seen that they exhibit a distribution. 99.7% of AUC (or results) is encompassed within the mean ± 3 SD
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