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 re-ducing the heavy costs of breast cancer, as one of the most highly prevalent cancer throughout the world. there-fore, 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 impres-sive visual intuition potentiality.