Assessing the measurement uncertainty of qualitative analysis in the clinical laboratory

Abstract

Measurement uncertainty is a parameter that is associated with the dispersion of measurements. Assessment of the measurement uncertainty is recommended in qualitative analyses in clinical laboratories; however, the measurement uncertainty of qualitative tests has been neglected despite the introduction of many adequate methods. We herein provide an overview of three reasonable statistical methods for quantifying the measurement uncertainties of qualitative assays, namely Bayes’ theorem, the normal distribution method, and the information theoretic approach. Unlike in quantitative analysis, the measurement uncertainty of qualitative analysis is expressed using a conditional probability, likelihood ratio, and entropy. With the necessary theoretical background, the practical applications for clinical laboratories are also provided using statistical calculations. Using statistical approaches, we hope that our review will contribute to the use of measurement uncertainty in qualitative analyses in the clinical laboratory environment.

Keywords

• bayes’ theorem
• information theory
• measurement uncertainty
• normal distribution
• qualitative analysis