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Related Experiment Videos

A computer program for estimation of reference percentile values in laboratory data.

R G Rossing, W E Hatcher

    Computer Programs in Biomedicine
    |January 1, 1979
    PubMed
    Summary

    Physicians can better interpret clinical laboratory results using percentile rankings. A new FORTRAN program estimates these reference percentiles using regression, offering more stable and less biased results than traditional interpolation methods.

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    Area of Science:

    • Clinical laboratory science
    • Biostatistics
    • Medical informatics

    Background:

    • Clinical laboratory results are conventionally reported in standard units.
    • Interpreting these results can be challenging without context of a reference population.
    • Percentile rankings offer a valuable supplementary method for result interpretation.

    Purpose of the Study:

    • To describe a FORTRAN program for estimating reference percentile values.
    • To provide physicians with enhanced data interpretation tools for clinical laboratory results.
    • To improve the accuracy and stability of percentile estimations.

    Main Methods:

    • Development of a FORTRAN program for percentile estimation.
    • Utilizing regression analysis over 20% of the distribution for estimation.

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  • Comparison of regression-based estimation with interpolation methods.

    • Main Results:

    • The FORTRAN program effectively estimates reference percentile values.
    • Regression-based estimates demonstrated negligible bias.
    • Percentile estimates derived from regression were more stable than those from interpolation.

    Conclusions:

    • The described FORTRAN program provides a robust method for estimating clinical laboratory result percentiles.
    • Regression-based percentile estimation offers advantages in stability and bias over interpolation.
    • This approach can aid physicians in more accurate interpretation of laboratory data.