Discriminant analysis for longitudinal data with multiple continuous responses and possibly missing data

Guillermo Marshall, Rolando De La Cruz-Mesía, Fernando A. Quintana, Anna E. Barón

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Multiple outcomes are often used to properly characterize an effect of interest. This article discusses model-based statistical methods for the classification of units into one of two or more groups where, for each unit, repeated measurements over time are obtained on each outcome. We relate the observed outcomes using multivariate nonlinear mixed-effects models to describe evolutions in different groups. Due to its flexibility, the random-effects approach for the joint modeling of multiple outcomes can be used to estimate population parameters for a discriminant model that classifies units into distinct predefined groups or populations. Parameter estimation is done via the expectation-maximization algorithm with a linear approximation step. We conduct a simulation study that sheds light on the effect that the linear approximation has on classification results. We present an example using data from a study in 161 pregnant women in Santiago, Chile, where the main interest is to predict normal versus abnormal pregnancy outcomes.

Original languageEnglish
Pages (from-to)69-80
Number of pages12
JournalBiometrics
Volume65
Issue number1
DOIs
StatePublished - Mar 2009

Keywords

  • Discriminant analysis
  • Joint modeling
  • Missing data
  • Multivariate responses
  • Nonlinear mixed-effects models

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