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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
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Interpreting two test results of one analyte from the same individual using bivariate reference values.

Arne Åsberg1, Gunhild Garmo Hov1,2, Gustav Mikkelsen1,2

  • 1Department of Clinical Chemistry, St. Olav's Hospital, Trondheim, Norway.

Scandinavian Journal of Clinical and Laboratory Investigation
|March 8, 2026
PubMed
Summary

Interpreting paired patient test results (x1, x2) can be improved using bivariate reference limits. This novel approach provides a more accurate assessment of patient health status compared to traditional univariate reference limits (RLs) and reference change values (RCVs).

Keywords:
Biological variationMonte Carlo methodbiological variationindividualnormal distributionpopulationreference ranges

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Area of Science:

  • Clinical Chemistry
  • Biostatistics
  • Medical Diagnostics

Background:

  • Physicians often interpret serial test results from the same patient, comparing them to univariate reference limits (RLs) and reference change values (RCVs).
  • Current methods may not fully capture the relationship between paired measurements (x1, x2) taken days or weeks apart.

Purpose of the Study:

  • To introduce and evaluate a novel bivariate approach for interpreting paired analyte measurements (x1, x2) in patients.
  • To compare the efficacy of bivariate percentiles against traditional univariate reference limits and reference change values.

Main Methods:

  • Simulated bivariate reference values (x1, x2) using data on RLs, intraindividual biological variation, and analytical variation.
  • Estimated bivariate percentiles using Mahalanobis distances (MDs) within the simulated reference distribution.
  • Compared the coverage of bivariate percentiles with combined 95% RLs and 95% RCVs.

Main Results:

  • The combination of 95% RLs and 95% RCVs failed to enclose reference values above the 95th bivariate percentile.
  • This traditional approach only enclosed 92-93% of reference values below the bivariate 95th percentile.
  • Bivariate percentiles can be derived from available data for paired measurements.

Conclusions:

  • Bivariate percentiles offer a more accurate method for interpreting paired patient test results (x1, x2).
  • This approach, derived from healthy reference populations, improves the reporting and interpretation of serial laboratory findings.