Research article Special Issues

A new statistical approach for modeling the bladder cancer and leukemia patients data sets: Case studies in the medical sector


  • Received: 04 May 2022 Revised: 30 June 2022 Accepted: 13 July 2022 Published: 25 July 2022
  • Statistical methods are frequently used in numerous healthcare and other related sectors. One of the possible applications of the statistical methods is to provide the best description of the data sets in the healthcare sector. Keeping in view the applicability of statistical methods in the medical sector, numerous models have been introduced. In this paper, we also introduce a novel statistical method called, a new modified-$ G $ family of distributions. Several mathematical properties of the new modified-$ G $ family are derived. Based on the new modified-$ G $ method, a new updated version of the Weibull model called, a new modified-Weibull distribution is introduced. Furthermore, the estimators of the parameters of the new modified-$ G $ distributions are also obtained. Finally, the applicability of the new modified-Weibull distribution is illustrated by analyzing two medical sets. Using certain analytical tools, it is observed that the new modified-Weibull distribution is the best choice to deal with the medical data sets.

    Citation: Mahmoud El-Morshedy, Zubair Ahmad, Elsayed tag-Eldin, Zahra Almaspoor, Mohamed S. Eliwa, Zahoor Iqbal. A new statistical approach for modeling the bladder cancer and leukemia patients data sets: Case studies in the medical sector[J]. Mathematical Biosciences and Engineering, 2022, 19(10): 10474-10492. doi: 10.3934/mbe.2022490

    Related Papers:

  • Statistical methods are frequently used in numerous healthcare and other related sectors. One of the possible applications of the statistical methods is to provide the best description of the data sets in the healthcare sector. Keeping in view the applicability of statistical methods in the medical sector, numerous models have been introduced. In this paper, we also introduce a novel statistical method called, a new modified-$ G $ family of distributions. Several mathematical properties of the new modified-$ G $ family are derived. Based on the new modified-$ G $ method, a new updated version of the Weibull model called, a new modified-Weibull distribution is introduced. Furthermore, the estimators of the parameters of the new modified-$ G $ distributions are also obtained. Finally, the applicability of the new modified-Weibull distribution is illustrated by analyzing two medical sets. Using certain analytical tools, it is observed that the new modified-Weibull distribution is the best choice to deal with the medical data sets.



    加载中


    [1] M. El-Morshedy, M. S. Eliwa, The odd flexible Weibull-H family of distributions: Properties and estimation with applications to complete and upper record data, Filomat, 33 (2019), 2635–2652. https://doi.org/10.2298/FIL1909635E doi: 10.2298/FIL1909635E
    [2] C. B. de Villiers, M. Kroese, S. Moorthie, Understanding polygenic models, their development and the potential application of polygenic scores in healthcare, J. Med. Genet., 57 (2020), 725–732. https://doi.org/10.1136/jmedgenet-2019-106763 doi: 10.1136/jmedgenet-2019-106763
    [3] M. S. Eliwa, Z. A. Alhussain, M. El-Morshedy, Discrete Gompertz-G family of distributions for over-and under-dispersed data with properties, estimation, and applications, Mathematics, 8 (2020), 358. https://doi.org/10.3390/math8030358 doi: 10.3390/math8030358
    [4] C. B. Sivaparthipan, N. Karthikeyan, S. Karthik, Designing statistical assessment healthcare information system for diabetics analysis using big data, Multimedia Tools Appl., 79 (2020), 8431–8444. https://doi.org/10.1007/s11042-018-6648-3 doi: 10.1007/s11042-018-6648-3
    [5] B. R. Urlacher, Complexity, causality, and control in statistical modeling, Am. Behav. Sci., 64 (2020), 55–73. https://doi.org/10.1177/0002764219859641 doi: 10.1177/0002764219859641
    [6] L. Sandberg, H. Taavola, Y. Aoki, R. Chandler, G. N. Norén, Risk factor considerations in statistical signal detection: using subgroup disproportionality to uncover risk groups for adverse drug reactions in VigiBase, Drug Safety, 43 (2020), 999–1009. https://doi.org/10.1007/s40264-020-00957-w doi: 10.1007/s40264-020-00957-w
    [7] E. Altun, M. Ç. Korkmaz, M. El-Morshedy, M. S. Eliwa, A new flexible family of continuous distributions: the additive odd-G family, Mathematics, 9 (2021), 1837. https://doi.org/10.3390/math9161837 doi: 10.3390/math9161837
    [8] M. Altaf-Ul-Amin, S. Kanaya, N. Ono, M. Huang, Recent trends in computational biomedical research, Life, 12 (2021), 27. https://doi.org/10.3390/life12010027 doi: 10.3390/life12010027
    [9] M. S. Eliwa, M. El-Morshedy, S. Ali, Exponentiated odd Chen-G family of distributions: statistical properties, Bayesian and non-Bayesian estimation with applications, J. Appl. Stat., 48 (2021), 1948–1974. https://doi.org/10.1080/02664763.2020.1783520 doi: 10.1080/02664763.2020.1783520
    [10] A. Ratnovsky, S. Rozenes, E. Bloch, P. Halpern, Statistical learning methodologies and admission prediction in an emergency department, Australas. Emergency Care, 24 (2021), 241–247. https://doi.org/10.1016/j.auec.2020.11.004 doi: 10.1016/j.auec.2020.11.004
    [11] D. Onchonga, E. Ngetich, W. Makunda, P. Wainaina, D. Wangeshi, Anxiety and depression due to 2019 SARS-CoV-2 among frontier healthcare workers in Kenya, Heliyon, 7 (2021), e06351. https://doi.org/10.1016/j.heliyon.2021.e06351 doi: 10.1016/j.heliyon.2021.e06351
    [12] M. El-Morshedy, E. Altun, M. S. Eliwa, A new statistical approach to model the counts of novel coronavirus cases, Math. Sci., (2021), 1–14. https://doi.org/10.21203/rs.3.rs-31163/v1
    [13] M. El-Morshedy, M. S. Eliwa, A. Tyagi, A discrete analogue of odd Weibull-G family of distributions: properties, classical and Bayesian estimation with applications to count data, J. Appl. Stat., (2021), 1–25. https://doi.org/10.1080/02664763.2021.1928018
    [14] A. Illescas, H. Zhong, C. Cozowicz, A. Gonzalez Della Valle, J. Liu, S. G. Memtsoudis, et al., Health services research in anesthesia: a brief overview of common methodologies, Anesth. Analg., 134 (2021), 540–547. https://doi.org/10.1213/ANE.0000000000005884 doi: 10.1213/ANE.0000000000005884
    [15] M. C. Jones, A. Noufaily, K. Burke, A bivariate power generalized Weibull distribution: a flexible parametric model for survival analysis, Stat. Methods Med. Res., 29 (2020), 2295–2306. https://doi.org/10.1177/0962280219890893 doi: 10.1177/0962280219890893
    [16] M. A. Looha, E. Zarean, F. Masaebi, M. A. Pourhoseingholi, M. R. Zali, Assessment of prognostic factors in long-term survival of male and female patients with colorectal cancer using non-mixture cure model based on the Weibull distribution, Surg. Oncol., 38 (2021), 101562. https://doi.org/10.1016/j.suronc.2021.101562 doi: 10.1016/j.suronc.2021.101562
    [17] C. S. Kumar, S. R. Nair, A generalized Log-Weibull distribution with bio-medical appligcations, Int. J. Stat. Med. Res., 10 (2021), 10–21. https://doi.org/10.6000/1929-6029.2021.10.02 doi: 10.6000/1929-6029.2021.10.02
    [18] X. Liu, Z. Ahmad, A. M. Gemeay, A. T. Abdulrahman, E. H. Hafez, N. Khalil, Modeling the survival times of the COVID-19 patients with a new statistical model: A case study from China, Plos One, 16 (2021), e0254999. https://doi.org/10.1371/journal.pone.0254999 doi: 10.1371/journal.pone.0254999
    [19] M. E. Omer, M. A. Bakar, M. Adam, M. Mustafa, Utilization of a mixture cure rate model based on the generalized modified Weibull distribution for the analysis of leukemia patients, Asian Pac. J. Cancer Prev., 22 (2021), 1045. https://doi.org/10.31557/APJCP.2021.22.4.1045 doi: 10.31557/APJCP.2021.22.4.1045
    [20] H. S. Mohammed, Z. Ahmad, A. T. Abdulrahman, S. K. Khosa, E. H. Hafez, M. M. Abd El-Raouf, et al., Statistical modelling for Bladder cancer disease using the NLT-W distribution, AIMS Math., 6 (2021), 9262–9276. https://doi.org/10.3934/math.2021538 doi: 10.3934/math.2021538
    [21] H. S. Klakattawi, Survival analysis of cancer patients using a new extended Weibull distribution, Plos One, 17 (2021), e0264229. https://doi.org/10.1371/journal.pone.0264229 doi: 10.1371/journal.pone.0264229
    [22] M. Arif, D. M. Khan, S. K. Khosa, M. Aamir, A. Aslam, Z. Ahmad, et al., Modelling insurance losses with a new family of heavy-tailed distributions, Comput. Mater. Continua, 66 (2021), 537–550. https://doi.org/10.32604/cmc.2020.012420 doi: 10.32604/cmc.2020.012420
    [23] W. Wang, Z. Ahmad, O. Kharazmi, C. B. Ampadu, E. H. Hafez, M. M. Mohie El-Din, New generalized-$X$ family: modeling the reliability engineering applications, Plos One, 16 (2021), e0248312. https://doi.org/10.1371/journal.pone.0248312 doi: 10.1371/journal.pone.0248312
    [24] Z. Ahmad, E. Mahmoudi, G. G. Hamedani, A new extended alpha power transformed family of distributions: properties, characterizations and an application to a data set in the insurance sciences, Commun. Stat. Appl. Methods, 28 (2021), 1–19. https://doi.org/10.29220/CSAM.2021.28.1.001 doi: 10.29220/CSAM.2021.28.1.001
    [25] Z. Ahmad, E. Mahmoudi, G. Hamedani, A class of claim distributions: properties, characterizations and applications to insurance claim data, Commun. Stat. Theory Methods, 51 (2022), 2183–2208. https://doi.org/10.1080/03610926.2020.1772306 doi: 10.1080/03610926.2020.1772306
    [26] M. Arif, D. M. Khan, M. Aamir, M. El-Morshedy, Z. Ahmad, Z. Khan, A new flexible exponentiated-X family of distributions: characterizations and applications to lifetime data, IETE J. Res., (2022), 1–13. https://doi.org/10.1080/03772063.2022.2034537
    [27] Z. Ahmad, G. G. Hamedani, N. S. Butt, Recent developments in distribution theory: a brief survey and some new generalized classes of distributions, Pak. J. Stat. Oper. Res., 15 (2019), 87–110. https://doi.org/10.18187/pjsor.v15i1.2803 doi: 10.18187/pjsor.v15i1.2803
    [28] E. T. Lee, J. Wang, Statistical Methods for Survival Data Analysis, John Wiley & Sons, 2003. https://doi.org/10.1002/0471458546
    [29] A. El-Gohary, A. H. El-Bassiouny, M. El-Morshedy, Exponentiated flexible Weibull extension distribution, Int. J. Math. Appl., 3 (2015), 1–12. https://doi.org/10.18576/jsap/050106 doi: 10.18576/jsap/050106
    [30] Y. Fang, L. Zhao, Approximation to the distribution of LAD estimators for censored regression by random weighting method, J. Stat. Plann. Inference, 136 (2006), 1302–1316. https://doi.org/10.1016/j.jspi.2004.09.010 doi: 10.1016/j.jspi.2004.09.010
    [31] Y. M. Kantar, V. Yildirim, Robust estimation for parameters of the extended Burr Type III distribution, Commun. Stat. Simul. Comput., 44 (2015), 1901–1930. https://doi.org/10.1080/03610918.2013.839032 doi: 10.1080/03610918.2013.839032
    [32] E. M. Almetwally, H. M. Almogy, Comparison between M-estimation, S-estimation, and MM estimation methods of robust estimation with application and simulation, Int. J. Math. Arch., 9 (2018), 55–63.
  • Reader Comments
  • © 2022 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(1323) PDF downloads(79) Cited by(0)

Article outline

Figures and Tables

Figures(7)  /  Tables(6)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog