TY - JOUR

T1 - Discriminant analysis for longitudinal data with multiple continuous responses and possibly missing data

AU - Marshall, Guillermo

AU - De La Cruz-Mesía, Rolando

AU - Quintana, Fernando A.

AU - Barón, Anna E.

PY - 2009/3

Y1 - 2009/3

N2 - 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.

AB - 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.

KW - Discriminant analysis

KW - Joint modeling

KW - Missing data

KW - Multivariate responses

KW - Nonlinear mixed-effects models

UR - http://www.scopus.com/inward/record.url?scp=62749090547&partnerID=8YFLogxK

U2 - 10.1111/j.1541-0420.2008.01016.x

DO - 10.1111/j.1541-0420.2008.01016.x

M3 - Article

C2 - 18363774

AN - SCOPUS:62749090547

SN - 0006-341X

VL - 65

SP - 69

EP - 80

JO - Biometrics

JF - Biometrics

IS - 1

ER -