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Bayesian approach for a nonlinear growth model

C S Berkey

    Biometrics
    |December 1, 1982
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an empirical Bayes approach for fitting the Jenss growth model to child length data. This method provides more accurate parameter estimates for individual children

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    Area of Science:

    • Pediatrics
    • Biostatistics
    • Growth Modeling

    Background:

    • The Jenss model is a standard for analyzing child growth during the first six years.
    • Current fitting methods, like nonlinear least squares, have limitations for individual parameter estimation.
    • Bayesian methods offer a framework for incorporating prior knowledge to improve parameter estimates.

    Purpose of the Study:

    • To develop an empirical Bayes approach for fitting the Jenss growth model.
    • To estimate the prior distribution of Jenss model parameters using existing data.
    • To provide a method for obtaining accurate individual child growth curve parameters.

    Main Methods:

    • An empirical Bayes framework was established for the Jenss growth model.
    • Prior distributions were derived from a large dataset of nonlinear least squares parameters.

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  • The posterior mode was estimated using an expression proportional to the posterior distribution.

    • Main Results:

    • The empirical Bayes approach successfully estimates the prior distribution of Jenss model parameters.
    • The posterior mode provides accurate Bayes estimates for individual child growth curves.
    • This method refines parameter estimation compared to traditional least squares fitting.

    Conclusions:

    • The developed empirical Bayes method offers a robust alternative for fitting the Jenss growth model.
    • This approach enhances the precision of individual child growth parameter estimation.
    • The findings have implications for longitudinal growth studies in pediatrics.