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