The linear mixed model is one of the common models used to analyze the longitudinal data;it may comprise of separate (Univariate), joint Bivariate, and joint Multivariate linear mixed model, which is predicted on the number of response variables incorporated in the analysis. Adjusting for correlation matrix and covariance matrix between and within subjects is one reason why modern longitudinal data analysis techniques are deemed more appropriate than some of the previous methods of analysis. Some studies assume that the correlation between observation is zero. However, it is unlikely that repeated measurements on the same individual Will actually be independent. To that end, comparing the different linear mixed models identifying the appropriate model demonstrates that the evolution of patients with congestive heart failure is necessary.
In this study the separate, bivariate, and multivariate linear mixed models were compared with different covariance and correlation structures. Finally, a multivariate linear mixed model with autoregressive order one correlation structure and unstructured covariance structure for random effects, to consider within and between patient's variations, was considered as a best model to depict the evolution of patients with congestive heart failure.