Bivariate linear mixed models using SAS proc MIXED.
R. Thiébaut, H. Jacqmin-Gadda, G. Chêne, C. Leport, and D. Commenges. Computer methods and programs in biomedicine, 69 (3):
249-56(November 2002)4825<m:linebreak></m:linebreak>LR: 20081120; JID: 8506513; 0 (RNA, Viral); HALMS143963; OID: NLM: HALMS143963; OID: NLM: PMC1950934; ppublish;.
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.
INSERM Unite 330, ISPED, Universite Victor Segalen Bordeaux II, 146, rue Leo Saignat 33076, Cedex, Bordeaux, France. rodolphe.thiebaut@isped.u-bordeaux2.fr
%0 Journal Article
%1 Thiebaut2002
%A Thiébaut, Rodolphe
%A Jacqmin-Gadda, Hélène
%A Chêne, Geneviève
%A Leport, Catherine
%A Commenges, Daniel
%D 2002
%J Computer methods and programs in biomedicine
%K AntiretroviralTherapy CD4LymphocyteCount HIVInfections HIVInfections:drugtherapy HIVInfections:immunology HIVInfections:virology HighlyActive Humans LinearModels LongitudinalStudies RNA Software Viral Viral:blood
%N 3
%P 249-56
%T Bivariate linear mixed models using SAS proc MIXED.
%U http://www.ncbi.nlm.nih.gov/pubmed/12204452
%V 69
%X 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.
%@ 0169-2607
@article{Thiebaut2002,
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.},
added-at = {2023-02-03T11:44:35.000+0100},
author = {Thiébaut, Rodolphe and Jacqmin-Gadda, Hélène and Chêne, Geneviève and Leport, Catherine and Commenges, Daniel},
biburl = {https://www.bibsonomy.org/bibtex/246ab4fb6df107b8af75149600487de41/jepcastel},
city = {INSERM Unite 330, ISPED, Universite Victor Segalen Bordeaux II, 146, rue Leo Saignat 33076, Cedex, Bordeaux, France. rodolphe.thiebaut@isped.u-bordeaux2.fr},
interhash = {8f51aa190e146c6919399aab90eebaf8},
intrahash = {46ab4fb6df107b8af75149600487de41},
isbn = {0169-2607},
issn = {0169-2607},
journal = {Computer methods and programs in biomedicine},
keywords = {AntiretroviralTherapy CD4LymphocyteCount HIVInfections HIVInfections:drugtherapy HIVInfections:immunology HIVInfections:virology HighlyActive Humans LinearModels LongitudinalStudies RNA Software Viral Viral:blood},
month = {11},
note = {4825<m:linebreak></m:linebreak>LR: 20081120; JID: 8506513; 0 (RNA, Viral); HALMS143963; OID: NLM: HALMS143963; OID: NLM: PMC1950934; ppublish;},
number = 3,
pages = {249-56},
pmid = {12204452},
timestamp = {2023-02-03T11:44:35.000+0100},
title = {Bivariate linear mixed models using SAS proc MIXED.},
url = {http://www.ncbi.nlm.nih.gov/pubmed/12204452},
volume = 69,
year = 2002
}