Discriminant Analysis for Longitudinal Data with Multiple Continuous Responses and Possibly Missing Data
Version of Record online: 24 MAR 2008
© 2008, The International Biometric Society
Volume 65, Issue 1, pages 69–80, March 2009
How to Cite
Marshall, G., De la Cruz-Mesía, R., Quintana, F. A. and Barón, A. E. (2009), Discriminant Analysis for Longitudinal Data with Multiple Continuous Responses and Possibly Missing Data. Biometrics, 65: 69–80. doi: 10.1111/j.1541-0420.2008.01016.x
- Issue online: 17 MAR 2009
- Version of Record online: 24 MAR 2008
- Received January 2006. Revised December 2007. Accepted January 2008.
- Discriminant analysis;
- Joint modeling;
- Missing data;
- Multivariate responses;
- Nonlinear mixed-effects models
Summary 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.