Bayesian Cure Rate Modeling for Time-to-Event Data
tools:::Rd_package_title("bayesCureRateModel")
Logarithm of the complete log-likelihood for the general cure rate mod...
Compute the achieved FDR and TPR
Main function of the package
The basic MCMC scheme.
PDF and CDF of the Dagum distribution
PDF and CDF of a Gamma mixture distribution
PDF and CDF of the Gamma distribution
PDF and CDF of the Gompertz distribution
PDF and CDF of the log-Logistic distribution.
PDF and CDF of the Lomax distribution
Define a finite mixture of a given family of distributions.
PDF and CDF of the Weibull distribution
Extract the log-likelihood.
Plot method
Plot method
Predict method.
Print method
Print method for the predict object
Print method for the summary
Computation of residuals.
Summary method.
Summary method for predictions.
Create a Survival Object
A fully Bayesian approach in order to estimate a general family of cure rate models under the presence of covariates, see Papastamoulis and Milienos (2024) <doi:10.1007/s11749-024-00942-w> and Papastamoulis and Milienos (2024b) <doi:10.48550/arXiv.2409.10221>. The promotion time can be modelled (a) parametrically using typical distributional assumptions for time to event data (including the Weibull, Exponential, Gompertz, log-Logistic distributions), or (b) semiparametrically using finite mixtures of distributions. 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.