The Effects of Prognostic Factors on Metastasis and Survival of Patients with Breast Cancer Using a Multi-State Model

Babaee, Ebrahim; Nafissi, Nahid; Tehrani-Banihashemi, Arash; Eshrati, Babak; Janani, Leila; Nojomi, Marzieh

doi:10.30476/mejc.2021.89311.1519

The Effects of Prognostic Factors on Metastasis and Survival of Patients with Breast Cancer Using a Multi-State Model

Document Type : Original Article(s)

Authors

Ebrahim Babaee ¹

Nahid Nafissi ²

Arash Tehrani-Banihashemi ¹

Babak Eshrati ¹

Leila Janani ¹

Marzieh Nojomi ¹^{, 3}

¹ Preventive Medicine and Public Health Research Center, Psychosocial Health Research Institute, Community and Family Medicine Department, School of Medicine, Iran University of Medical Sciences, Tehran, Iran

² Breast Department, School of Medicine, Iran University of Medical Sciences, Tehran, Iran

³ Department of Sociology and Anthropology, Nipissing University, North Bay, Ontario, Canada

https://doi.org/10.30476/mejc.2021.89311.1519

Abstract

Background: The multi-state models help more closely study of the factors affecting the survival of patients with breast cancer.
Method: We conducted the present retrospective cohort study on 2030 Iranian patients with breast cancer in 2020. The patients’ follow-up period ranged from 1 month to 15 years. Accordingly, the initial treatment, metastasis, and death were considered as the first, second, and absorbing states, respectively. The multi-state model was utilized for modeling and analyzing the data at a 95% significance level using the MSM package in R software.
Results: The mean age (± standard deviation) of the patients included at diagnosis time was 55.3 (±12.07) years old. The first one year and 5 years adjusted transition probabilities for transitions from the treatment to metastasis estimated as 0.85 (0.15 – 0.89) and 0.45 (0.21 – 0.61), and for metastasis to death transitions, they were estimated as 0.15 (0.1 – 0.21) and 0.55 (0.41 - 0.69), respectively. Moreover, the average sojourn times were estimated as 0.27 and 74.85 months for the treatment and metastasis states, respectively.
Conclusion: The obtained results revealed that over time, the transition probabilities of patients from surgery to metastasis state decreased, whereas the transition probabilities from metastasis to death state increased using the multi-state model.

Keywords

Multi-state model

Prognostic factors

Survival analysis

Breast cancer

How to cite this article:

Babaee E, Nafissi N, Tehrani-Banihashemi A, Eshrati B, Janani L, Nojomi M. The effects of prognostic factors on metastasis and survival of patients with breast cancer using a multi-state model. Middle East J Cancer. 2022;13(1):150-8. doi: 10.30476/mejc.2021. 89311.1519.

1.Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65(2):87-108. doi: 10.3322/caac.21262.
2.Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin. 2019;69(1):7-34. doi: 10.3322/caac.21551.
3.Global Burden of Disease Cancer Collaboration, Fitzmaurice C, Abate D, Abbasi N, Abbastabar H, Abd-Allah F, et al. Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 29 cancer groups, 1990 to 2017: A systematic analysis for the global burden of disease study. JAMA Oncol. 2019;5(12):1749-68. doi: 10.1001/jamaoncol. 2019.2996.
4.DeSantis CE, Miller KD, Goding Sauer A, Jemal A, Siegel RL. Cancer statistics for African Americans, 2019. CA Cancer J Clin. 2019;69(3):211-33. doi: 10.3322/caac.21555.
5.Roshandel G, Ghanbari-Motlagh A, Partovipour E, Salavati F, Hasanpour-Heidari S, Mohammadi G, et al. Cancer incidence in Iran in 2014: results of the Iranian National Population-based Cancer Registry. Cancer Epidemiol. 2019;61:50-8. doi.org/10.1016/j. canep.2019.05.009.
6.Shahkhodabandeh S, Piri Z, Biglo M, Asadi M, Chakhmachi DN. Breast cancer in Iran: Iranian scientists approach to breast cancer researchers in Medline database. [In Persian] Iran J Breast Dis. 2009;2(2): 49-59.
7.Nafissi N, Khayamzadeh M, Zeinali Z, Pazooki D, Hosseini M, Akbari ME. Epidemiology and histopathology of breast cancer in iran versus other middle eastern countries. Middle East J Cancer. 2018;9(3):243-51. doi.10.30476/MEJC.2018.42130.
8.Early Breast Cancer Trialists' Collaborative Group (EBCTCG). Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials. Lancet. 2005;365(9472):1687-717. doi: 10.1016/ S0140-6736(05)66544-0.
9.Saphner T, Tormey DC, Gray R. Annual hazard rates of recurrence for breast cancer after primary therapy. J Clin Oncol. 1996;14(10):2738-46. doi: 10.1200/JCO. 1996.14.10.2738.
10.Mazroui Y, Mathoulin-Pélissier S, Macgrogan G, Brouste V, Rondeau V. Multivariate frailty models for two types of recurrent events with a dependent terminal event: application to breast cancer data. Biom J. 2013;55(6):866-84. doi: 10.1002/bimj.201200196.
11.Putter H, van der Hage J, de Bock GH, Elgalta R, van de Velde CJ. Estimation and prediction in a multi-state model for breast cancer. Biom J. 2006;48(3): 366-80. doi: 10.1002/bimj.200510218.
12.Grover G, Swain PK, Goel K, Singh V. Multistate Markov modelling for disease progression of breast cancer patients based on CA15-3 marker. Thailand Statistician. 2018;16(2):129-39.
13.Hosseni AF, Gohari MR. Application of multilevel model in determining the effective factors in the length of stay among appendectomy patients. [In Persian] Razi Journal of Medical Sciences (RJMS). 2014;20(115):70-7.
14.Raesizadeh M, Seghatoleslami M, Hoseinzade M, Saki Malehi A. Survival analysis of breast cancer patients according to intermediate and endpoint events: Applying illness-death model. [In Persian] The Iranian Journal of Epidemiology (IJE). 2018;13(4):291-8.
15.Amirkafi M. The significance and logic of multilevel models in social research. [In Persian] Iranian J of Sociology. 2007;7(4):38-71.
16.Correa C, Harris EE, Leonardi MC, Smith BD, Taghian AG, Thompson AM, et al. Accelerated partial breast irradiation: Executive summary for the update of an ASTRO Evidence-Based Consensus Statement. Pract Radiat Oncol. 2017;7(2):73-9. doi: 10.1016/j.prro. 2016.09.007.
17.Broët P, de la Rochefordière A, Scholl SM, Fourquet A, De Rycke Y, Pouillart P, et al. Analyzing prognostic factors in breast cancer using a multistate model. Breast Cancer Res Treat. 1999;54(1):83-9. doi: 10.1023/a:1006197524405.
18.Putter H, Fiocco M, Geskus RB. Tutorial in biostatistics: competing risks and multi-state models. Stat Med. 2007;26(11):2389-430. doi: 10.1002/ sim.2712.
19.Faradmal J, Mafi M, Sadighi-Pashaki A, Karami M, Roshanaei G. Factors affecting survival in breast cancer patients referred to the darol aitam-e mahdieh center. [In Persian] J Adv Med Biomed Res. 2014; 22(93):105-15.
20. De Bock G, Putter H, Bonnema J, Van Der Hage J, Bartelink H, Van De Velde C. The impact of loco-regional recurrences on metastatic progression in early-stage breast cancer: a multistate model. Breast Cancer Res Treat. 2009;117(2):401-8. doi: 10.1007/ s10549-008-0300-2.
21.Hajihosseini M, Faradmal J, Sadighi-Pashaki A. Survival analysis of breast cancer patients after surgery with an intermediate event: application of illness-death model. Iran J Public Health. 2015;44(12):1677.
22.Lakzaei M, Salarilak S, Khalkhali HR, Maleki D, Esnaashari O. Association between age of morbidity and prognosis of breast cancer. [In Persian] Studies in Medical Sciences. 2015;26(7):625-33.
23.Moghadami FZ, Abolghasemi J, Asgari DA, Gohari M. Survival analysis of patients with breast cancer using the Aalen's additive hazard model. [In Persian] Journal of North Khorasan University of Medical Sciences (NKUMS). 2011;3:171-80. doi: 10.29252/ jnkums.3.5.s5.171.
24.Haffty BG, Reiss M, Beinfield M, Fischer D, Ward B, McKhann C. Ipsilateral breast tumor recurrence as a predictor of distant disease: implications for systemic therapy at the time of local relapse. J Clin Oncol. 1996;14(1):52-7. doi: 10.1200/JCO.1996. 14.1.52.
25.Jackson CH. Multi-state models for panel data: the msm package for R. J Stat Softw. 2011;38(8):1-29. doi: 10.18637/jss.v038.i08.
26.Fisher B, Anderson S, Fisher ER, Redmond C, Wickerham DL, Wolmark N, et al. Significance of ipsilateral breast tumour recurrence after lumpectomy. Lancet. 1991;338(8763):327-31. doi: 10.1016/0140-6736(91)90475-5.
27.Saint-Pierre P, Combescure C, Daurès JP, Godard P. The analysis of asthma control under a Markov assumption with use of covariates. Stat Med. 2003;22(24):3755-70. doi: 10.1002/sim.1680.