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Model uncertainty quantification in Cox regression

Author

Listed:
  • Gonzalo García‐Donato
  • Stefano Cabras
  • María Eugenia Castellanos

Abstract

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.

Suggested Citation

  • Gonzalo García‐Donato & Stefano Cabras & María Eugenia Castellanos, 2023. "Model uncertainty quantification in Cox regression," Biometrics, The International Biometric Society, vol. 79(3), pages 1726-1736, September.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:3:p:1726-1736
    DOI: 10.1111/biom.13823
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    References listed on IDEAS

    as
    1. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    2. Liang, Feng & Paulo, Rui & Molina, German & Clyde, Merlise A. & Berger, Jim O., 2008. "Mixtures of g Priors for Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 410-423, March.
    3. Ghosh, Joyee & Clyde, Merlise A., 2011. "Rao–Blackwellization for Bayesian Variable Selection and Model Averaging in Linear and Binary Regression: A Novel Data Augmentation Approach," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 1041-1052.
    4. repec:dau:papers:123456789/3222 is not listed on IDEAS
    5. Chris T. Volinsky & Adrian E. Raftery, 2000. "Bayesian Information Criterion for Censored Survival Models," Biometrics, The International Biometric Society, vol. 56(1), pages 256-262, March.
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