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Bayesian Generalized Weibull Regression with Applications to Survival Data

Abstract Number:

3765 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Keshav Pokhrel (1)

Institutions:

(1) University of Michigan-Dearborn, N/A

First Author:

Keshav Pokhrel  
University of Michigan-Dearborn

Presenting Author:

Keshav Pokhrel  
University of Michigan-Dearborn

Abstract Text:

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.

Keywords:

Weibull Regression|Bayesian Inference|Hamiltonian Monte Carlo|No-U-Turn Sampler|Accelerated Failure Time|

Sponsors:

Section on Statistics in Epidemiology

Tracks:

Disease Prediction

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