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Joint models for discrete longitudinal outcomes in aging research

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  • Ardo Hout
  • Graciela Muniz-Terrera

Abstract

type="main" xml:id="rssc12114-abs-0001"> Given the aging population in the UK, statistical modelling of cognitive function in the older population is of interest. Joint models are formulated for survival and cognitive function in the older population. Because tests of cognitive function often result in discrete outcomes, binomial and beta–binomial mixed effects regression models are applied to analyse longitudinal measurements. Dropout due to death is accounted for by parametric survival models, where the choice of a Gompertz baseline hazard and the specification of the random-effects structure are of specific interest. The measurement model and the survival model are combined in a shared parameter joint model. Estimation is by marginal likelihood. The methods are used to analyse data from the Cambridge City over-75s cohort study and the English Longitudinal Study of Ageing.

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  • Ardo Hout & Graciela Muniz-Terrera, 2016. "Joint models for discrete longitudinal outcomes in aging research," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(1), pages 167-186, January.
    • Handle: RePEc:bla:jorssc:v:65:y:2016:i:1:p:167-186
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    File URL: http://hdl.handle.net/10.1111/rssc.2016.65.issue-1
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    Cited by:

    1. Marie Böhnstedt & Jutta Gampe & Monique A. A. Caljouw & Hein Putter, 2023. "Incorporating delayed entry into the joint frailty model for recurrent events and a terminal event," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(3), pages 585-607, July.

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