Using a nonlinear mixed model to evaluate three models of human stature

The modern mixed model approach is used to evaluate three current nonlinear models of development of human stature. By combining both fixed and random effects in the same model, the mixed approach incorporates variability bet ween subjects in the estimation of the mean parameter values. This allows u s to provide a single statistical test for the differences between each pai r of statistical models. Asymptotic growth models from Preece and Baines (1 978), Jolicoeur et al. (1988, 1991,1992), and Kanefuji and Shohoji (1990) w ere applied to height data collected from 28 males and 25 females. The NLIN MIX Macro from SAS was used to evaluate the fit of each model allowing for two random components in addition to the fixed mean parameter values. In ev ery case, the addition of random parameters improved the fit of each growth model. Models were evaluated by the calculation of the Akaike Information Criterion, differences in -2 log likelihood, and determination of the resid ual variance. For males, the Jolicoeur et al. model was superior, while for females, the Kanefuji and Shohoji model provided the best fit. This new ap proach is more parsimonious than previous techniques by allowing for indivi dual variation in the estimation of model parameters in a population averag e model of growth.