  Two-part joint models for a longitudinal semicontinuous biomarker and a
terminal event have been recently introduced based on frequentist estimation.
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 fit more complex
models. Our simulation studies confirm that INLA provides accurate
approximation of posterior estimates and to reduced computation time and
variability of estimates compared to frailtypack in the situations considered.
We contrast the Bayesian and frequentist approaches in the analysis of two
randomized cancer clinical trials (GERCOR and PRIME studies), where INLA has a
reduced variability for the 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.
Our study suggests that the Bayesian approach using INLA algorithm enables to
fit complex joint models that might be of interest in a wide range of clinical
applications.
