IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i17p3045-d895766.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/17/3045/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/17/3045/
    Download Restriction: no
    ---><---

    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)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Chenxi, 2016. "The Fine–Gray model under interval censored competing risks data," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 327-344.
    2. Annalisa Orenti & Patrizia Boracchi & Giuseppe Marano & Elia Biganzoli & Federico Ambrogi, 2022. "A pseudo-values regression model for non-fatal event free survival in the presence of semi-competing risks," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 709-727, September.
    3. Michael J. Martens & Brent R. Logan, 2018. "A group sequential test for treatment effect based on the Fine–Gray model," Biometrics, The International Biometric Society, vol. 74(3), pages 1006-1013, September.
    4. Kozumi, Hideo, 2004. "Posterior analysis of latent competing risk models by parallel tempering," Computational Statistics & Data Analysis, Elsevier, vol. 46(3), pages 441-458, June.
    5. Erik T. Parner & Per K. Andersen & Morten Overgaard, 2020. "Cumulative risk regression in case–cohort studies using pseudo-observations," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 639-658, October.
    6. Su, Pei-Fang & Chi, Yunchan & Li, Chung-I & Shyr, Yu & Liao, Yi-De, 2011. "Analyzing survival curves at a fixed point in time for paired and clustered right-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1617-1628, April.
    7. Serge M. A. Somda & Eve Leconte & Andrew Kramar & Nicolas Penel & Christine Chevreau & Martine Delannes & Maria Rios & Thomas Filleron, 2014. "Determining the Length of Posttherapeutic Follow-up for Cancer Patients Using Competing Risks Modeling," Medical Decision Making, , vol. 34(2), pages 168-179, February.
    8. Frank Eriksson & Thomas Scheike, 2015. "Additive gamma frailty models with applications to competing risks in related individuals," Biometrics, The International Biometric Society, vol. 71(3), pages 677-686, September.
    9. S. R. Haile & J.-H. Jeong & X. Chen & Y. Cheng, 2016. "A 3-parameter Gompertz distribution for survival data with competing risks, with an application to breast cancer data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(12), pages 2239-2253, September.
    10. Lu Mao & Dan-Yu Lin & Donglin Zeng, 2017. "Semiparametric regression analysis of interval-censored competing risks data," Biometrics, The International Biometric Society, vol. 73(3), pages 857-865, September.
    11. Zijing Yang & Chengfeng Zhang & Yawen Hou & Zheng Chen, 2023. "Analysis of dynamic restricted mean survival time based on pseudo‐observations," Biometrics, The International Biometric Society, vol. 79(4), pages 3690-3700, December.
    12. Deresa, Negera Wakgari & Van Keilegom, Ingrid, 2020. "A multivariate normal regression model for survival data subject to different types of dependent censoring," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    13. Sangbum Choi & Xuelin Huang, 2014. "Maximum likelihood estimation of semiparametric mixture component models for competing risks data," Biometrics, The International Biometric Society, vol. 70(3), pages 588-598, September.
    14. Michael J. Martens & Brent R. Logan, 2020. "Group sequential tests for treatment effect on survival and cumulative incidence at a fixed time point," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(3), pages 603-623, July.
    15. M. A. Nicolaie & J. C. van Houwelingen & T. M. de Witte & H. Putter, 2013. "Dynamic Pseudo-Observations: A Robust Approach to Dynamic Prediction in Competing Risks," Biometrics, The International Biometric Society, vol. 69(4), pages 1043-1052, December.
    16. Karin Biering & Torsten Toftegaard Nielsen & Kurt Rasmussen & Troels Niemann & Niels Henrik Hjollund, 2012. "Return to Work after Percutaneous Coronary Intervention: The Predictive Value of Self-Reported Health Compared to Clinical Measures," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-7, November.
    17. Yuxue Jin & Tze Leung Lai, 2017. "A new approach to regression analysis of censored competing-risks data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 605-625, October.
    18. Joshi, Amol M. & Hemmatian, Iman, 2018. "How do legal surprises drive organizational attention and case resolution? An analysis of false patent marking lawsuits," Research Policy, Elsevier, vol. 47(9), pages 1741-1761.
    19. Jacobo de Uña‐Álvarez & Micha Mandel, 2018. "Nonparametric estimation of transition probabilities for a general progressive multi‐state model under cross‐sectional sampling," Biometrics, The International Biometric Society, vol. 74(4), pages 1203-1212, December.
    20. Wycinka Ewa, 2019. "Competing Risk Models of Default in the Presence of Early Repayments," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 23(2), pages 99-120, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3045-:d:895766. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.