Competing regression models for longitudinal data.
Biom J
; 54(2): 214-29, 2012 Mar.
Article
em En
| MEDLINE
| ID: mdl-22522378
ABSTRACT
The choice of an appropriate family of linear models for the analysis of longitudinal data is often a matter of concern for practitioners. To attenuate such difficulties, we discuss some issues that emerge when analyzing this type of data via a practical example involving pretest-posttest longitudinal data. In particular, we consider log-normal linear mixed models (LNLMM), generalized linear mixed models (GLMM), and models based on generalized estimating equations (GEE). We show how some special features of the data, like a nonconstant coefficient of variation, may be handled in the three approaches and evaluate their performance with respect to the magnitude of standard errors of interpretable and comparable parameters. We also show how different diagnostic tools may be employed to identify outliers and comment on available software. We conclude by noting that the results are similar, but that GEE-based models may be preferable when the goal is to compare the marginal expected responses.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Análise de Regressão
/
Estudos Longitudinais
Tipo de estudo:
Diagnostic_studies
/
Observational_studies
Limite:
Child
/
Child, preschool
/
Humans
Idioma:
En
Revista:
Biom J
Ano de publicação:
2012
Tipo de documento:
Article
País de afiliação:
Brasil