.png){kind=icon}
Return
Bivariate linear mixed models using SAS proc MIXED
.png){kind=icon}
OA
AI
DOI：10.1016/S0169-2607(02)00017-2.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.
Keywords:
bivariate random effects model
bivariate first order auto-regressive process
SAS proc MIXED
HIV infection
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.8
Papers:
7.0K
Citations:
2.1W
Organization
No organization information available
Cited Papers
Tuning the water permeability of ultra-high molecular weight polyethylene microporous membrane by molecular self-assembly and flow field.png)
Polymer
PolymerIF0
PREAI
ShareSaveShareSaveShareSaveShareSaveShareSave
no more
no more