Search PubMed⌕ Search
Home/Archive/PubMed

PubMed · 12204452

Bivariate linear mixed models using SAS proc MIXED.

Rodolphe Thiébaut·Hélène Jacqmin-Gadda·Geneviève Chêne·Catherine Leport·Daniel Commenges
View original source ↗Export metadata ↓

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.

Explore related subjects

Antiretroviral Therapy, Highly ActiveCD4 Lymphocyte CountHIV InfectionsHumansLinear ModelsLongitudinal StudiesRNA, ViralSoftwareJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

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

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2007

Random changepoint modelling of HIV immunologic responses.

We propose a changepoint model for the analysis of longitudinal CD4 T-cell counts for HIV infected subjects following highly active antiretroviral treatment. The profile of CD4 counts for each subject follows a simple, 'broken stick' changepoint model, with random subject-specific parameters, including the changepoint. The model accounts for baseline covariates. The longitudinal CD4 records are censored at the time of the subject going off-study-treatment. This is a potentially informative drop-out mechanism, which we address by modelling it jointly with the CD4 count outcome. The drop-out model incorporates terms from the CD4 model, including the changepoint. The estimation is done in a Bayesian framework, with implementation via Markov chain Monte Carlo methods in the WinBUGS software. Model selection using DIC indicates that the data support the complex random changepoint and informative censoring model.

Antiretroviral Therapy, Highly Active↗
PubMed2007

Estimation and prediction with HIV-treatment interruption data.

We consider longitudinal clinical data for HIV patients undergoing treatment interruptions. We use a nonlinear dynamical mathematical model in attempts to fit individual patient data. A statistically-based censored data method is combined with inverse problem techniques to estimate dynamic parameters. The predictive capabilities of this approach are demonstrated by comparing simulations based on estimation of parameters using only half of the longitudinal observations to the full longitudinal data sets.

Antiretroviral Therapy, Highly Active↗