A multivariate Bayesian model for embryonic growth.

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 Med

journal_title

Statistics in medicine

authors

Willemsen SP,Eilers PH,Steegers-Theunissen RP,Lesaffre E

doi

10.1002/sim.6411

subject

Has Abstract

pub_date

2015-04-15 00:00:00

pages

1351-65

issue

8

eissn

0277-6715

issn

1097-0258

journal_volume

34

pub_type

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