  Multiple outcomes, both continuous and discrete, are routinely gathered on
subjects in longitudinal studies and during routine clinical follow-up in
general. To motivate our work, we consider a longitudinal study on patients
with primary biliary cirrhosis (PBC) with a continuous bilirubin level, a
discrete platelet count and a dichotomous indication of blood vessel
malformations as examples of such longitudinal outcomes. An apparent
requirement is to use all the outcome values to classify the subjects into
groups (e.g., groups of subjects with a similar prognosis in a clinical
setting). In recent years, numerous approaches have been suggested for
classification based on longitudinal (or otherwise correlated) outcomes,
targeting not only traditional areas like biostatistics, but also rapidly
evolving bioinformatics and many others. However, most available approaches
consider only continuous outcomes as a basis for classification, or if
noncontinuous outcomes are considered, then not in combination with other
outcomes of a different nature. Here, we propose a statistical method for
clustering (classification) of subjects into a prespecified number of groups
with a priori unknown characteristics on the basis of repeated measurements of
several longitudinal outcomes of a different nature. This method relies on a
multivariate extension of the classical generalized linear mixed model where a
mixture distribution is additionally assumed for random effects. We base the
inference on a Bayesian specification of the model and simulation-based Markov
chain Monte Carlo methodology. To apply the method in practice, we have
prepared ready-to-use software for use in R (http://www.R-project.org). We also
discuss evaluation of uncertainty in the classification and also discuss usage
of a recently proposed methodology for model comparison - the selection of a
number of clusters in our case - based on the penalized posterior deviance
proposed by Plummer [Biostatistics 9 (2008) 523-539].
