Discriminant analysis for longitudinal data with multiple continuous responses and possibly missing data.
Biometrics
; 65(1): 69-80, 2009 Mar.
Article
em En
| MEDLINE
| ID: mdl-18363774
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Análise Discriminante
/
Estudos Longitudinais
/
Biometria
Tipo de estudo:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Female
/
Humans
/
Pregnancy
País/Região como assunto:
America do sul
/
Chile
Idioma:
En
Revista:
Biometrics
Ano de publicação:
2009
Tipo de documento:
Article
País de afiliação:
Chile
País de publicação:
Estados Unidos