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    Bayesian Estimation of Two-Part Joint Models for a Longitudinal Semicontinuous Biomarker and a Terminal Event with R-INLA: Interests for Cancer Clinical Trial Evaluation

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    Type
    Preprint
    Authors
    Rustand, Denis
    Niekerk, Janet van
    Rue, Haavard cc
    Tournigand, Christophe
    Rondeau, Virginie
    Briollais, Laurent
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    Date
    2020-10-26
    Permanent link to this record
    http://hdl.handle.net/10754/665796
    
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    Abstract
    Two-part joint model for a longitudinal semicontinuous biomarker and a terminal event has been recently introduced based on frequentist computation. The biomarker distribution is decomposed into a probability of positive value and the expected value among positive values. Shared random effects can represent the association structure between the biomarker and the terminal event. The computational burden increases compared to standard joint models with a single regression model for the biomarker. In this context, the frequentist estimation implemented in the R package frailtypack can be challenging for complex models (i.e., large number of parameters and dimension of the random effects). As an alternative, we propose a Bayesian estimation of two-part joint models based on the Integrated Nested Laplace Approximation (INLA) algorithm to alleviate the computational burden and be able to fit more complex models. Our simulation studies show that R-INLA reduces the computation time substantially as well as the variability of the estimates and improves the model convergence compared to frailtypack. We contrast the Bayesian and frequentist approaches in two randomized cancer clinical trials (GERCOR and PRIME studies), where R-INLA suggests a stronger association between the biomarker and the risk of event. Moreover, the Bayesian approach was able to characterize subgroups of patients associated with different responses to treatment in the PRIME study where frailtypack had convergence issues. Our study suggests that the Bayesian approach using R-INLA algorithm enables broader applications of the two-part joint model to clinical applications.
    Publisher
    arXiv
    arXiv
    2010.13704
    Additional Links
    https://arxiv.org/pdf/2010.13704
    Collections
    Preprints; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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