jmpost is currently under development and not yet testable.
The goal of the jmpost package is to fit joint models involving:
- a parametric time-to-event sub-model,
- a nonlinear (or linear) mixed-effect sub-model, describing individual time profiles (i.e. trajectories) for a continuous marker,
- a link function (a.k.a. association term). More specifically, the model implemented in this package utilizes a modeling framework described previously [1-3] to link overall survival to tumor size data in oncology clinical trials.
[1] Tardivon et al. Association between tumor size kinetics and survival in patients with urothelial carcinoma treated with atezolizumab: Implications for patient follow-up. Clin Pharm Ther, 2019.
[2] Kerioui et al. Bayesian inference using Hamiltonian Monte-Carlo algorithm for nonlinear joint modeling in the context of cancer immunotherapy. Stat in Med, 2020.
[3] Kerioui et al. Modelling the association between biomarkers and clinical outcome: An introduction to nonlinear joint models. Br J Clin Pharm, 2022.
The models are implemented in STAN, and the package provides a flexible user interface.
Please reach out to us via issues or email (see the DESCRIPTION file) if you have comments or questions, thank you!