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

Rolando De La Cruz-Mesía, Fernando A. Quintana

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

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 data available at early stages of pregnancy.

Original languageEnglish
Pages (from-to)228-238
Number of pages11
JournalBiostatistics
Volume8
Issue number2
DOIs
StatePublished - Apr 2007

Keywords

  • Discriminant analysis
  • Longitudinal data
  • Nonlinear hierarchical models

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