Bayesian survival model using Weibull regression on both scale and shape parameters. Dependence of shape parameter on covariates permits deviation from proportional-hazard assumption, leading to dynamic - i.e. non-constant with time - hazard ratios between subjects. Bayesian Lasso shrinkage in the form of two Laplace priors - one for scale and one for shape coefficients - allows for many covariates to be included. Cross-validation helper functions can be used to tune the shrinkage parameters. Monte Carlo Markov Chain (MCMC) sampling using a Gibbs wrapper around Radford Neal's univariate slice sampler (R package MfUSampler) is used for coefficient estimation.
Package details |
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Author | Alireza S. Mahani, Mansour T.A. Sharabiani |
Maintainer | Alireza S. Mahani <alireza.s.mahani@gmail.com> |
License | GPL (>= 2) |
Version | 0.9.4 |
Package repository | View on CRAN |
Installation |
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