Clustering for multivariate continuous and discrete longitudinal data

TL;DR

This paper introduces a Bayesian clustering method for longitudinal data with mixed outcome types, enabling classification of subjects based on multiple correlated outcomes of different natures.

Contribution

It develops a multivariate extension of generalized linear mixed models with mixture distributions, allowing for joint analysis of continuous and discrete longitudinal outcomes.

Findings

Provides a Bayesian MCMC-based implementation in R

Enables classification with uncertain group assignment

Includes a model comparison approach for selecting the number of clusters

Abstract

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].