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Bayesian variable selection for a semi-competing risks model with three hazard functions

Author

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  • Chapple, Andrew G.
  • Vannucci, Marina
  • Thall, Peter F.
  • Lin, Steven

Abstract

A variable selection procedure is developed for a semi-competing risks regression model with three hazard functions that uses spike-and-slab priors and stochastic search variable selection algorithms for posterior inference. A rule is devised for choosing the threshold on the marginal posterior probability of variable inclusion based on the Deviance Information Criterion (DIC) that is examined in a simulation study. The method is applied to data from esophageal cancer patients from the MD Anderson Cancer Center, Houston, TX, where the most important covariates are selected in each of the hazards of effusion, death before effusion, and death after effusion. The DIC procedure that is proposed leads to similar selected models regardless of the choices of some of the hyperparameters. The application results show that patients with intensity-modulated radiation therapy have significantly reduced risks of pericardial effusion, pleural effusion, and death before either effusion type.

Suggested Citation

  • Chapple, Andrew G. & Vannucci, Marina & Thall, Peter F. & Lin, Steven, 2017. "Bayesian variable selection for a semi-competing risks model with three hazard functions," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 170-185.
    • Handle: RePEc:eee:csdana:v:112:y:2017:i:c:p:170-185
    DOI: 10.1016/j.csda.2017.03.002
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    References listed on IDEAS

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    1. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    2. Kyu Ha Lee & Sebastien Haneuse & Deborah Schrag & Francesca Dominici, 2015. "Bayesian semiparametric analysis of semicompeting risks data: investigating hospital readmission after a pancreatic cancer diagnosis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 64(2), pages 253-273, February.
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    Cited by:

    1. Xifen Huang & Jinfeng Xu & Hao Guo & Jianhua Shi & Wenjie Zhao, 2022. "An MM Algorithm for the Frailty-Based Illness Death Model with Semi-Competing Risks Data," Mathematics, MDPI, vol. 10(19), pages 1-13, October.

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