Longitudinal measurement in health-related surveys: A Bayesian joint growth model for multivariate ordinal responses

Josine Verhagen, Jean-Paul Fox

    Research output: Contribution to journalArticleAcademicpeer-review

    23 Citations (Scopus)

    Abstract

    Longitudinal surveys measuring physical or mental health status are a common method to evaluate treatments. Multiple items are administered repeatedly to assess changes in the underlying health status of the patient. Traditional models to analyze the resulting data assume that the characteristics of at least some items are identical over measurement occasions. When this assumption is not met, this can result in ambiguous latent health status estimates. Changes in item characteristics over occasions are allowed in the proposed measurement model, which includes truncated and correlated random effects and a growth model for item parameters. In a joint estimation procedure adopting MCMC methods, both item and latent health status parameters are modeled as longitudinal random effects. Simulation study results show accurate parameter recovery. Data from a randomized clinical trial concerning the treatment of depression by increasing psychological acceptance showed significant item parameter shifts. For some items, the probability of responding in the middle category versus the highest or lowest category increased significantly over time. The resulting latent depression scores decreased more over time for the experimental group than for the control group and the amount of decrease was related to the increase in acceptance level. Copyright © 2012 John Wiley & Sons, Ltd.
    Original languageEnglish
    Pages (from-to)2988-3005
    JournalStatistics in medicine
    Volume32
    Issue number17
    DOIs
    Publication statusPublished - 5 Dec 2013

    Keywords

    • Bayesian hierarchical modeling
    • Survey data
    • Mental health
    • Measurement invariance (MI)
    • MCMC
    • Longitudinal data
    • Latent variable models
    • IRT

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