Model uncertainty quantification in Cox regression

We consider covariate selection and the ensuing model uncertainty aspects in the context of Cox regression. The perspective we take is probabilistic, and we handle it within a Bayesian framework. One of the critical elements in variable/model selection is choosing a suitable prior for model parameters. Here, we derive the so-called conventional prior approach and propose a comprehensive implementation that results in an automatic procedure. Our simulation studies and real applications show improvements over existing literature. For the sake of reproducibility but also for its intrinsic interest for practitioners, a web application requiring minimum statistical knowledge implements the proposed approach.

Automated summary

This study addresses covariate selection and model uncertainty in Cox regression using a probabilistic approach within a Bayesian framework. The selection of suitable prior for model parameters is a critical element, and we derive the conventional prior approach and propose a comprehensive implementation for automatic selection. Our simulation studies and real applications demonstrate improvements over existing literature. To enhance reproducibility and appeal to practitioners, a web application with minimal statistical knowledge requirement is developed to implement the proposed approach.