Comparative study of five growth models applied to weight data from congolese infants between birth and 13 months of age

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Abstract
Five growth models are compared using weight data from 95 rural Congolese infants between birth and 13 months of age. The objective is to find the best model in terms of goodness of fit and distribution of parameter estimates. The Infancy component of the Karlberg model, the Count model, and the Kouchi model, which are all three-parameter models, are tested together with the four- and five-parameter versions of the Reed model. The closest fits are obtained using the Reed models, followed by the Karlberg model, while the Count and Kouchi models provide poor fits. The five-parameter Reed model is not superior to the four-parameter version. Examination of mean residuals by age shows a systematic bias in neonatal weight estimation with the three-parameter models. Mean within- and between-individual correlations are especially high for the Kouchi and Reed models. Extreme skewness is observed for some parameters of the Kouchi model and of the five-parameter Reed model. Despite its high degree of collinearity, the four-parameter linear Reed model should be preferred on weight data between birth and 1 year. The I-component of the Karlberg model could be used between ages 2 and 12 months.