Uncertainty of measurement: an immunology laboratory perspective

Area of Science:

• Clinical Laboratory Science
• Immunology
• Measurement Science

Background:

• ISO15189 mandates measurement uncertainty determination for patient sample analyses.
• Numeric data allows uncertainty expression via standard deviation or coefficient of variation.
• Immunology assays often yield semi-quantitative (e.g., antibody titre) or qualitative (positive/negative) results, complicating uncertainty assessment.

Purpose of the Study:

• To explore the challenges of determining measurement uncertainty in immunology.
• To identify strategies for minimizing uncertainty in qualitative and semi-quantitative assays.
• To highlight the applicability of these challenges to other disciplines with similar data types.

Main Methods:

• Review of parameters contributing to measurement uncertainty (bias, precision, sensitivity, specificity, etc.).
• Discussion of the difficulties in quantifying uncertainty for non-numeric results.
• Exploration of strategies to minimize uncertainty in immunological assays.

Main Results:

• Quantifying measurement uncertainty is significantly more difficult for qualitative and semi-quantitative assays compared to numeric ones.
• Several parameters, including bias, precision, repeatability, and reproducibility, influence measurement uncertainty.
• Traceability and standardization are critical for managing uncertainty.

Conclusions:

• Addressing measurement uncertainty in immunology requires specific strategies, particularly for qualitative and semi-quantitative data.
• The challenges and strategies discussed are relevant to other scientific fields dealing with non-numeric data.
• Minimizing uncertainty is achievable through careful consideration of influencing parameters and appropriate methodologies.