Volume 11, Issue 3 (12-2023)
Jorjani Biomed J 2023, 11(3): 14-17 |
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Pourdarvish A, Hashemi R, Azar J, Norouzi S. Analysis of competing risks in the CoxPH model for progressive censorship with binomial removal. Jorjani Biomed J 2023; 11 (3) :14-17
URL: http://goums.ac.ir/jorjanijournal/article-1-970-en.html
URL: http://goums.ac.ir/jorjanijournal/article-1-970-en.html
1- Department of Statistics, University of Mazandaran, Babolsar, Iran
2- Department of Statistics, Faculty of Basic Science, Razi University, Kermanshah, Iran
3- University of Applied Science and Technology, Branch of Ravansar, Ravansar, Iran
4- Department of Statistics and Epidemiology, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran ,snorouzibiostatistics@gmail.com
2- Department of Statistics, Faculty of Basic Science, Razi University, Kermanshah, Iran
3- University of Applied Science and Technology, Branch of Ravansar, Ravansar, Iran
4- Department of Statistics and Epidemiology, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran ,
Abstract: (1302 Views)
Background: In medical research and survival analysis, it is common for an individual or item's failure to be attributable to multiple causes, also known as competing risks. This article focuses on examining the competing risks model as the data increasingly becomes type II censored and randomly removed. The model assumes that the causes of failure are independent and that the lifetimes of individuals are described by the Cox model. At each failure time, the number of items or people removed follows a binomial distribution. The article derives estimators for the indefinite parameters in the model. The study presents a set of detailed data and includes a simulation study that also illustrates the results.
Methods: Different reasons, frequently known as competing risks, are frequently embroiled in an individual's or an item's failure in medical research survival analysis. The competing risks shown under sort II dynamic censoring with random removals are the subject of this research.
We get the maximum likelihood and inexact most extreme probability estimators of the obscure parameters. The asymptotic distribution of the maximum probability estimators is utilized to decide the CIs. Then, Monte Carlo simulations were applied to demonstrate the approach. The analyses were performed utilizing R 4.0.4 software.
Results: For stroke, systolic blood pressure (SBP) and hypertension status are the only significant variables. In contrast, gender, body mass index (BMI), smoking status, the logarithm of urinary albumin and creatinine ratio, and diabetes status are significant variables for coronary heart disease (CHD) and other cardiovascular diseases (CVDs). The results suggest that significant risk factors differ for different types of CVD events.
Conclusion: The outcomes of the simulation study indicate that progressively right-censored type II sampling designs outperformed the usual censored type II sampling designs. Therefore, the estimated parameters on the defined pattern setting are recommended. They can be used in many practical situations when competing risks occur, and progressive censoring could be considered.
Methods: Different reasons, frequently known as competing risks, are frequently embroiled in an individual's or an item's failure in medical research survival analysis. The competing risks shown under sort II dynamic censoring with random removals are the subject of this research.
We get the maximum likelihood and inexact most extreme probability estimators of the obscure parameters. The asymptotic distribution of the maximum probability estimators is utilized to decide the CIs. Then, Monte Carlo simulations were applied to demonstrate the approach. The analyses were performed utilizing R 4.0.4 software.
Results: For stroke, systolic blood pressure (SBP) and hypertension status are the only significant variables. In contrast, gender, body mass index (BMI), smoking status, the logarithm of urinary albumin and creatinine ratio, and diabetes status are significant variables for coronary heart disease (CHD) and other cardiovascular diseases (CVDs). The results suggest that significant risk factors differ for different types of CVD events.
Conclusion: The outcomes of the simulation study indicate that progressively right-censored type II sampling designs outperformed the usual censored type II sampling designs. Therefore, the estimated parameters on the defined pattern setting are recommended. They can be used in many practical situations when competing risks occur, and progressive censoring could be considered.
Type of Article: Original article |
Subject:
Bio-statistics
Received: 2023/06/16 | Accepted: 2023/12/14 | Published: 2023/12/21
Received: 2023/06/16 | Accepted: 2023/12/14 | Published: 2023/12/21
References
1. Andersen PK, Geskus RB, de Witte T, Putter H. Competing risks in epidemiology: possibilities and pitfalls. Int J Epidemiol. 2012;41(3):861-70. [View at Publisher] [DOI] [PMID] [Google Scholar]
2. Pintilie M. Competing risks: a practical perspective. England: John Wiley & Sons; 2006. [View at Publisher] [DOI] [Google Scholar]
3. Pintilie M. An introduction to competing risks analysis. Rev Esp de Cardiol. 2011;64(7):599-605.
https://doi.org/10.1016/j.rec.2011.03.016 [View at Publisher] [DOI] [Google Scholar]
4. Kleinbaum DG, Klein M. Survival Analysis: A Self‐Learning Text. New York: Spinger; 2012. [View at Publisher] [Google Scholar]
5. Crowder MJ. Classical competing risks. New York: CRC Press; 2001. [View at Publisher] [DOI] [Google Scholar]
6. Dutz A, Löck S. Competing risks in survival data analysis. Radiother Oncol. 2019;130:185-9. [View at Publisher] [DOI] [PMID] [Google Scholar]
7. Basu AP, Ghosh JK. Competing risks theory and identifiability problems. The Exponential Distribution: Routledge; 2019. p.489-95. [View at Publisher] [DOI] [PMID] [Google Scholar]
8. Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. Journal of the American statistical association. 1958;53(282):457-81. [View at Publisher] [DOI] [Google Scholar]
9. Fox J. Cox proportional-hazards regression for survival data. An R and S-PLUS companion to applied regression. London: SAGE; 2002. [View at Publisher] [Google Scholar]
10. Klein JP, Moeschberger ML. Survival analysis: techniques for censored and truncated data. New York: Springer; 2003. [View at Publisher] [DOI] [Google Scholar]
11. Klein JP, Van Houwelingen HC, Ibrahim JG, Scheike TH. Handbook of survival analysis. New York: CRC Press; 2016. [View at Publisher] [DOI] [Google Scholar]
12. Kleinbaum DG, Klein M. Survival analysis: A Self-Learning Text. New York: Springer; 2010. [View at Publisher] [DOI] [Google Scholar]
13. Kamps U. A concept of generalized order statistics. Journal of Statistical Planning and Inference. 1995;48(1):1-23. [View at Publisher] [DOI] [Google Scholar]
14. Razmkhah M, Simriz S. Statistical inferences based on INID progressively type II censored order statistics. Annals of the Institute of Statistical Mathematics. 2018;70(3):583-604. [View at Publisher] [DOI] [Google Scholar]
15. Balakrishnan N, Aggarwala R. Progressive censoring: theory, methods, and applications. US: Springer Science & Business Media; 2000. [View at Publisher] [DOI] [Google Scholar]
16. Balakrishnan N, Cramer E. The art of progressive censoring: Applications to Reliability and Quality. New York: Birkhäuser; 2014. [View at Publisher] [DOI] [Google Scholar]
17. Balakrishnan N. Progressive censoring methodology: an appraisal. Test. 2007;16(2):211-59. [View at Publisher] [DOI] [Google Scholar]
18. Salemi U, Rezaei S, Si Y, Nadarajah S. On optimal progressive censoring schemes for normal distribution. Annals of Data Science. 2018;5(4):637-58. [View at Publisher] [DOI] [Google Scholar]
19. Burkschat M, Cramer E, Kamps U. On optimal schemes in progressive censoring. Statistics & probability letters. 2006;76(10):1032-6. [View at Publisher] [DOI] [Google Scholar]
20. Tse SK, Yang C, Yuen H-K. Statistical analysis of Weibull distributed lifetime data under Type II progressive censoring with binomial removals. Journal of Applied Statistics. 2010;27(8):1033-43. [View at Publisher] [DOI] [Google Scholar]
21. Miller Jr RG. Survival analysis. 2nd ed. John Wiley & Sons; 2011. [View at Publisher] [Google Scholar]
22. Pareek B, Kundu D, Kumar S. On progressively censored competing risks data for Weibull distributions. Computational Statistics & Data Analysis. 2009;53(12):4083-94. [View at Publisher] [DOI] [Google Scholar]
23. Sarhan AM, Alameri M, Al-Wasel I. Analysis of progressive censoring competing risks data with binomial removals. Int J Math Analysis. 2008;2(20):965-76. [View at Publisher] [Google Scholar]
24. Lin DY. On the Breslow estimator. Lifetime data analysis. 2007;13:471-80. [View at Publisher] [DOI] [PMID] [Google Scholar]
25. Hashemi R, Amiri L. Analysis of progressive Type-II censoring in the Weibull model for competing risks data with binomial removals. Appl Math Sci. 2011;5(21-24):1073-87. [View at Publisher] [Google Scholar]
26. Lodhi C, Tripathi YM, Bhattacharya R. On a progressively censored competing risks data from Gompertz distribution. Communications in Statistics-Simulation and Computation. 2023;52(4):1278-99. [View at Publisher] [DOI] [Google Scholar]
27. Wang L, Wu SJ, Lin H, Tripathi YM. Inference for block progressive censored competing risks data from an inverted exponentiated exponential model. Quality and Reliability Engineering International. 2023;39(7):2736-64. [View at Publisher] [DOI] [Google Scholar]
28. Azizi F, Haghighi F, Tabibi Gilani N. Statistical inference for competing risks model under progressive interval censored Weibull data. Communications in Statistics-Simulation and Computation. 2020;49(7):1931-44. [View at Publisher] [DOI] [Google Scholar]
29. Chacko M, Mohan R. Bayesian analysis of Weibull distribution based on progressive type-II censored competing risks data with binomial removals. Computational Statistics. 2019;34:233-52. [View at Publisher] [DOI] [Google Scholar]
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