Bayesian Cure Rate Modeling for Time-to-Event Data
Overview
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.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK14 OK2026-04-22
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- ERROR13 OK, 1 ERROR2026-04-18
- OK14 OK2026-03-10
Documentation
- Help pages
- 26includes a package overview page
- Examples that run
- 100%21 run in a plain check
- Exported functions with examples
- not tracked
- Documented parameters
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- Return-value docs
- not tracked
- Help pages citing references
- 58%
Downloads
Repository
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Checks run against github.com/mqbssppe/bayesian_cure_rate_model on 2026-10-18.
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Dependencies
Installing bayesCureRateModel adds 54 packages (32 with compiled code, 50 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB.
Code & Tests
Datasets
People & History
Package metadata
- First published
- 2024-06-27
- Total releases
- 7 / 2 yrs
- License
- GPL-2 OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 21 KB / 2 files
- Download size
- 76 KB
Cite
Cite this package
Run in R for the authors' entry:
citation("bayesCureRateModel")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1007/s11749-024-00942-w.
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