Bivariate linear mixed models using SAS proc MIXED.
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.
INSERM Unité 330, ISPED, Université Victor Segalen Bordeaux II, 146, rue Léo Saignat 33076, Cedex, Bordeaux, France. rodolphe.thiebaut@isped.u-bordeaux2.fr
Rodolphe
R
Thiébaut
Hélène
H
Jacqmin-Gadda
Geneviève
G
Chêne
Catherine
C
Leport
Daniel
D
Commenges
D023241:Antiretroviral Therapy, Highly Active; D018791:CD4 Lymphocyte Count; D015658:HIV Infections / Q000188:drug therapy / Q000276:immunology / Q000821:virology; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies; D012367:RNA, Viral / Q000097:blood; D012984:Software
D012367:RNA, Viral