IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v59y2025i4d10.1007_s11135-025-02092-z.html
   My bibliography  Save this article

Bayesian classifier based on cancer prognostic markers using accelerated failure time model with frailty effect

Author

Listed:
  • Gajendra K. Vishwakarma

    (Indian Institute of Technology Dhanbad)

  • Pragya Kumari

    (Indian Institute of Technology Dhanbad)

  • Atanu Bhattacharjee

    (University of Dundee)

  • Seng Huat Ong

    (UCSI University
    University of Malaya)

Abstract

This work presents a Bayesian classifier technique to categorize patients based on predictive biomarkers of time-to-event data utilizing the Accelerated Failure Time (AFT) model incorporating the frailty effect. Before classification, efficient and significant markers from a high-dimensional gene expression dataset need to be identified. Currently, it is an emerging area in oncology. A conventional three-step feature selection approach is introduced to select the most relevant markers from high-dimensional data. A threshold value for each selected marker is obtained using the proposed Bayesian classification procedure incorporating the AFT model with the frailty effect. The frailty effect is incorporated to account for unobserved heterogeneity in the expression values of subjects, allowing for a more accurate investigation of the risk of cancer-related outcomes. A simulation study is performed to validate the proposed classification approach, and the classification’s performance is evaluated using the Brier score, and the result indicates that it is relatively high. Application of the proposed classification technique is provided for two high-dimensional TCGA datasets of lung cancer patients. Our results provide evidence about the effect of classified gene expression values on the survival risk of lung cancer patients.

Suggested Citation

  • Gajendra K. Vishwakarma & Pragya Kumari & Atanu Bhattacharjee & Seng Huat Ong, 2025. "Bayesian classifier based on cancer prognostic markers using accelerated failure time model with frailty effect," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(4), pages 3023-3049, August.
  • Handle: RePEc:spr:qualqt:v:59:y:2025:i:4:d:10.1007_s11135-025-02092-z
    DOI: 10.1007/s11135-025-02092-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11135-025-02092-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11135-025-02092-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Jiajia Zhang & Andrew B. Lawson, 2011. "Bayesian parametric accelerated failure time spatial model and its application to prostate cancer," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(3), pages 591-603, November.
    2. Bani K. Mallick & Debashis Ghosh & Malay Ghosh, 2005. "Bayesian classification of tumours by using gene expression data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 219-234, April.
    3. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    4. 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.
    5. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Linde, 2014. "The deviance information criterion: 12 years on," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(3), pages 485-493, June.
    6. James Vaupel & Kenneth Manton & Eric Stallard, 1979. "The impact of heterogeneity in individual frailty on the dynamics of mortality," Demography, Springer;Population Association of America (PAA), vol. 16(3), pages 439-454, August.
    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. Kai Yang & Qingqing Zhang & Xinyang Yu & Xiaogang Dong, 2023. "Bayesian inference for a mixture double autoregressive model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 77(2), pages 188-207, May.
    2. Luping Zhao & Timothy E. Hanson, 2011. "Spatially Dependent Polya Tree Modeling for Survival Data," Biometrics, The International Biometric Society, vol. 67(2), pages 391-403, June.
    3. Papastamoulis, Panagiotis, 2018. "Overfitting Bayesian mixtures of factor analyzers with an unknown number of components," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 220-234.
    4. Vicente G. Cancho & Gladys D. C. Barriga & Gauss M. Cordeiro & Edwin M. M. Ortega & Adriano K. Suzuki, 2021. "Bayesian survival model induced by frailty for lifetime with long‐term survivors," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 75(3), pages 299-323, August.
    5. Muhammed Semakula & Franco̧is Niragire & Christel Faes, 2020. "Bayesian spatio-temporal modeling of malaria risk in Rwanda," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-16, September.
    6. Fabian Krüger & Sebastian Lerch & Thordis Thorarinsdottir & Tilmann Gneiting, 2021. "Predictive Inference Based on Markov Chain Monte Carlo Output," International Statistical Review, International Statistical Institute, vol. 89(2), pages 274-301, August.
    7. Yang, Kai & Yu, Xinyang & Zhang, Qingqing & Dong, Xiaogang, 2022. "On MCMC sampling in self-exciting integer-valued threshold time series models," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    8. Yaojun Zhang & Lanpeng Ji & Georgios Aivaliotis & Charles Taylor, 2023. "Bayesian CART models for insurance claims frequency," Papers 2303.01923, arXiv.org, revised Dec 2023.
    9. Pedro Saramago & Karl Claxton & Nicky J. Welton & Marta Soares, 2020. "Bayesian econometric modelling of observational data for cost‐effectiveness analysis: establishing the value of negative pressure wound therapy in the healing of open surgical wounds," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1575-1593, October.
    10. Jianan Sun & Yunxiao Chen & Jingchen Liu & Zhiliang Ying & Tao Xin, 2016. "Latent Variable Selection for Multidimensional Item Response Theory Models via $$L_{1}$$ L 1 Regularization," Psychometrika, Springer;The Psychometric Society, vol. 81(4), pages 921-939, December.
    11. Oludare Ariyo & Emmanuel Lesaffre & Geert Verbeke & Adrian Quintero, 2022. "Bayesian Model Selection for Longitudinal Count Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 516-547, November.
    12. Voleti, Sudhir & Srinivasan, V. & Ghosh, Pulak, 2017. "An approach to improve the predictive power of choice-based conjoint analysis," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 325-335.
    13. Chen, Yunxiao & Lu, Yan & Moustaki, Irini, 2022. "Detection of two-way outliers in multivariate data and application to cheating detection in educational tests," LSE Research Online Documents on Economics 112499, London School of Economics and Political Science, LSE Library.
    14. Bresson Georges & Chaturvedi Anoop & Rahman Mohammad Arshad & Shalabh, 2021. "Seemingly unrelated regression with measurement error: estimation via Markov Chain Monte Carlo and mean field variational Bayes approximation," The International Journal of Biostatistics, De Gruyter, vol. 17(1), pages 75-97, May.
    15. Palamara, Gian Marco & Dennis, Stuart R. & Haenggi, Corinne & Schuwirth, Nele & Reichert, Peter, 2022. "Investigating the effect of pesticides on Daphnia population dynamics by inferring structure and parameters of a stochastic model," Ecological Modelling, Elsevier, vol. 472(C).
    16. Briana J. K. Stephenson & Amy H. Herring & Andrew F. Olshan, 2022. "Derivation of maternal dietary patterns accounting for regional heterogeneity," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1957-1977, November.
    17. Zhang, Yaojun & Ji, Lanpeng & Aivaliotis, Georgios & Taylor, Charles, 2024. "Bayesian CART models for insurance claims frequency," Insurance: Mathematics and Economics, Elsevier, vol. 114(C), pages 108-131.
    18. Andreea L. Erciulescu & Jean D. Opsomer, 2022. "A model‐based approach to predict employee compensation components," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1503-1520, November.
    19. Kelvyn Jones & David Manley & Ron Johnston & Dewi Owen, 2018. "Modelling residential segregation as unevenness and clustering: A multilevel modelling approach incorporating spatial dependence and tackling the MAUP," Environment and Planning B, , vol. 45(6), pages 1122-1141, November.
    20. Łukasz Lenart & Justyna Mokrzycka-Gajda, 2025. "Imitated student’s t distribution: a Bayesian approach," Statistical Papers, Springer, vol. 66(4), pages 1-44, June.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:spr:qualqt:v:59:y:2025:i:4:d:10.1007_s11135-025-02092-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.