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Latent mixture models for multivariate and longitudinal outcomes

¹ Biostatistics, Health Methodology Research Group, University of Manchester, University Place, Oxford Road, Manchester, M13 9PL, UK
² Department of Psychiatry, University of Cambridge, Addenbrooke’s Hospital, Hills Road, Cambridge, CB2 2QQ, UK

^* To whom correspondence should be addressed. E-mail: andrew.pickles{at}manchester.ac.uk.

	Abstract

Repeated measures and multivariate outcomes are an increasingly common feature of trials. Their joint analysis by means of random effects and latent variable models is appealing but patterns of heterogeneity in outcome profile may not conform to standard multivariate normal assumptions. In addition, there is much interest in both allowing for and identifying sub-groups of patients who vary in treatment responsiveness. We review methods based on discrete random effects distributions and mixture models for application in this field.

First published on July 16, 2009
Statistical Methods in Medical Research 2009, doi:10.1177/0962280209105016


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