Evaluation of the Pairwise Approach for Fitting Joint Linear Mixed Models: A Simulation Study

Abstract

A mixed model is a flexible tool for joint modelling purposes, especially when the gathered data are unbalanced. However, computational problems due to the dimension of the joint covariance matrix of the random effects arise as soon as the number of outcomes and/or the number of used random effects per outcome increases. We propose a pairwise approach in which all possible bivariate models are fitted, and where inference follows from pseudo-likelihood arguments. The approach is applicable for linear, generalised linear and nonlinear mixed models, or for combinations of these. This paper evalu-ates the performance of the pairwise approach for joint linear mixed models using a set of simulation studies