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Discriminant Analysis for Longitudinal Data with Multiple Continuous Responses and Possibly Missing Data
Author(s) -
Marshall Guillermo,
De la CruzMesía Rolando,
Quintana Fernando A.,
Barón Anna E.
Publication year - 2009
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2008.01016.x
Subject(s) - linear discriminant analysis , multivariate statistics , expectation–maximization algorithm , missing data , random effects model , computer science , maximization , flexibility (engineering) , statistics , generalized linear mixed model , population , mixed model , artificial intelligence , econometrics , mathematics , machine learning , maximum likelihood , mathematical optimization , medicine , meta analysis , environmental health
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.