Abstract:
:Most longitudinal growth curve models evaluate the evolution of each of the anthropometric measurements separately. When applied to a 'reference population', this exercise leads to univariate reference curves against which new individuals can be evaluated. However, growth should be evaluated in totality, that is, by evaluating all body characteristics jointly. Recently, Cole et al. suggested the Superimposition by Translation and Rotation (SITAR) model, which expresses individual growth curves by three subject-specific parameters indicating their deviation from a flexible overall growth curve. This model allows the characterization of normal growth in a flexible though compact manner. In this paper, we generalize the SITAR model in a Bayesian way to multiple dimensions. The multivariate SITAR model allows us to create multivariate reference regions, which is advantageous for prediction. The usefulness of the model is illustrated on longitudinal measurements of embryonic growth obtained in the first semester of pregnancy, collected in the ongoing Rotterdam Predict study. Further, we demonstrate how the model can be used to find determinants of embryonic growth.
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Willemsen SP,Eilers PH,Steegers-Theunissen RP,Lesaffre Edoi
10.1002/sim.6411subject
Has Abstractpub_date
2015-04-15 00:00:00pages
1351-65issue
8eissn
0277-6715issn
1097-0258journal_volume
34pub_type
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journal_title:Statistics in medicine
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