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    This summary is machine-generated.

    Estimating reference ranges for healthy individuals is crucial for clinical decisions. This study introduces three methods (frequentist, Bayesian, empirical) to accurately calculate these ranges from meta-analyses, improving generalizability.

    Keywords:
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    Area of Science:

    • Biostatistics
    • Clinical Epidemiology
    • Medical Informatics

    Background:

    • Reference ranges define normal health, crucial for clinical interpretation of patient measurements.
    • Accurate reference ranges require generalizable data, often achieved through meta-analysis.
    • Existing meta-analysis intervals (confidence, prediction) do not capture healthy individual variation.

    Purpose of the Study:

    • To present three novel methods for estimating reference ranges from meta-analyses.
    • To incorporate both within- and between-study variations in reference range estimation.
    • To provide a guide applicable to aggregate and individual-participant data meta-analyses.

    Main Methods:

    • Frequentist approach for reference range estimation.
    • Bayesian approach for reference range estimation.
    • Empirical approach for reference range estimation.
    • Application to individual-participant data meta-analysis for robust results.

    Main Results:

    • Demonstrated three distinct methods for estimating reference ranges from meta-analysis data.
    • Highlighted the importance of accounting for within- and between-study variability.
    • Illustrated methods using liver stiffness data from a meta-analysis of healthy individuals (2006-2016).

    Conclusions:

    • The proposed methods offer improved accuracy in reference range estimation from meta-analyses.
    • Individual-participant data meta-analysis serves as the gold standard for these estimations.
    • Accurate reference ranges enhance clinical decision-making for healthy populations.