TITLE:
The Effects of Covariance Structures on Modelling of Longitudinal Data
AUTHORS:
Yin Chen, Yu Fei, Jianxin Pan
KEYWORDS:
Linear Mixed Models, Correlated Random Effects, Mean-Covariance Modeling, Longitudinal Data
JOURNAL NAME:
Open Access Library Journal,
Vol.2 No.10,
October
28,
2015
ABSTRACT: Extending the general linear model to the linear mixed model takes into
account the within-subject correlation between observations with introduction
of random effects. Fixed covariance structures of random error and random
effect are assumed in linear mixed models. However, a potential risk of model
selection still exists. That is, if the specified structure is not appropriate
to real data, we cannot make correct statistical inferences. Joint modelling
method removes all specifications about covariance structures and comes over
the above risk. It simply models covariance structures just like modelling the
mean structures in the general linear model. Our conclusions include: a) The estimators
of fixed effects parameters are similar, that is, the expected mean values of
response variables are similar. b) The standard deviations from different
models are obviously different, which
indicates that the width of confidence interval is evidently different. c) Through
comparing the AIC or BIC value, we conclude that the data-driven
mean-covariance regression model can fit data much better and result in more precise
and reliable statistical inferences.