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Bivariate linear mixed models using SAS proc MIXED.

Rodolphe Thiébaut1, Hélène Jacqmin-Gadda, Geneviève Chêne

  • 1INSERM Unité 330, ISPED, Université Victor Segalen Bordeaux II, 146, rue Léo Saignat 33076, Cedex, Bordeaux, France. rodolphe.thiebaut@isped.u-bordeaux2.fr

Computer Methods and Programs in Biomedicine
|September 3, 2002
PubMed
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This study introduces a bivariate linear mixed model for analyzing two associated markers in longitudinal data. The model, implemented in SAS Proc MIXED, offers a flexible approach for complex data analysis.

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Longitudinal studies often involve analyzing multiple associated markers over time.
  • Standard statistical models may not adequately capture the complex correlation structures in such data.

Purpose of the Study:

  • To present a bivariate linear mixed model suitable for longitudinal data with two associated markers.
  • To provide practical guidance and code for implementing these models using SAS Proc MIXED.
  • To illustrate the model's application with a real-world example in HIV infection research.

Main Methods:

  • Development of a bivariate linear mixed model incorporating random effects and autoregressive processes.
  • Utilizing SAS Proc MIXED for model fitting, including specific coding strategies.
  • Discussion of the model's limitations and potential extensions.

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Main Results:

  • The proposed bivariate linear mixed model effectively analyzes longitudinal data with two associated markers.
  • SAS Proc MIXED provides a viable tool for fitting these complex models.
  • The model was successfully applied to a dataset concerning HIV infection.

Conclusions:

  • Bivariate linear mixed models are valuable for analyzing associated markers in longitudinal studies.
  • SAS Proc MIXED is a practical and adaptable tool for implementing these models.
  • The methodology shows promise for extension to multivariate longitudinal data analysis.