Latent mixture models for multivariate and longitudinal outcomes

Andrew Pickles (Lead / Corresponding author), Tim Croudace

    Research output: Contribution to journalArticle

    43 Citations (Scopus)

    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.
    Original languageEnglish
    Pages (from-to)271-289
    Number of pages19
    JournalStatistical Methods in Medical Research
    Volume19
    Issue number3
    DOIs
    Publication statusPublished - Jun 2010

    Keywords

    • Epidemiology
    • Health Information Management
    • Statistics and Probability

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