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Updated: Sep 29, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
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
Abstract:
Bivariate linear mixed models are useful when analyzing longitudinal data of two associated markers. In this paper, we present a bivariate linear mixed model including random effects or first-order auto-regressive process and independent measurement error for both markers. Codes and tricks to fit these models using SAS Proc MIXED are provided. Limitations of this program are discussed and an example in the field of HIV infection is shown. Despite some limitations, SAS Proc MIXED is a useful tool that may be easily extendable to multivariate response in longitudinal studies.
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