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Model uncertainty quantification in Cox regression.

Gonzalo García-Donato1, Stefano Cabras2, María Eugenia Castellanos3

  • 1Department of Economy and Finance, University of Castilla-La Mancha, Albacete, Spain.

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Summary
This summary is machine-generated.

This study introduces an automatic Bayesian approach for covariate selection in Cox regression models, improving model uncertainty handling. The method offers enhanced performance in simulations and real-world applications for practitioners.

Keywords:
Bayesian variable selectionFisher informationconventional priormedian modelsurvival analysis

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Area of Science:

  • Biostatistics
  • Statistical modeling
  • Computational statistics

Background:

  • Cox regression models are widely used for survival analysis.
  • Model uncertainty and covariate selection are critical challenges in Cox regression.
  • Choosing appropriate priors for model parameters significantly impacts Bayesian analyses.

Purpose of the Study:

  • To develop a probabilistic and Bayesian framework for covariate selection in Cox regression.
  • To propose a comprehensive and automatic procedure for prior implementation in model selection.
  • To address model uncertainty inherent in variable selection processes.

Main Methods:

  • Bayesian framework for probabilistic covariate selection.
  • Derivation and implementation of the conventional prior approach.
  • Simulation studies and real-world data applications.

Main Results:

  • The proposed conventional prior approach provides an automatic procedure for covariate selection.
  • Demonstrated improvements over existing methods in simulation studies.
  • Validated effectiveness through real-world data applications, showing enhanced performance.

Conclusions:

  • The automatic Bayesian conventional prior approach effectively handles covariate selection and model uncertainty in Cox regression.
  • The method offers practical advantages for statisticians and researchers.
  • A user-friendly web application enhances reproducibility and accessibility for practitioners.