Practical modeling strategies for unbalanced longitudinal data analysis
In practice, data are often measured repeatedly on the same individual at several points in time. Main interest often relies in characterizing the way the response changes in time, and the predictors of that change. Marginal, mixed and transition are frequently considered to be the main models for continuous longitudinal data analysis. These approaches are proposed primarily for balanced longitudinal design. However, in clinic studies, data are usually not balanced and some restrictions are necessary in order to use these models. This paper was motivated by a data set related to longitudinal height measurements in children of HIV-infected mothers that was recorded at the university hospital of the Federal University in Minas Gerais, Brazil. This data set is severely unbalanced. The goal of this paper is to assess the application of continuous longitudinal models for the analysis of unbalanced data set.
Year of publication: |
2012
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Authors: | Colosimo, Enrico A. ; Fausto, Maria Arlene ; Freitas, Marta Afonso ; Pinto, Jorge Andrade |
Published in: |
Journal of Applied Statistics. - Taylor & Francis Journals, ISSN 0266-4763. - Vol. 39.2012, 9, p. 2005-2013
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Publisher: |
Taylor & Francis Journals |
Saved in:
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