Infant Growth Modelling and Assessment of Growth
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.
KeywordsQuadratic Polynomial Multivariate Normal Distribution Gompertz Model Typical Subject Infant Growth
The investigators and research staff of CAPS for the supply of data used in the example.
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