Model selection techniques for the covariance matrix for incomplete longitudinal data

In longitudinal studies with incomplete data, where the number of time points can become numerous, it is often advantageous to model the covariance matrix. We describe several covariance models (for example, mixed models, compound symmetry, AR(1)-type models, and combination models) that offer parsimonious alternatives to unstructured sigma. We evaluate each covariance model with longitudinal data concerning cholesterol as the repeated outcome measure. We discuss strategies for deciding the 'best' model and show a graphical technique for judging goodness-of-fit of covariance models.