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)|
|Package repository||View on CRAN|
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