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.
|Author||Alireza S. Mahani, Mansour T.A. Sharabiani|
|Date of publication||2016-09-21 08:06:29|
|Maintainer||Alireza S. Mahani <firstname.lastname@example.org>|
|License||GPL (>= 2)|
bsgw: Bayesian Survival using Generalized Weibull Regression
crossval_bsgw: Convenience functions for cross-validation-based selection of...
plot_bsgw: Plot diagnostics for a bsgw object
predict_bsgw: Predict method for bsgw model fits
summary_bsgw: Summarizing Bayesian Survival Generalized Weibull (BSGW)...