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

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.

Citation
Rustand, D., van Niekerk, J., Rue, H., Tournigand, C., Rondeau, V., & Briollais, L. (2023). Bayesian estimation of two-part joint models for a longitudinal semicontinuous biomarker and a terminal event with INLA: Interests for cancer clinical trial evaluation. Biometrical Journal, 2100322. Portico. https://doi.org/10.1002/bimj.202100322

Acknowledgements
This publication is based on research using information obtained from www.projectdatasphere.org, which is maintained by Project Data Sphere, LLC. Neither Project Data Sphere, LLC nor the owner(s) of any information from the web site have contributed to, approved or are in any way responsible for the contents of this publication. The authors acknowledge the insightful and constructive comments made by associate editor and two reviewers. These comments have greatly helped to sharpen the original submission.

Publisher
arXiv

Journal
Accepted by Biometrical Journal

DOI
10.1002/bimj.202100322

arXiv
2010.13704

Additional Links
https://arxiv.org/pdf/2010.13704

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2023-02-06 05:46:25
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2020-11-03 13:56:49
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