Research article

The accelerated failure time regression model under the extended-exponential distribution with survival analysis

  • Received: 04 February 2024 Revised: 10 April 2024 Accepted: 18 April 2024 Published: 29 April 2024
  • MSC : 60E05, 62E99

  • In this paper, we propose a parametric accelerated failure time (AFT) hazard-based regression model with the extended alpha-power exponential (EAPE) baseline distribution. The proposed model is called the extended alpha-power exponential-AFT (EAPE-AFT) regression model. We show that the EAPE distribution is closed under the AFT model. The parameters of the proposed EAPE-AFT model have been estimated by using the method of maximum likelihood estimation. An extensive simulation study was also conducted to examine the performance of the estimates under several scenarios based on the shapes of the baseline hazard function. Finally, real-life censored survival data has been used to illustrate the applicability of the proposed model.

    Citation: Veronica Kariuki, Anthony Wanjoya, Oscar Ngesa, Mahmoud M. Mansour, Enayat M. Abd Elrazik, Ahmed Z. Afify. The accelerated failure time regression model under the extended-exponential distribution with survival analysis[J]. AIMS Mathematics, 2024, 9(6): 15610-15638. doi: 10.3934/math.2024754

    Related Papers:

  • In this paper, we propose a parametric accelerated failure time (AFT) hazard-based regression model with the extended alpha-power exponential (EAPE) baseline distribution. The proposed model is called the extended alpha-power exponential-AFT (EAPE-AFT) regression model. We show that the EAPE distribution is closed under the AFT model. The parameters of the proposed EAPE-AFT model have been estimated by using the method of maximum likelihood estimation. An extensive simulation study was also conducted to examine the performance of the estimates under several scenarios based on the shapes of the baseline hazard function. Finally, real-life censored survival data has been used to illustrate the applicability of the proposed model.



    加载中


    [1] J. F. Lawless, Statistical models and methods for lifetime data, John Wiley & Sons, Inc., 2011. https://doi.org/10.1002/9781118033005
    [2] H. Piriaei, G. Yari, R. Farnoosh, E-Bayesian estimations for the cumulative hazard rate and mean residual life based on exponential distribution and record data, J. Stat. Comput. Simul., 90 (2020), 271–290. https://doi.org/10.1080/00949655.2019.1678623 doi: 10.1080/00949655.2019.1678623
    [3] A. H. Muse, S. Mwalili, O. Ngesa, H. M. Alshanbari, S. K. Khosa, E. Hussam, Bayesian and frequentist approach for the generalized log-logistic accelerated failure time model with applications to larynx-cancer patients, Alex. Eng. J., 10 (2022), 7953–7978. https://doi.org/10.1016/j.aej.2022.01.033 doi: 10.1016/j.aej.2022.01.033
    [4] S. A. B. Mastor, O. Ngesa, J. Mung'atu, N. M. Alfaer, A. Z. Afify, The extended exponential Weibull distribution: Properties, inference, and applications to real-life data, Complexity, 2022 (2022), 4068842. https://doi.org/10.1155/2022/4068842 doi: 10.1155/2022/4068842
    [5] S. A. Khan, Exponentiated Weibull regression for time-to-event data, Lifetime Data Anal., 24 (2018), 328–354. https://doi.org/10.1007/s10985-017-9394-3 doi: 10.1007/s10985-017-9394-3
    [6] C. D. Lai, Generalized Weibull distributions, Springer, 2014, https://doi.org/10.1007/978-3-642-39106-4_2
    [7] A. H. Muse, S. Mwalili, O. Ngesa, C. Chesneau, A. Al-Bossly, M. El-Morshedy, Bayesian and frequentist approaches for a tractable parametric general class of Hazard-based regression models: an application to oncology data, Mathematics, 10 (2022), 3813. https://doi.org/10.3390/math10203813 doi: 10.3390/math10203813
    [8] R. D. Gupta, D. Kundu, Exponentiated exponential family: an alternative to gamma and Weibull distributions, Biomet. J., 43 (2001), 117–130. https://doi.org/10.1002/1521-4036(200102)43:1%3C117::AID-BIMJ117%3E3.0.CO;2-R doi: 10.1002/1521-4036(200102)43:1%3C117::AID-BIMJ117%3E3.0.CO;2-R
    [9] S. Nadarajah, F. Haghighi, An extension of the exponential distribution, Statistics, 45 (2011), 543–558. https://doi.org/10.1080/02331881003678678 doi: 10.1080/02331881003678678
    [10] A. Z. Afify, M. Zayed, M. Ahsanullah, The extended exponential distribution and its applications, J. Stat. Theory Appl., 17 (2018), 213–229. https://doi.org/10.2991/jsta.2018.17.2.3 doi: 10.2991/jsta.2018.17.2.3
    [11] S. Nadarajah, S. Kotz, The exponentiated type distributions, Acta Appl. Math., 92 (2006), 97–111. https://doi.org/10.1007/s10440-006-9055-0 doi: 10.1007/s10440-006-9055-0
    [12] S. Nadarajah, S. Kotz, The beta exponential distribution, Reliab. Eng. Syst. Saf., 91 (2006), 689–697. https://doi.org/10.1016/j.ress.2005.05.008 doi: 10.1016/j.ress.2005.05.008
    [13] M. Kilai, G. A. Waititu, W. A. Kibira, M. M. A. El-Raouf, T. A. Abushal, A new versatile modification of the Rayleigh distribution for modeling COVID-19 mortality rates, Results Phys., 35 (2022), 105–260. https://doi.org/10.1016/j.rinp.2022.105260 doi: 10.1016/j.rinp.2022.105260
    [14] E. A. ElSherpieny, E. M. Almetwally, The exponentiated generalized Alpha power family of distribution: properties and applications, Pak. J. Stat. Oper. Res., 18 (2022), 349–367. https://doi.org/10.18187/pjsor.v18i2.3515 doi: 10.18187/pjsor.v18i2.3515
    [15] D. Alvares, E. Lázaro, V. Gómez-Rubio, C. Armero, Carmen Bayesian survival analysis with BUGS, Stat. Med., 40 (2021), 2975–3020. https://doi.org/10.1002/sim.8933 doi: 10.1002/sim.8933
    [16] F. N. Demarqui, V. D. Mayrink, Yang and Prentice model with piecewise exponential baseline distribution for modeling lifetime data with crossing survival curve, Braz. J. Probab. Stat., 35 (2020), 172–186. https://doi.org/10.1214/20-BJPS471 doi: 10.1214/20-BJPS471
    [17] F. J. Rubio, L. Remontet, N. P. Jewell, A. Belot, On a general structure for hazard-based regression models: an application to population-based cancer research, Stat. Methods Med. Res., 28 (2019), 2404–2417. https://doi.org/10.1177/09622802187822 doi: 10.1177/09622802187822
    [18] H. Zhou, T. Hanson, Bayesian spatial survival models, Springer, 2015. https://doi.org/10.1007/978-3-319-19518-6_11
    [19] L. M. Leemis, L. H. Shih, K. Reynertson, Variate generation for accelerated life and proportional hazards models with time-dependent covariates, Stat. Probab. Lett., 10 (1990), 335–339. https://doi.org/10.1016/0167-7152(90)90052-9 doi: 10.1016/0167-7152(90)90052-9
    [20] M. Ashraf-Ul-Alam, A. A. Khan, Generalized Topp-Leone-Weibull AFT modelling: a Bayesian analysis with MCMC tools using R and Stan, Aust. J. Stat., 50 (2021), 52–76. https://doi.org/10.17713/ajs.v50i5.1166 doi: 10.17713/ajs.v50i5.1166
    [21] A. B. Mastor, O. Ngesa, J. Mung'atu, A. Z. Afify, The extended exponential-Weibull accelerated failure time model with applications to cancer data set, International Conference on Mathematics and Its Applications in Science and Engineering, 2022.
    [22] K. Burke, M. C. Jones, A. Noufaily, A flexible parametric modelling framework for survival analysis, J. R. Stat. Soc. Ser. C Appl. Stat., 69 (2020), 429–457. https://doi.org/10.1111/rssc.12398 doi: 10.1111/rssc.12398
    [23] X. Wang, Y. Yue, J. J. Faraway, Bayesian regression modeling with INLA, 1 Ed., Chapman and Hall/CRC, 2018. https://doi.org/10.1201/9781351165761
    [24] S. A. Khan, N. Basharat, Accelerated failure time models for recurrent event data analysis and joint modeling, Comput. Stat., 37 (2022), 1569–1597. https://doi.org/10.1007/s00180-021-01171-7 doi: 10.1007/s00180-021-01171-7
    [25] J. Le-Rademacher, X. Wang, Time-to-event data: an overview and analysis considerations, J. Thorac. Oncol., 16 (2021), 1067–1074. https://doi.org/10.1016/j.jtho.2021.04.004 doi: 10.1016/j.jtho.2021.04.004
    [26] S. Choi, H. Cho, Accelerated failure time models for the analysis of competing risks, J. Korean Stat. Soc., 48 (2019), 315–326. https://doi.org/10.1016/j.jkss.2018.10.003 doi: 10.1016/j.jkss.2018.10.003
    [27] R. Mokarram, M. Emadi, A. H. Rad, M. J. Nooghabi, A comparison of parametric and semi-parametric survival models with artificial neural networks, Commun. Stat., 47 (2018), 738–746. https://doi.org/ 10.1080/03610918.2017.1291961 doi: 10.1080/03610918.2017.1291961
    [28] I. Selingerova, S. Katina, I. Horova, Comparison of parametric and semiparametric survival regression models with kernel estimation, J. Stat. Comput. Simul., 91 (2021), 2717–2739. https://doi.org/10.1080/00949655.2021.1906875 doi: 10.1080/00949655.2021.1906875
    [29] J. P. Klein, M. L. Moeschberger, Survival analysis: techniques for censored and truncated data, 2 Eds., Springer, 2003. https://doi.org/10.1007/b97377
    [30] T. M. Therneau, P. M. Grambsch, T. M. Therneau, P. M. Grambsch, The Cox model, In: Modeling survival data: extending the Cox model, Springer, 2000. https://doi.org/10.1007/978-1-4757-3294-8_3
    [31] D. G. Kleinbaum, M. Klein, Survival analysis: a self-learning text, 3 Eds., Springer, 2012. https://doi.org/10.1007/978-1-4419-6646-9
    [32] M. J. Crowther, P. Royston, M. Clements, A flexible parametric accelerated failure time model, arXiv, 2020. https://doi.org/10.48550/arXiv.2006.06807
    [33] J. D. Kalbfleisch, R. L. Prentice, The statistical analysis of failure time data, 2 Eds., John Wiley & Sons, Inc., 2001. https://doi.org/10.1002/9781118032985
    [34] V. Kariuki, A. Wanjoya, O. Ngesa, M. Alqawba, A flexible family of distributions based on the alpha power family of distributions and its application to survival data, Pak. J. Statist., 39 (2023), 237–258.
    [35] D. G. Kleinbaum, M. Klein, Survival analysis a self-learning text, 3 Eds., Springer, 1996. https://doi.org/10.1007/978-1-4419-6646-9
    [36] S. Wang, W. Chen, M. Chen, Y. Zhou, Maximum likelihood estimation of the parameters of the inverse Gaussian distribution using maximum rank set sampling with unequal samples, Math. Popul. Stud., 30 (2023), 1–21. https://doi.org/10.1080/08898480.2021.1996822 doi: 10.1080/08898480.2021.1996822
    [37] D. G. Kleinbaum, Evaluating the proportional hazards assumption, 3 Eds., Springer, 2012. https://doi.org/10.1007/978-1-4757-2555-1_4
    [38] C. G. Broyden, A new method of solving nonlinear simultaneous equations, Comput. J., 12 (1969), 94–99. https://doi.org/10.1093/comjnl/12.1.94 doi: 10.1093/comjnl/12.1.94
    [39] R. Fletcher, A class of methods for nonlinear programming with termination and convergence properties, North-Holland, 1970.
    [40] D. Goldfarb, A family of variable-metric methods derived by variational means, Math. Comput., 24 (1970), 23–26. https://doi.org/10.2307/2004873 doi: 10.2307/2004873
    [41] D. F. Shanno, Conditioning of quasi-Newton methods for function minimization, Math. Comput., 24 (1970), 647–656.
    [42] S. A. Khan, S. K. Khosa, Generalized log-logistic proportional hazard model with applications in survival analysis, J. Stat. Distrib. Appl., 3 (2016), 16. https://doi.org/10.1186/s40488-016-0054-z doi: 10.1186/s40488-016-0054-z
    [43] P. Royston, D. G. Altman, External validation of a Cox prognostic model: principles and methods, BMC Med. Res. Methodol., 13 (2013), 33. https://doi.org/10.1186/1471-2288-13-33 doi: 10.1186/1471-2288-13-33
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(658) PDF downloads(56) Cited by(0)

Article outline

Figures and Tables

Figures(8)  /  Tables(17)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog