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Faltering growth among children is a nutritional problem prevalent in low tomedium income countries; it is generally defined as a slower rate of growthcompared to a reference healthy population of the same age and gender. Asfaltering is closely associated with reduced physical, intellectual andeconomic productivity potential, it is important to identify faltered childrenand be able to characterise different growth patterns so that targetedtreatments can be designed and administered. We introduce a multiclassclassification model for growth trajectory that flexibly extends a currentclassification approach called the broken stick model, which is a piecewiselinear model with breaks at fixed knot locations. Heterogeneity in growthpatterns among children is captured using mixture distributed random effects,whereby the mixture components determine the classification of children intosubgroups. The mixture distribution is modelled using a Dirichlet processprior, which avoids the need to choose the "true" number of mixture components,and allows this to be driven by the complexity of the data. Because childrenhave individual differences in the onset of growth stages, we introducechild-specific random change points. Simulation results show that the randomchange point model outperforms the broken stick model because it has fewerrestrictions on knot locations. We illustrate our model on a longitudinal birthcohort from the Healthy Birth, Growth and Development knowledge integrationproject funded by the Bill and Melinda Gates Foundation. Analysis reveals 9subgroups of children within the population which exhibit varying falteringtrends between birth and age one.

Multiclass classification of growth curves using random change points and heterogeneous random effects