A mixture model for the joint analysis of latent developmental trajectories and survival

Rinke H. Klein Entink, Jean-Paul Fox, Ardo van den Hout

    Research output: Contribution to journalArticleAcademicpeer-review

    11 Citations (Scopus)


    A general joint modeling framework is proposed that includes a parametric stratified survival component for continuous time survival data, and a mixture multilevel item response component to model latent developmental trajectories given mixed discrete response data. The joint model is illustrated in a real data setting, where the utility of longitudinally measured cognitive function as a predictor for survival is investigated in a group of elderly persons. The object is partly to determine whether cognitive impairment is accompanied by a higher mortality rate. Time-dependent cognitive function is measured using the generalized partial credit model given occasion-specific mini-mental state examination response data. A parametric survival model is applied for the survival information, and cognitive function as a continuous latent variable is included as a time-dependent explanatory variable along with other explanatory information. A mixture model is defined, which incorporates the latent developmental trajectory and the survival component. The mixture model captures the heterogeneity in the developmental trajectories that could not be fully explained by the multilevel item response model and other explanatory variables. A Bayesian modeling approach is pursued, where a Markov chain Monte Carlo algorithm is developed for simultaneous estimation of the joint model parameters. Practical issues as model building and assessment are addressed using the DIC and various posterior predictive tests.
    Original languageEnglish
    Pages (from-to)2310-2325
    JournalStatistics in medicine
    Issue number18
    Publication statusPublished - 2011


    • Joint longitudinal and survival modeling
    • MCMC
    • Mixture models
    • MMSE
    • Developmental trajectory modeling
    • Bayesian item response modeling


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