Predictive functional ANOVA models for longitudinal analysis of mandibular shape changes

Biom J. 2019 Jul;61(4):918-933. doi: 10.1002/bimj.201800228. Epub 2019 Mar 13.


In this paper, we introduce a Bayesian statistical model for the analysis of functional data observed at several time points. Examples of such data include the Michigan growth study where we wish to characterize the shape changes of human mandible profiles. The form of the mandible is often used by clinicians as an aid in predicting the mandibular growth. However, whereas many studies have demonstrated the changes in size that may occur during the period of pubertal growth spurt, shape changes have been less well investigated. Considering a group of subjects presenting normal occlusion, in this paper we thus describe a Bayesian functional ANOVA model that provides information about where and when the shape changes of the mandible occur during different stages of development. The model is developed by defining the notion of predictive process models for Gaussian process (GP) distributions used as priors over the random functional effects. We show that the predictive approach is computationally appealing and that it is useful to analyze multivariate functional data with unequally spaced observations that differ among subjects and times. Graphical posterior summaries show that our model is able to provide a biological interpretation of the morphometric findings and that they comprehensively describe the shape changes of the human mandible profiles. Compared with classical cephalometric analysis, this paper represents a significant methodological advance for the study of mandibular shape changes in two dimensions.

Keywords: Bayesian inference; Gaussian processes; analysis of variance; cephalometrics; functional data; mandible; morphometrics.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biometry / methods*
  • Humans
  • Longitudinal Studies
  • Mandible / anatomy & histology*
  • Mandible / growth & development
  • Models, Statistical*
  • Normal Distribution