Targeted use of growth mixture modeling: a learning perspective

Booil Jo, Robert L. Findling, Chen Pin Wang, Trevor J. Hastie, Eric A. Youngstrom, L. Eugene Arnold, Mary A. Fristad, Sarah Mc Cue Horwitz

    Research output: Contribution to journalArticlepeer-review

    15 Scopus citations

    Abstract

    From the statistical learning perspective, this paper shows a new direction for the use of growth mixture modeling (GMM), a method of identifying latent subpopulations that manifest heterogeneous outcome trajectories. In the proposed approach, we utilize the benefits of the conventional use of GMM for the purpose of generating potential candidate models based on empirical model fitting, which can be viewed as unsupervised learning. We then evaluate candidate GMM models on the basis of a direct measure of success; how well the trajectory types are predicted by clinically and demographically relevant baseline features, which can be viewed as supervised learning. We examine the proposed approach focusing on a particular utility of latent trajectory classes, as outcomes that can be used as valid prediction targets in clinical prognostic models. Our approach is illustrated using data from the Longitudinal Assessment of Manic Symptoms study.

    Original languageEnglish (US)
    Pages (from-to)671-686
    Number of pages16
    JournalStatistics in Medicine
    Volume36
    Issue number4
    DOIs
    StatePublished - Feb 20 2017

    Keywords

    • early prediction
    • growth mixture modeling
    • latent trajectory class
    • sensitivity
    • specificity
    • supervised learning
    • unsupervised learning

    ASJC Scopus subject areas

    • Epidemiology
    • Statistics and Probability

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