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Updated: Jun 21, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
Published on: September 17, 2019
Latent mixture models for multivariate and longitudinal outcomes
1Biostatistics, Health Methodology Research Group, University of Manchester, University Place, Oxford Road, Manchester, M13 9PL, UK. andrew.pickles@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.
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