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Biostatistics Advance Access published online on June 5, 2006
Biostatistics, doi:10.1093/biostatistics/kxl003
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org
Received June 23, 2004
Accepted May 10, 2006

Article

A model-based approach to Bayesian classification with applications to predicting pregnancy outcomes from longitudinal {beta}-hCG profiles

Rolando De la Cruz-Mesía 1 * and Fernando A. Quintana 1

1 Departamento de Estadística, Facultad de Matemáticas, Pontificia Universidad Católica de Chile, Casilla 306, Correo 22, Santiago, CHILE

* To whom correspondence should be addressed.
Rolando De la Cruz-Mesía, E-mail: rolando{at}mat.puc.cl


  Abstract

This paper discusses Bayesian statistical methods for the classification of observations into two or more groups based on hierarchical models for nonlinear longitudinal profiles. Parameter estimation for a discriminant model that classifies individuals into distinct predefined groups or populations uses appropriate posterior simulation schemes. The methods are illustrated with data from a study involving 173 pregnant women. The main objective in this study is to predict normal versus abnormal pregnancy outcomes from beta human chorionic gonadotropin ({beta}-hCG) data available at early stages of pregnancy.

Keywords: Discriminant Analysis; Longitudinal Data; Nonlinear hierarchical models.
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