A fully Bayesian approach in order to estimate a general family of cure rate models under the presence of covariates, see Papastamoulis and Milienos, 2024a and Papastamoulis and Milienos, 2024b.
The promotion time can be modelled
In both cases, user-defined families of distributions are allowed under some specific requirements. Posterior inference is carried out by constructing a Metropolis-coupled Markov chain Monte Carlo (MCMC) sampler, which combines Gibbs sampling for the latent cure indicators and Metropolis-Hastings steps with Langevin diffusion dynamics for parameter updates. The main MCMC algorithm is embedded within a parallel tempering scheme by considering heated versions of the target posterior distribution.
The R package bayesCureRateModel package is available on CRAN. The latest version is 1.4 (18/6/2025).
Papastamoulis P and Milienos FS (2024a). Bayesian inference and cure rate modeling for event history data. TEST.
Papastamoulis P and Milienos FS (2024b). bayesCureRateModel: Bayesian Cure Rate Modeling for Time to Event Data in R. arXiv pre-print.
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