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Practical modeling strategies for unbalanced longitudinal data analysis

Author

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  • Enrico A. Colosimo
  • Maria Arlene Fausto
  • Marta Afonso Freitas
  • Jorge Andrade Pinto

Abstract

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.

Suggested Citation

  • Enrico A. Colosimo & Maria Arlene Fausto & Marta Afonso Freitas & Jorge Andrade Pinto, 2012. "Practical modeling strategies for unbalanced longitudinal data analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(9), pages 2005-2013, May.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:9:p:2005-2013
    DOI: 10.1080/02664763.2012.699954
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    References listed on IDEAS

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    1. H. Zhang & Y. Xia & R. Chen & D. Gunzler & W. Tang & Xin Tu, 2011. "Modeling longitudinal binomial responses: implications from two dueling paradigms," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2373-2390, December.
    2. Wenzheng Huang & Garrett M. Fitzmaurice, 2005. "Analysis of longitudinal data unbalanced over time," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(1), pages 135-155, February.
    3. Wenqin Pan & Donglin Zeng & Xihong Lin, 2009. "Estimation in Semiparametric Transition Measurement Error Models for Longitudinal Data," Biometrics, The International Biometric Society, vol. 65(3), pages 728-736, September.
    4. Patrick J. Heagerty, 2002. "Marginalized Transition Models and Likelihood Inference for Longitudinal Categorical Data," Biometrics, The International Biometric Society, vol. 58(2), pages 342-351, June.
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