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Bayesian Analysis of Masked Competing Risks Data Based on Proportional Subdistribution Hazards Model

Author

Listed:
  • Yosra Yousif

    (Department of Mechanical Engineering, Faculty of Engineering, International Islamic University Malaysia (IIUM), P.O. Box 10, Kuala Lumpur 50728, Malaysia)

  • Faiz Elfaki

    (Statistics Program, Department of Mathematics, Statistics and Physics, College of Arts and Sciences, Qatar University, Doha P.O. Box 2713, Qatar)

  • Meftah Hrairi

    (Department of Mechanical Engineering, Faculty of Engineering, International Islamic University Malaysia (IIUM), P.O. Box 10, Kuala Lumpur 50728, Malaysia)

  • Oyelola Adegboye

    (Public Health & Tropical Medicine, College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, QLD 4811, Australia
    Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, QLD 4811, Australia)

Abstract

Masked issues can emerge when dealing with competing risk data. Such issues are exemplified by the cause of a particular failure not being directly exhibited for all units to observe but only proven to be a subset of possible causes of failure. For assessing the impact of explanatory variables (covariates) on the cumulative incidence function (CIF), a process of Bayesian analysis is discussed in this paper. The symmetry assumption is not imposed on the masking probabilities and independent Dirichlet priors assigned to them. The Markov Chain Monte Carlo (MCMC) technique is utilized to implement the Bayesian analysis. The effectiveness of the developed model is tested via numerical studies, including simulated and real data sets.

Suggested Citation

  • Yosra Yousif & Faiz Elfaki & Meftah Hrairi & Oyelola Adegboye, 2022. "Bayesian Analysis of Masked Competing Risks Data Based on Proportional Subdistribution Hazards Model," Mathematics, MDPI, vol. 10(17), pages 1-10, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3045-:d:895766
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    References listed on IDEAS

    as
    1. John P. Klein & Per Kragh Andersen, 2005. "Regression Modeling of Competing Risks Data Based on Pseudovalues of the Cumulative Incidence Function," Biometrics, The International Biometric Society, vol. 61(1), pages 223-229, March.
    2. Jong‐Hyeon Jeong & Jason Fine, 2006. "Direct parametric inference for the cumulative incidence function," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(2), pages 187-200, April.
    3. Kuo, Lynn & Yang, Tae Young, 2000. "Bayesian reliability modeling for masked system lifetime data," Statistics & Probability Letters, Elsevier, vol. 47(3), pages 229-241, April.
    4. Yosra Yousif & Faiz A. M. Elfaki & Meftah Hrairi & Oyelola A. Adegboye, 2020. "A Bayesian Approach to Competing Risks Model with Masked Causes of Failure and Incomplete Failure Times," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-7, April.
    5. Michael G. Hudgens & Chenxi Li & Jason P. Fine, 2014. "Parametric likelihood inference for interval censored competing risks data," Biometrics, The International Biometric Society, vol. 70(1), pages 1-9, March.
    Full references (including those not matched with items on IDEAS)

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