Infant growth modelling and assessment of growth

Ken J. Beath*

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

    4 Citations (Scopus)


    Assessment of factors influencing infant growth is best performed using a modelling approach; however this is difficult due to the high initial rate of growth and wide variability. The aim is to obtain a model which produces a good fit to the data with a minimum number of parameters. A number of parametric models have been used, motivated mainly by ability to fit data, rather than biological considerations. Biologically it is unlikely that growth can be modelled by a simple function, so a semi-parametric model appears more appropriate and may produce more interpretable parameters. A semi-parametric model is based around a flexible shape which is common to all subjects, combined with parameters that transform the curve for individual subjects, with only few models of this type available. When fitting models, a mixed effects model approach is preferred, rather than fitting subjects individually. Covariates may be included in the models as either time-independent or time-dependent covariates, but interpretation may be difficult for time-dependent covariates. The models are compared using data on weight and length in the first 2 years of life. A semi-parametric model, the shape invariant model, had similar fit to the Jenss–Bayley model, but with more easily interpretable parameters. A quartic (fourth degree) polynomial did have a superior fit but at the expense of a larger number of parameters, and possible overfitting.
    Original languageEnglish
    Title of host publicationHandbook of growth and growth monitoring in health and disease
    EditorsVictor R Preedy
    Place of PublicationNew York, NY
    PublisherSpringer, Springer Nature
    Number of pages12
    ISBN (Electronic)9781441917959
    ISBN (Print)9781441917942
    Publication statusPublished - 1 Jan 2012


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