Add to ORKGBivariate 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
In many biomedical studies, one jointly collects longitudinal continuous, binary, and survival outco...
In many experimental design situations, one or more of the factors in the study may be random factor...
Mixed effects models are widely used for modelling clustered data when there are large variations be...
Bivariate linear mixed models are useful when analyzing longitudinal data of two associated markers....
The objectives of this article are twofold: (a) to outline the basic concepts associated with the li...
Longitudinal studies could be complicated by left-censored repeated measures. For example, in Human ...
This paperback edition is a reprint of the 2000 edition. This book provides a comprehensive treatmen...
Various statistical methods can be used to analyze correlated data from a clinical study. At baselin...
A user-friendly SAS macro application to perform all possible model selection of fixed effects inclu...
The analysis of multileveled data with bivariate outcomes is very common in the fields of education,...
We propose a scaled linear mixed model to assess the effects of exposure and other covariates on mul...
Often, when a response percent change from baseline in a clinical parameter is not normally distribu...
This paper provides motivation for the use of mixed linear models (i.e. fixed and random effects mod...
Various statistical methods can be used to analyze correlated data from a clinical study. At baselin...
The analysis of complex longitudinal data is challenging due to several inherent features: (i) more ...
In many biomedical studies, one jointly collects longitudinal continuous, binary, and survival outco...
In many experimental design situations, one or more of the factors in the study may be random factor...
Mixed effects models are widely used for modelling clustered data when there are large variations be...
Bivariate linear mixed models are useful when analyzing longitudinal data of two associated markers....
The objectives of this article are twofold: (a) to outline the basic concepts associated with the li...
Longitudinal studies could be complicated by left-censored repeated measures. For example, in Human ...
This paperback edition is a reprint of the 2000 edition. This book provides a comprehensive treatmen...
Various statistical methods can be used to analyze correlated data from a clinical study. At baselin...
A user-friendly SAS macro application to perform all possible model selection of fixed effects inclu...
The analysis of multileveled data with bivariate outcomes is very common in the fields of education,...
We propose a scaled linear mixed model to assess the effects of exposure and other covariates on mul...
Often, when a response percent change from baseline in a clinical parameter is not normally distribu...
This paper provides motivation for the use of mixed linear models (i.e. fixed and random effects mod...
Various statistical methods can be used to analyze correlated data from a clinical study. At baselin...
The analysis of complex longitudinal data is challenging due to several inherent features: (i) more ...
In many biomedical studies, one jointly collects longitudinal continuous, binary, and survival outco...
In many experimental design situations, one or more of the factors in the study may be random factor...
Mixed effects models are widely used for modelling clustered data when there are large variations be...