文章基本信息

标题：Model-based Recursive Partitioning for Survival of Iranian Female Breast Cancer Patients: Comparing with Parametric Survival Models
其他标题：Model-based Recursive Partitioning for Survival of Iranian Female Breast Cancer Patients: Comparing with Parametric Survival Models
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作者：Mozhgan SAFE ; Javad FARADMAL ; Jalal POOROLAJAL 等
期刊名称：Iranian Journal of Public Health
印刷版ISSN：2251-6085
电子版ISSN：2251-6093
出版年度：2017
卷号：46
期号：1
页码：35-43
语种：English
出版社：Tehran University of Medical Sciences
摘要：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.
关键词：Breast cancer; Parametric survival model; Recursive partitioning