Original Article

Model-based Recursive Partitioning for Survival of Iranian Female Breast Cancer Patients: Comparing with Parametric Survival Models


Background: Precise diagnosis of disease risk factors via efficient statistical models is the primary step for reducing the heavy costs of breast cancer, as one of the most highly prevalent cancer throughout the world. Therefore, the aim of this study was to present a recently introduced statistical model in order to assess its proficiency for model fitting.

Methods: The information of 1465 eligible Iranian women with breast cancer was used for this retrospective cohort study. The statistical performances of exponential, Weibull, Log-logistic and Lognormal, as the most proper parametric survival models, were evaluated and compared with 'Model-based Recursive Partitioning' in order to survey their capability of more relevant risk factor detection.

Results: 'Model-based Recursive Partitioning' recognized the largest number of significant affective risk factors, whereas, all four parametric models agreed and unable to detect the effectiveness of 'Progesterone Receptor' as an indicator; 'Log-Normal-based Recursive Partitioning' could provide the paramount fit.

Conclusion: The superiority of 'Model-based Recursive Partitioning' was ascertained; not only by its excellent fitness but also by its susceptibility for classification of individuals to homogeneous severity levels and its impressive visual intuition potentiality.


Liang B, Yunhui L (2014). Prognostic Significance of VEGF-C Expression in Patients with Breast Cancer: A Meta-Analysis. Iran J Public Health, 43(2):128-35.

Mousavi SM, Mohaghegghi MA, Mousavi-Jerrahi A, Nahvijou A, Seddighi Z (2006). Burden of breast cancer in Iran: a study of the Tehran population-based cancer registry. Asian Pac J Cancer Prev, 7(4):571-4.

Cui J, Zhou L, Wee B, Shen F, Ma X, Zhao J (2014). Predicting Survival Time in Noncurative Patients with Advanced Cancer: A Prospective Study in China. J Palliat Med, 17(5):545-52.

Baneshi M, Talei A (2012). Assessment of Internal Validity of Prognostic Models through Bootstrapping and Multiple Imputation of Missing Data. Iran J Public Health, 41(5):110-5.

Rashidian A, Barfar E, Hosseini H, Nosratnejad S, Barooti E (2013). Cost effectiveness of breast cancer screening using mammography; a systematic review. Iran J Public Health, 42(4):347-57.

Faradmal J, Talebi A, Rezaianzadeh A, Mahjub H (2012). Survival analysis of breast cancer patients using cox and frailty models. J Res Health Sci, 12(2):127-30.

Kurt I, Ture M, Kurum AT (2008). Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease. Expert Syst Appl, 34(1):366-74.

Süt N, Şenocak M (2007). Assessment of the performances of multilayer perceptron neural networks in comparison with recurrent neural networks and two statistical methods for diagnosing coronary artery disease. Expert Syst, 24(3):131-42.

Nilsson J, Ohlsson M, Thulin L, Höglund P, Nashef SA, Brandt J (2006). Risk factor identification and mortality prediction in cardiac surgery using artificial neural networks. J Thorac Cardiovasc Surg, 132(1):12-9.

Shi HY, Hwang SL, Lee KT, Lin CL (2013). In-hospital mortality after traumatic brain injury surgery: a nationwide population-based comparison of mortality predictors used in artificial neural network and logistic regression models: clinical article. J Neurosurg, 118(4):746-52.

Zeileis A, Hothorn T, Hornik K (2006). Evaluating model-based trees in practice (Tech. Rep. No. 32). Vienna, Austria: Vienna University of Economics and Business, Department of Statistics and Mathematics.

Hothorn T, Bühlmann P, Dudoit S, Molinaro A, Van Der Laan MJ (2006). Survival ensembles. Biostatistics, 7(3):355-73.

Faradmal J, Mafi M, Sadighi-Pashaki A, Karami M, Roshanaei G (2014). Factors Affecting Survival in Breast Cancer Patients Referred to the Darol Aitam-e Mahdieh Center. J Zanjan Univ Med Sci, 22(93):105-15.

Baghestani A, Moghaddam S, Majd H, Akbari M, Nafissi N, Gohari K (2015). Survival Analysis of Patients with Breast Cancer using Weibull Parametric Model. Asian Pac J Cancer Prev, 16(18):8567-71.

Ahmad LG, Eshlaghy AT, Poorebrahimi A, Ebrahimi M and Razavi AR (2013). Using three machine learning techniques for predicting breast cancer recurrence. J Health Med Inform, 4:124.

Faradmal J, Soltanian AR, Roshanaei G, Khodabakhshi R, Kasaeian A (2014). Comparison of the performance of log-logistic regression and artificial neural networks for predicting breast cancer relapse. Asian Pac J Cancer Prev, 15(14):5883-8.

Salehi M, Gohari M, Vahabi N, Zayeri F, Yahyazadeh S, Kafashian M (2013). Comparison of artificial neural network and cox regression models in survival prediction of breast cancer patients. J Ilam Univ Med Sci, 21(2):120-8.

Zeileis A, Hothorn T, Hornik K (2008). Model-based recursive partitioning. J Comp Graph Stat, 17(2):492-514.

Corbiere F, Joly P (2007). A SAS macro for parametric and semiparametric mixture cure models. Comput Methods Programs Biomed, 85(2):173-80.

Basu S, Tiwari RC (2010). Breast cancer survival, competing risks and mixture cure model: a Bayesian analysis. J R Stat Soc Ser A, 173(2):307-29.

Woods L, Rachet B, Lambert P, Coleman M (2009). ‘Cure’from breast cancer among two populations of women followed for 23 yr after diagnosis. Ann Oncol, 20(8):1331-6.

Jafari-Koshki T, Mansourian M, Mokarian F (2014). Exploring Factors Related to Metastasis Free Survival in Breast Cancer Patients Using Bayesian Cure Models. Asian Pac J Cancer Prev, 15(22):9673-8.

Mousavi SM, Montazeri A, Mohagheghi MA, Jarrahi AM, Harirchi I, Najafi M, Ebrahimi M (2007). Breast cancer in Iran: an epidemiological review. Breast J, 13(4):383-91.

Rouzier R, Coutant C, Lesieur B, Mazouni C, Incitti R, Natowicz R, Pusztai L (2009). Direct comparison of logistic regression and recursive partitioning to predict chemotherapy response of breast cancer based on clinical pathological variables. Breast Cancer Res Treat, 117(2):325-31.

Rondeau V, Schaffner E, Corbière F, Gonzalez JR, Mathoulin-Pélissier S (2013). Cure frailty models for survival data: Application to recurrences for breast cancer and to hospital readmissions for colorectal cancer. Stat Methods Med Res, 22(3):243-60.

Yin Y, Anderson SJ (2001). Exponential Tree-Structured Modeling for Interval-Censored Survival Data. Proc Am Stat Assoc, Biometrics Section. Alexandria, VA: American Statistical Association.

Kim H, Loh WY (2011). Classification trees with unbiased multiway splits. J Amer Statist Assoc, 96:598-604.

IssueVol 46 No 1 (2017) QRcode
SectionOriginal Article(s)
Breast cancer Parametric survival model Recursive partitioning

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How to Cite
SAFE M, FARADMAL J, POOROLAJAL J, MAHJUB H. Model-based Recursive Partitioning for Survival of Iranian Female Breast Cancer Patients: Comparing with Parametric Survival Models. Iran J Public Health. 2017;46(1):35-43.