Bivariate linear mixed models using SAS proc MIXED.

Rodolphe Thiébaut; Hélène Jacqmin-Gadda; Geneviève Chêne; Catherine Leport; Daniel Commenges

doi:10.1016/s0169-2607(02)00017-2

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Bivariate linear mixed models using SAS proc MIXED.

Rodolphe Thiébaut·Hélène Jacqmin-Gadda·Geneviève Chêne·Catherine Leport·Daniel Commenges

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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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Antiretroviral Therapy, Highly Active CD4 Lymphocyte Count HIV Infections Humans Linear Models Longitudinal Studies RNA, Viral Software Journal Article

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Rodolphe Thiébaut, Hélène Jacqmin-Gadda, Geneviève Chêne, Catherine Leport, Daniel Commenges. 2002. Bivariate linear mixed models using SAS proc MIXED.. https://doi.org/10.1016/s0169-2607(02)00017-2

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