Bayesian Generalized Weibull Regression with Applications to Survival Data

Oregon Convention Center 

We propose a Bayesian generalized Weibull regression method and develop Accelerated Time to Failure models using Bayesian methods. The parameter estimation procedure is carried out using Hamiltonian Monte Carlo algorithm with No-U-Turn Sampler and compares the results of generalized Weibull regression with exponentiated Weibull regression, Weibull regression, and log-normal distribution across simulated and clinical data sets. We examine the effectiveness of generalized Weibull distribution as a survival model and compare it to more studied probability distributions. In addition to monotone and bathtub hazard shapes, the additional shape parameter in the generalized Weibull distribution provides flexibility to model a broader class of monotone hazard rates.

Weibull Regression

Bayesian Inference

Hamiltonian Monte Carlo

No-U-Turn Sampler

Accelerated Failure Time 

Section on Statistics in Epidemiology