Discriminant analysis for longitudinal data with multiple continuous responses and possibly missing data.
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.
Marshall
Guillermo
G
Departamento de Estadística, Facultad de Matemáticas, Pontificia Universidad Católica de Chile, Casilla 306, Correo 22, Santiago, Chile.
De la Cruz-Mesía
Rolando
R
Quintana
Fernando A
FA
Barón
Anna E
AE
D001699:Biometry / Q000379:methods*; D002677:Chile; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D011247:Pregnancy; D011256:Pregnancy Outcome