Competing regression models for longitudinal data

Competing regression models for longitudinal data

Author Alencar, Airlane P. Google Scholar
Singer, Julio M. Google Scholar
Rocha, Francisco Marcelo M. Autor UNIFESP Google Scholar
Institution Universidade de São Paulo (USP)
Universidade Federal de São Paulo (UNIFESP)
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 pretestposttest 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.
Keywords Estimating equations method
Generalized linear models
Longitudinal data
Mixed models
posttest measures
Language English
Sponsor Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Date 2012-03-01
Published in Biometrical Journal. Hoboken: Wiley-Blackwell, v. 54, n. 2, p. 214-229, 2012.
ISSN 0323-3847 (Sherpa/Romeo, impact factor)
Publisher Wiley-Blackwell
Extent 214-229
Access rights Closed access
Type Article
Web of Science ID WOS:000303045200004

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