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Two-part joint models for a longitudinal semicontinuous biomarker and aterminal event have been recently introduced based on frequentist estimation.The biomarker distribution is decomposed into a probability of positive valueand the expected value among positive values. Shared random effects canrepresent the association structure between the biomarker and the terminalevent. The computational burden increases compared to standard joint modelswith a single regression model for the biomarker. In this context, thefrequentist estimation implemented in the R package frailtypack can bechallenging for complex models (i.e., large number of parameters and dimensionof the random effects). As an alternative, we propose a Bayesian estimation oftwo-part joint models based on the Integrated Nested Laplace Approximation(INLA) algorithm to alleviate the computational burden and fit more complexmodels. Our simulation studies confirm that INLA provides accurateapproximation of posterior estimates and to reduced computation time andvariability of estimates compared to frailtypack in the situations considered.We contrast the Bayesian and frequentist approaches in the analysis of tworandomized cancer clinical trials (GERCOR and PRIME studies), where INLA has areduced variability for the association between the biomarker and the risk ofevent. Moreover, the Bayesian approach was able to characterize subgroups ofpatients associated with different responses to treatment in the PRIME study.Our study suggests that the Bayesian approach using INLA algorithm enables tofit complex joint models that might be of interest in a wide range of clinicalapplications.

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