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A simple transformation independent method for outlier definition
Martin Berg Johansen1, Peter Astrup Christensen2
1Unit of Clinical Biostatistics, Aalborg University Hospital, Aalborg, Denmark.
Clinical Chemistry and Laboratory Medicine
|April 11, 2018
Summary
A new non-parametric method for outlier detection in medical laboratories is as good as or better than current methods. This transformation-independent approach improves the accuracy of establishing reference intervals (RIs).
Area of Science:
- Clinical Laboratory Science
- Biostatistics
- Medical Diagnostics
Background:
- Accurate outlier definition is crucial for medical laboratories establishing or verifying reference intervals (RIs).
- Outliers can significantly impact the determination of reference limits, affecting diagnostic accuracy.
- Existing outlier detection methods often require data transformation, particularly for non-normal distributions.
Purpose of the Study:
- To develop and evaluate a non-parametric, transformation-independent method for outlier definition.
- To compare the proposed method against the CLSI/IFCC recommended approach using Box-Cox transformation (BCT) and Tukey's fences.
- To assess the impact of outliers on BCT and its subsequent effect on RI determination.
Main Methods:
- Developed a novel non-parametric outlier definition method based on reproducible histograms with defined bin sizes around the median.
- Compared the proposed method with the CLSI/IFCC recommended method (BCT and Tukey's fences).
- Evaluated performance on eight simulated distributions and indirect clinical datasets with and without added outliers.
Main Results:
- The proposed method demonstrated comparable or superior performance to the recommended method, especially when outliers were present on one side of the distribution.
- The presence of outliers was found to negatively affect Box-Cox transformation (BCT), consequently impacting determined reference limits, particularly in skewed distributions.
- The new outlier definition successfully reproduced current RI limits on clinical data containing outliers.
Conclusions:
- The developed non-parametric, transformation-independent outlier detection method is a viable and effective alternative to current recommended techniques.
- This method offers improved robustness and accuracy in reference interval determination, especially in the presence of outliers and non-normal data.
- The findings suggest a simpler and more reliable approach for outlier management in clinical laboratory settings.
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