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    A Bayesian inference for the penalized spline joint models of longitudinal and time-to-event data: A prior sensitivity analysis

    Access Status
    Fulltext not available
    Authors
    Thi Thu Pham, H.
    Pham, H.
    Nur, Darfiana
    Date
    2020
    Type
    Journal Article
    
    Metadata
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    Citation
    Thi Thu Pham, H. and Pham, H. and Nur, D. 2020. A Bayesian inference for the penalized spline joint models of longitudinal and time-to-event data: A prior sensitivity analysis. Monte Carlo Methods and Applications.26 (1): pp. 49-68.
    Source Title
    Monte Carlo Methods and Applications
    DOI
    10.1515/mcma-2020-2058
    ISSN
    0929-9629
    Faculty
    Faculty of Science and Engineering
    School
    School of Elec Eng, Comp and Math Sci (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/88546
    Collection
    • Curtin Research Publications
    Abstract

    Bayesian approaches have been used in the literature to estimate the parameters for joint models of longitudinal and time-to-event data. The main aim of this paper is to analyze the impact of prior distributions on estimating parameters in a proposed fully Bayesian analysis setting for the penalized spline joint models. To achieve this aim, the joint posterior distribution of parameters in survival and longitudinal submodels is presented. The Markov chain Monte Carlo (MCMC) algorithm is then proposed, which consists of the Gibbs sampler (GS) and Metropolis Hastings (MH) algorithms to sample for the target conditional posterior distributions. The prior sensitivity analysis for the baseline hazard rate and association parameters is performed through simulation studies and a case study.

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