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Reference range: Which statistical intervals to use?

Wei Liu¹, Frank Bretz², Mario Cortina-Borja³

¹Mathematical Sciences & Southampton Statistical Sciences Research Institute, University of Southampton, Southampton, UK.

Statistical Methods in Medical Research

|October 15, 2020

Summary

This summary is machine-generated.

Reference ranges in clinical labs are crucial for identifying atypical results. This study shows that tolerance intervals are more reliable than prediction intervals for establishing accurate reference ranges, ensuring better population coverage.

Keywords:

Nonparametric prediction interval nonparametric tolerance interval prediction interval reference range tolerance interval

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Area of Science:

Clinical Laboratory Science
Biostatistics
Statistical Inference

Background:

Reference ranges are essential for interpreting clinical laboratory results and identifying atypical observations.
Establishing accurate reference ranges is critical for patient diagnosis and monitoring.
The probabilistic nature of reference ranges necessitates careful selection of statistical methods.

Purpose of the Study:

To evaluate the suitability of different statistical intervals as reference ranges in clinical laboratories.
To demonstrate the inadequacy of prediction intervals for constructing reliable reference ranges.
To advocate for the use of tolerance intervals for robust reference range establishment.

Main Methods:

Comparison of statistical properties of prediction intervals and tolerance intervals.
Analysis of population coverage probability for different interval types.
Illustrative example using real-world clinical laboratory data.

Main Results:

Prediction intervals may fail to cover the specified population proportion, especially with large sample sizes.
Tolerance intervals are designed to cover a specified population proportion with a defined confidence level.
The study highlights the probabilistic limitations of prediction intervals for reference range applications.

Conclusions:

Tolerance intervals are statistically superior and more appropriate for use as reference ranges in clinical laboratories.
Prediction intervals can lead to inaccurate classification of patient results due to unreliable population coverage.
The choice of interval is critical for ensuring the validity and reliability of clinical laboratory reference ranges.