logo
Volume 12, Issue 4 (9-2025)                   jbrms 2025, 12(4): 12-19 | Back to browse issues page

XML Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Mamashli K, Nourmohammadi H, Sayehmiri K. Risk Factors Associated with Overall Survival of Breast Cancer Patients Using Multivariate Cox Extended Models. jbrms 2025; 12 (4) :12-19
URL: http://jbrms.medilam.ac.ir/article-1-879-en.html
Health faculty, Biostatistics Department, Ilam University of Medical Sciences, Ilam, Iran , kourosh86@gmail.com
Abstract:   (11 Views)
Introduction: The present study aimed at identifying the risk factors associated with overall survival (OS) of breast cancer (BC) patients using multivariate Cox extended models.
Materials & Methods: This retrospective cohort study was conducted on 348 women with BC who were followed up for 10 years. Kaplan-Meier (KM) and log-rank statistics, Cox proportional hazard (PH), and multivariate Cox models were used to analyze the data. STATA V.17 and SPSS V.27 were used for data analysis.
Results:  The median age of the patients was 55 years, and the median survival time was 29 months. Five- and 10-year OS were estimated at 93.4% and 88.4%, respectively. The results of multivariate analysis using the Cox model showed that lymph node (LN+) (hazard ratio (HR) = 2.86, P = 0.002), tumor size (HR = 1.99, P = 0.001), and progesterone receptor (PR-) (HR = 4.5, P = 0.002) increase death hazard significantly.
Conclusion:  Prognostic factors indicated that women with lymph node involvement (LN+), positivity of human epidermal growth factor receptor 2 (HER2+), negativity of estrogen receptor (ER-), negative expression of progesterone receptor (PR-), advanced disease grade, and large tumor sizes were more likely to have a high hazard of death than other women.

 
Full-Text [PDF 517 kb]   (5 Downloads)    
Type of Study: Research | Subject: Biostatistics
Received: 2024/07/17 | Accepted: 2025/08/8 | Published: 2025/09/30

References
1. Orbe J, Ferreira E, Núñez‐Antón V. Comparing proportional hazards and accelerated failure time models for survival analysis. Stat Med. 2002;21(22):3493-510.
2. Elsayed M, Alhussini M, Basha A, Awad A. Analysis of loco-regional and distant recurrences in breast cancer after conservative surgery. World J Surg Oncol. 2016;14(1):144.
3. Liu Z, Shen X, Liu R, Zhu G, Huang T, Xing M. Stage II Differentiated Thyroid Cancer Is a High-risk Disease in Patients <45/55 Years Old. J Clin Endocrinol Metab. 2019.
4. Amran SE, Abdullah MAA, Kek SL, Jamil SAM. Analysis of survival in breast cancer patients by using different parametric models. J Phys Conf Ser. 2017; p. 012169.
5. Narod S. Tumour size predicts long-term survival among women with lymph node-positive breast cancer. Curr Oncol. 2012;19(5):249.
6. Moghaddami FZ, Abolghasemi J, Asadi LM, Mohammadi M, Gohari M, Salehi M. Effective factors in the appearance of metastasis in patients with breast cancer using frailty model. J Arak Univ Med Sci. 2013;15(8):85-94.
7. Tonellotto F, Bergmann A, de Souza Abrahão K, de Aguiar SS, Bello MA, Thuler LCS. Impact of number of positive lymph nodes and lymph node ratio on survival of women with node-positive breast cancer. Eur J Breast Health. 2019;15(2):76.
8. Baghestani A, Moghaddam S, Majd H, Akbari M, Nafissi N, Gohari K. Survival analysis of patients with breast cancer using Weibull parametric model. Asian Pac J Cancer Prev. 2015;16(18):8567-71.
9. Alfonso AG, de Oca NAM. Application of hazard models for patients with breast cancer in Cuba. Int J Clin Exp Med. 2011;4(2):148.
10. Karimi A, Delpisheh A, Sayehmiri K. Application of accelerated failure time models for breast cancer patients' survival in Kurdistan Province of Iran. J Cancer Res Ther. 2016;12(3):1184.
11. Fallahzadeh H, Mohammadzadeh M, Pahlavani V, Pahlavani N. A study on the prognostic factors of breast cancer survival time using Bayesian Cox model. J Isfahan Med Sch. 2018;36(466):49-55.
12. Ederer F. The relative survival rate: a statistical methodology. J Natl Cancer Inst. 1961;6:101-21.
13. Kleinbaum DG, Klein M. The Cox proportional hazards model and its characteristics. Survival analysis: Springer; 2012. p. 97-159.
14. Khoei R, Bakhshi E, Azarkeivan A, Biglarian A. Survival analysis of the thalassemia major patients using parametric and semiparametric survival models. JHA. 2015;18(59).
15. Klein JP, Moeschberger ML. Survival analysis: techniques for censored and truncated data. Springer Science & Business Media; 2006.
16. Montaseri M, Charati JY, Espahbodi F. Application of parametric models to a survival analysis of hemodialysis patients. Nephrourol Mon. 2016;8(6).
17. Prat A, Parker JS, Karginova O, Fan C, Livasy C, Herschkowitz JI, et al. Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer. Breast Cancer Res. 2010;12(5):2.
18. Qi J. Comparison of proportional hazards and accelerated failure time models. 2009.
19. George B, Seals S, Aban I. Survival analysis and regression models. J Nucl Cardiol. 2014;21(4):686-94.
20. Hosseini M, Mohammad K, Rahimzadeh M, Mahmoodi M. Comparison of survival models in studying breastfeeding duration. Hakim J. 2007;10(1):66-71.
21. Mulugeta C, Emagneneh T, Kumie G, Sisay A, Abebaw N, Ayele M, et al. Predictors of survival rates among breast cancer patients in Ethiopia: a systematic review and meta-analysis 2024. Arch Public Health. 2025;83(1):30.
22. Abedi G, Janbabai G, Moosazadeh M, Farshidi F, Amiri M, Khosravi A. Survival rate of breast cancer in Iran: a meta-analysis. Asian Pac J Cancer Prev. 2016;17(10):4615-21.
23. Jayasinghe UW, Taylor R, Boyages J. Is age at diagnosis an independent prognostic factor for survival following breast cancer? ANZ J Surg. 2005;75(9):762-7.
24. Lees AW, Jenkins HJ, May CL, Cherian G, Lam EW, Hanson J. Risk factors and 10-year breast cancer survival in northern Alberta. Breast Cancer Res Treat. 1989;13(2):143-51.
25. Abadi A, Yavari P, Dehghani-Arani M, Alavi-Majd H, Ghasemi E, Amanpour F, et al. Cox models survival analysis based on breast cancer treatments. Iran J Cancer Prev. 2014;7(3):124.
26. Ghorbani N, Yazdani-Charati J, Anvari K, Ghorbanifar N. Application of Weibull accelerated failure time model on the disease-free survival rate of breast cancer. Iran J Cancer Prev. 2016;4(2):11-8.

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.