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The Extended Exponential-Weibull Accelerated Failure Time Model with Application to Sudan COVID-19 Data

Author

Listed:
  • Adam Braima S. Mastor

    (Department of Mathematics (Statistics Option) Programme, Pan African University, Institute for Basic Sciences, Technology and Innovation (PAUSTI), Nairobi P.O. Box 62000-00200, Kenya)

  • Abdulaziz S. Alghamdi

    (Department of Mathematics, College of Science & Arts, King Abdulaziz University, P.O. Box 344, Rabigh 21911, Saudi Arabia)

  • Oscar Ngesa

    (Department of Mathematics and Physical Sciences, Taita Taveta University, Voi P.O. Box 635-80300, Kenya)

  • Joseph Mung’atu

    (Department of Mathematics, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Juja P.O. Box 62000-00200, Kenya)

  • Christophe Chesneau

    (Department of Mathematics, LMNO, CNRS-Université de Caen, Campus II, Science 3, 14032 Caen, France)

  • Ahmed Z. Afify

    (Department of Statistics, Mathematics and Insurance, Benha University, Benha 13511, Egypt)

Abstract

A fully parametric accelerated failure time (AFT) model with a flexible, novel modified exponential Weibull baseline distribution called the extended exponential Weibull accelerated failure time (ExEW-AFT) model is proposed. The model is presented using the multi-parameter survival regression model, where more than one distributional parameter is linked to the covariates. The model formulation, probabilistic functions, and some of its sub-models were derived. The parameters of the introduced model are estimated using the maximum likelihood approach. An extensive simulation study is used to assess the estimates’ performance using different scenarios based on the baseline hazard shape. The proposed model is applied to a real-life right-censored COVID-19 data set from Sudan to illustrate the practical applicability of the proposed AFT model.

Suggested Citation

  • Adam Braima S. Mastor & Abdulaziz S. Alghamdi & Oscar Ngesa & Joseph Mung’atu & Christophe Chesneau & Ahmed Z. Afify, 2023. "The Extended Exponential-Weibull Accelerated Failure Time Model with Application to Sudan COVID-19 Data," Mathematics, MDPI, vol. 11(2), pages 1-26, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:2:p:460-:d:1036531
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    References listed on IDEAS

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    1. Leemis, Lawrence M. & Shih, Li-Hsing & Reynertson, Kurt, 1990. "Variate generation for accelerated life and proportional hazards models with time dependent covariates," Statistics & Probability Letters, Elsevier, vol. 10(4), pages 335-339, September.
    2. Kevin Burke & M. C. Jones & Angela Noufaily, 2020. "A flexible parametric modelling framework for survival analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(2), pages 429-457, April.
    3. Legrand, Catherine, 2021. "Advanced Survival Models," LIDAM Reprints ISBA 2021015, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    4. Shahedul A. Khan, 2018. "Exponentiated Weibull regression for time-to-event data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(2), pages 328-354, April.
    5. Kevin Burke & Frank Eriksson & C. B. Pipper, 2020. "Semiparametric multiparameter regression survival modeling," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(2), pages 555-571, June.
    6. Lawrence M. Leemis, 1987. "Technical Note—Variate Generation for Accelerated Life and Proportional Hazards Models," Operations Research, INFORMS, vol. 35(6), pages 892-894, December.
    7. Shahedul A. Khan & Nyla Basharat, 2022. "Accelerated failure time models for recurrent event data analysis and joint modeling," Computational Statistics, Springer, vol. 37(4), pages 1569-1597, September.
    8. Sanjoy K. Sinha, 2019. "Robust estimation in accelerated failure time models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(1), pages 52-78, January.
    9. Gabriela M. Rodrigues & Edwin M. M. Ortega & Gauss M. Cordeiro & Roberto Vila, 2022. "An Extended Weibull Regression for Censored Data: Application for COVID-19 in Campinas, Brazil," Mathematics, MDPI, vol. 10(19), pages 1-17, October.
    10. Shahedul A. Khan & Saima K. Khosa, 2016. "Generalized log-logistic proportional hazard model with applications in survival analysis," Journal of Statistical Distributions and Applications, Springer, vol. 3(1), pages 1-18, December.
    11. Abdisalam Hassan Muse & Samuel Mwalili & Oscar Ngesa & Christophe Chesneau & Afrah Al-Bossly & Mahmoud El-Morshedy, 2022. "Bayesian and Frequentist Approaches for a Tractable Parametric General Class of Hazard-Based Regression Models: An Application to Oncology Data," Mathematics, MDPI, vol. 10(20), pages 1-41, October.
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