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Biostatistics Advance Access first published online on June 5, 2006
This version published online on March 5, 2007
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.

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

Rolando de la Cruz-Mesía* and Fernando A. Quintana

Departamento de Estadística, Facultad de Matemáticas, Pontificia Universidad Católica de Chile, Casilla 306, Correo 22, Santiago, Chile rolando{at}mat.puc.cl

* To whom correspondence should be addressed.

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 data available at early stages of pregnancy.

Keywords: Discriminant analysis; Longitudinal data; Nonlinear hierarchical models

Received June 23, 2004; revised April 25, 2005; revised December 15, 2005; revised April 12, 2006; accepted for publication May 10, 2006.


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