Linear mixed models for longitudinal shape data with applications to facial modelling

Barry, S.J.E. and Bowman, A.W. (2008) Linear mixed models for longitudinal shape data with applications to facial modelling. Biostatistics, 9(3), pp. 555-565. (doi:10.1093/biostatistics/kxm056)

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Abstract

We present a novel application of methods for analysis of high-dimensional longitudinal data to a comparison of facial shape over time between babies with cleft lip and palate and similarly aged controls. A pairwise methodology is used that was introduced in Fieuws and Verbeke (2006) in order to apply a linear mixed-effects model to data of high dimensions, such as describe facial shape. The approach involves fitting bivariate linear mixed-effects models to all the pairwise combinations of responses, where the latter result from the individual coordinate positions, and aggregating the results across repeated parameter estimates (such as the random-effects variance for a particular coordinate). We describe one example using landmarks and another using facial curves from the cleft lip study, the latter using B-splines to provide an efficient parameterization. The results are presented in 2 dimensions, both in the profile and in the frontal views, with bivariate confidence intervals for the mean position of each landmark or curve, allowing objective assessment of significant differences in particular areas of the face between the 2 groups. Model comparison is performed using Wald and pseudolikelihood ratio tests.

Item Type:Articles
Keywords:Curves, Mixed models, Multivariate longitudinal profiles, Pairwise modelling, Shape analysis
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Bowman, Professor Adrian and Barry, Dr Sarah
Authors: Barry, S.J.E., and Bowman, A.W.
Subjects:Q Science > QA Mathematics
College/School:College of Medical Veterinary and Life Sciences > Institute of Health and Wellbeing > Robertson Centre
College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Biostatistics
ISSN:1465-4644
ISSN (Online):1468-4357
Published Online:05 February 2008

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