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We propose a model for unbalanced longitudinal data, where the univariatemargins can be selected arbitrarily and the dependence structure is describedwith the help of a D-vine copula. We show that our approach is an extremelyflexible extension of the widely used linear mixed model if the correlation ishomogeneous over the considered individuals. As an alternative to jointmaximum-likelihood a sequential estimation approach for the D-vine copula isprovided and validated in a simulation study. The model can handle missingvalues without being forced to discard data. Since conditional distributionsare known analytically, we easily make predictions for future events. For modelselection we adjust the Bayesian information criterion to our situation. In anapplication to heart surgery data our model performs clearly better thancompeting linear mixed models.

A D-vine copula based model for repeated measurements extending linear mixed models with homogeneous correlation structure