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Volume 17, Issue 2 (Iranian Journal of Breast Diseases 2024)
ijbd 2024, 17(2): 40-53 |
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Ethics code: IR.MODARES.REC.1401.116
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Sedighi kamel F, Rasekhi A A, Haghighat S. Bayesian Analysis of Factors Affecting Long-Term and Short-Term Survival of Breast Cancer Patients Using the Smooth Semi-Nonparametric Mixture Cure Model. ijbd 2024; 17 (2) :40-53
URL: http://ijbd.ir/article-1-1093-en.html
URL: http://ijbd.ir/article-1-1093-en.html
1- Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
2- Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran ,rasekhi@modares.ac.ir
3- Department of Breast Diseases, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran
2- Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran ,
3- Department of Breast Diseases, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran
Abstract: (779 Views)
Introduction: Today, according to the advancements in cancer treatment, a fraction of patients never experience an adverse event, such as death, even when the duration of the disease is prolonged. Cure models are used in the analysis of these types of diseases. In this study, we examined the survival of patients, the cure probability, and the affecting factors among breast cancer patients.
Methods: We analyzed the data of 1,247 breast cancer patients who referred to Motamed Jihad University Research Institute in Tehran between 1995 and 2013 and followed them up until 2018. Data analysis was done using R version 4.3.0 software to check the survival time of uncured patients and the cure rate and to identify the effective factors with the Bayesian estimation method by fitting the semi-nonparametric smooth mixture cure model.
Results: The results of this study showed that out of 1,247 patients with breast cancer, 82.8% of the patients were censored, and 17.2% of the patients died. The cure rate was 58%, according to the Kaplan-Meier curve. Examining the factors affecting the death of patients showed that the patient's high weight, more advanced stages of the disease, involvement of lymph nodes, and breast-conserving surgery were effective on the time to death and short-term survival.
Conclusion: Based on the results of this, there are several crucial prognostic factors associated with breast cancer that play a significant role in identifying high-risk patients and choosing the type of treatment in the short term.
Methods: We analyzed the data of 1,247 breast cancer patients who referred to Motamed Jihad University Research Institute in Tehran between 1995 and 2013 and followed them up until 2018. Data analysis was done using R version 4.3.0 software to check the survival time of uncured patients and the cure rate and to identify the effective factors with the Bayesian estimation method by fitting the semi-nonparametric smooth mixture cure model.
Results: The results of this study showed that out of 1,247 patients with breast cancer, 82.8% of the patients were censored, and 17.2% of the patients died. The cure rate was 58%, according to the Kaplan-Meier curve. Examining the factors affecting the death of patients showed that the patient's high weight, more advanced stages of the disease, involvement of lymph nodes, and breast-conserving surgery were effective on the time to death and short-term survival.
Conclusion: Based on the results of this, there are several crucial prognostic factors associated with breast cancer that play a significant role in identifying high-risk patients and choosing the type of treatment in the short term.
Type of Study: Research |
Subject:
Diagnosis, treatment, rehabilitation
Received: 2024/01/14 | Accepted: 2024/07/6 | Published: 2024/07/24
Received: 2024/01/14 | Accepted: 2024/07/6 | Published: 2024/07/24
References
1. Deo S, Sharma J, Kumar S. GLOBOCAN 2020 report on global cancer burden: challenges and opportunities for surgical oncologists. Annals of surgical oncology. 2022;29(11):6497-500. [DOI:10.1245/s10434-022-12151-6] [PMID]
2. Alagheband M, Mazloomy Mahmoodabad SS, Yassini Ardekani SM, Fallahzadeh H, Rezaei MR, Yavari MR, Moghadam JA. The impact of religious cognitive behavioural therapy (RCBT) on general health among Iranians. Mental Health, Religion & Culture. 2019;22(1):73-81. [DOI:10.1080/13674676.2018.1517254]
3. Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. Ca Cancer J Clin. 2023;73(1):17-48. [DOI:10.3322/caac.21763] [PMID]
4. Howlader N, Cronin KA, Kurian AW, Andridge R. Differences in breast cancer survival by molecular subtypes in the United States. Cancer Epidemiology, Biomarkers & Prevention. 2018;27(6):619-26. [DOI:10.1158/1055-9965.EPI-17-0627] [PMID]
5. Meshkat M, Baghestani AR, Zayeri F, Khayamzadeh M, Akbari ME. Survival rate and prognostic factors among Iranian breast cancer patients. Iranian Journal of Public Health. 2020;49(2):341. [Persian] [DOI:10.18502/ijph.v49i2.3102] [PMID] []
6. Mosavi-Naeini S, Mofid B, Mohebbi H, Mehmannavaz M, Khoshini S. Comparison of regional recurrence, metastasis and survival rate between two surgical methods in the treatment of breast cancer stage I and II. Kowsar Med J. 2009;14(2):89-94. [Persian]
7. Haghighat S, Omidi Z, Ghanbari-Motlagh A. Trend of Breast Cancer Incidence in Iran During A Fifteen-Year Interval According To National Cancer Registry Reports. Iranian Journal of Breast Diseases. 2022;15(2):4-17. [Persian] [DOI:10.30699/ijbd.15.2.4]
8. Klein JP, Moeschberger ML. Survival analysis: techniques for censored and truncated data: Springer; 2003. [DOI:10.1007/b97377]
9. Kleinbaum DG, Klein M. Survival analysis a self-learning text: Springer; 1996. [DOI:10.2307/2532873]
10. Collett D. Modelling survival data in medical research: CRC press; 2023. [DOI:10.1201/9781003282525]
11. Maller RA, Zhou X. Survival analysis with long-term survivors. (No Title). 1996.
12. Sposto R. Cure model analysis in cancer: an application to data from the Children's Cancer Group. Statistics in medicine. 2002;21(2):293-312. [DOI:10.1002/sim.987] [PMID]
13. Legrand C, Bertrand A. Cure models in cancer clinical trials. Textbook of Clinical Trials in Oncology: Chapman and Hall/CRC. 2019:465-92. [DOI:10.1201/9781315112084-22]
14. Othus M, Barlogie B, LeBlanc ML, Crowley JJ. Cure models as a useful statistical tool for analyzing survival. Clinical Cancer Research. 2012;18(14):3731-6. [DOI:10.1158/1078-0432.CCR-11-2859] [PMID] []
15. Amico M, Van Keilegom I. Cure models in survival analysis. Annual Review of Statistics and Its Application. 2018;5:311-42. [DOI:10.1146/annurev-statistics-031017-100101]
16. Peng Y, Yu B. Cure models: methods, applications, and implementation: CRC Press; 2021. [DOI:10.1201/9780429032301]
17. Gallant AR, Nychka DW. Semi-nonparametric maximum likelihood estimation. Econometrica: Journal of the econometric society. 1987:363-90. [DOI:10.2307/1913241]
18. Walker S, Mallick BK. A Bayesian semiparametric accelerated failure time model. Biometrics. 1999;55(2):477-83. [DOI:10.1111/j.0006-341X.1999.00477.x] [PMID]
19. Van Ravenzwaaij D, Cassey P, Brown SD. A simple introduction to Markov Chain Monte-Carlo sampling. Psychonomic bulletin & review. 2018;25(1):143-54. [DOI:10.3758/s13423-016-1015-8] [PMID] []
20. Li H, Zhang J, Tang Y. Smooth Semi‐nonparametric Analysis for Mixture Cure Models and Its Application to Breast Cancer. Australian & New Zealand Journal of Statistics. 2014;56(3):217-35. [DOI:10.1111/anzs.12080]
21. Li C. Breast cancer epidemiology: Springer; 2010. [DOI:10.1007/978-1-4419-0685-4]
22. Ghasemi F, Rasekhi A, Haghighat S. Analysis of the Survival of Breast Cancer Patients Using Weibull and Poisson Beta-Weibull Non-Mixture Cure Models. Research in Medicine: Journal of Research in Medical Sciences. 2019;42(4)236-42. [Persian]
23. Poorolajal J, Nafissi N, Akbari ME, Mahjub H, Esmailnasab N. Breast cancer survival analysis based on immunohistochemistry subtypes (ER/PR/HER2): a retrospective cohort study. Archives of Iranian medicine. 2016;19(10):680-6.
24. Wei J, Jiang Y, Shao Z. The survival benefit of postmastectomy radiotherapy for breast cancer patients with T1-2N1 disease according to molecular subtype. The Breast. 2020;51:40-9. [DOI:10.1016/j.breast.2020.03.003] [PMID] []
25. Faradmal J, Talebi A, Rezaianzadeh A, Mahjub H. Survival analysis of breast cancer patients using cox and frailty models. Journal of research in health sciences. 2012;12(2):127-30.
26. Rosenberg J, Chia YL, Plevritis S. The effect of age, race, tumor size, tumor grade, and disease stage on invasive ductal breast cancer survival in the US SEER database. Breast cancer research and treatment. 2005;89:47-54. [DOI:10.1007/s10549-004-1470-1] [PMID]
27. Hajian K, Gholizadehpasha A, Bozorgzadeh S. Association of obesity and central obesity with breast cancer risk in pre-and postmenopausal women. Journal of Babol university of medical sciences. 2013;15(3):7-15. [Persian]
28. Badrian M, Ahmadi P, Amani M, Motamedi N. Prevalence of risk factors for breast cancer in 20 to 69 years old women. Iranian Journal of Breast Diseases. 2014;7(2):67-75. [Persian]
29. Rostampour F, Soltani-Momtaz RG, Eslamlu HF, Pashaee F, Mahmudlu R. Risk Factors for Breast Cancer in Urmia: A Case-Control Study. 2023. [DOI:10.30699/ijbd.16.2.55]
30. Fendereski A, Hajizadeh E, Haghighat S, Rasekhi A. Evaluation of Factors Related to Short-Term and Long-Term Survival of Breast Cancer Patients by Mixture Cure Model. Iranian Journal of Breast Diseases. 2022;15(1):4-17. [Persian] [DOI:10.30699/ijbd.15.1.4]
31. Li Y, Lu S, Zhang Y, Wang S, Liu H. Loco-regional recurrence trend and prognosis in young women with breast cancer according to molecular subtypes: analysis of 1099 cases. World Journal of Surgical Oncology. 2021;19(1):1-17.
https://doi.org/10.1186/s12957-021-02214-5
https://doi.org/10.1186/s12957-023-03277-2 [DOI:10.1016/j.suronc.2020.11.004] [PMID] []
32. Asano J, Hirakawa A. Assessing the prediction accuracy of a cure model for censored survival data with long-term survivors: application to breast cancer data. Journal of Biopharmaceutical statistics. 2017;27(6):918-32. [DOI:10.1080/10543406.2017.1293082] [PMID]
33. Yazdani A, Yaseri M, Haghighat S, Kaviani A, Zeraati H. Investigation of prognostic factors of survival in breast cancer using a frailty model: a multicenter study. Breast cancer: basic and clinical research. 2019;13:1178223419879112. [DOI:10.1177/1178223419879112] [PMID] []
34. Andersson Y, Bergkvist L, Frisell J, de Boniface J. Long-term breast cancer survival in relation to the metastatic tumor burden in axillary lymph nodes. Breast Cancer Research and Treatment. 2018;171:359-9. [DOI:10.1007/s10549-018-4820-0] [PMID]
35. Bafford AC, Burstein HJ, Barkley CR, Smith BL, Lipsitz S, Iglehart JD, et al. Breast surgery in stage IV breast cancer: impact of staging and patient selection on overall survival. Breast cancer research and treatment. 2009;115:7-12. [DOI:10.1007/s10549-008-0101-7] [PMID]
36. Fallahpour S, Navaneelan T, De P, Borgo A. Breast cancer survival by molecular subtype: a population-based analysis of cancer registry data. Canadian Medical Association Open Access Journal. 2017;5(3):E734-E9. [DOI:10.9778/cmajo.20170030] [PMID] []
37. Fallahzadeh H, Mohammadzadeh M, Taghipour S, Pahlevani V. Assessment of AFT and Cox Models in Analysis of Factors Influencing the survival of Women with Breast Cancer in Yazd city. Journal of Babol University of Medical Sciences. 2018;20(5):74-80. [Persian]
38. Khodabakhshi R, Reza Gohari M, Moghadamifard Z, Foadzi H, Vahabi N. Disease-Free Survival of Breast Cancer Patients and Identification of Related Factors. Razi Journal of Medical Sciences. 2011;18(89)27-33. [Persian]
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