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Nonlinear growth curve analysis: estimating the population parameters.

C S Berkey, N M Laird

    Annals of Human Biology
    |March 1, 1986
    PubMed
    Summary
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    This study introduces a new model for analyzing longitudinal growth data, accounting for individual variations and covariate effects. It offers improved methods for understanding how covariates influence growth parameters and rates.

    Area of Science:

    • Biostatistics
    • Longitudinal Data Analysis
    • Growth Modeling

    Background:

    • Analyzing longitudinal growth data presents challenges in accounting for individual variability and covariate influences.
    • Existing methods for cross-sectional analyses can obscure the effects of covariates on growth parameters over time.

    Purpose of the Study:

    • To propose a novel model for analyzing longitudinal growth data with covariates.
    • To develop and compare two-stage estimation methods for inferring covariate effects on nonlinear growth models.
    • To introduce a method for validating nonlinear growth models.

    Main Methods:

    • A nonlinear growth model is assumed for individual data, with population-level parameter variation dependent on covariates.
    • Several two-stage estimation methods are described and evaluated for their ability to estimate covariate effects.

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  • A model validation technique for nonlinear models is presented.

    • Main Results:

    • The proposed two-stage methods effectively address inference problems in analyzing covariate effects across age scales.
    • These methods allow for the study of covariate impacts on growth rate and other nonlinear growth parameters.
    • Comparative analysis reveals differential performance among the proposed methods in reproducing covariate effects.

    Conclusions:

    • The developed model and estimation techniques provide a robust framework for analyzing longitudinal growth data with covariates.
    • The two-stage approaches offer advantages over traditional methods for understanding covariate-mediated growth dynamics.
    • The findings highlight the importance of selecting appropriate estimation methods for accurate inference in growth modeling.