Assessment of the fitness of Cox and parametric regression models of survival distribution for Iranian breast cancer patients' data - Maryam Mohseny; Reza Shekarriz-Foumani; Parastoo Amiri; Marjan Vejdani; Pezhman Farshidmehr; Hossein Zabihi Mahmoudabadi; Farzaneh Amanpour; Pegah Mohaghegh; Farzad Tajdini; Azadeh Sayarifard; Esmat Davoudi-Monfared (Wolters Kluwer Medknow Publications, 2019)
Factors affecting the time of survival after breast cancer (BC) diagnosis remain unknown. However, some of the prognostic factors have been identified. The aim of this study was to investigate the effects of biologic and socioeconomic factors on long-term survival of BC patients. This was a descriptive chart review and survey of all women with a confirmed diagnosis of BC registered in Shohada-e-Tajrish Cancer Research Center database from March 2004 to March 2015. The checklist of study consisted of biologic, demographic, reproductive, genetic, medical, and therapeutic information of patients. The minimum time of follow-up was 3 years and the maximum was 10 years. We then evaluated possible associations of these variables with BC survival using Cox and parametric regression models of survival analysis. The study population was 1276 BC patients. Their mean survival was 23 (range 1-120) months. Between the parametric models, Weibull regression model demonstrated the lowest Akaike information criterion and thus the best fit, and tumor size, number of lymph nodes, BC stage, educational level, and high-fat diet were significant in this model. Based on our findings, educational level, consumption of fat, and characteristics of tumor at the time of diagnosis (disease stage, tumor size, number of involved lymph nodes) are the most important prognostic factors affecting long-term survival of BC patients. We suggest that future studies assess the efficacy of possible interventions for these factors.