Abstract

Introduction: In analyzing time-to-event data, it is frequent that a proportion of individuals will never experience the event of interest. Using the cured models instead of standard ones for survival analysis will result in accurate estimates for predicting survival time. This study aimed to estimate the effect of potential risk factors on long-term and short-term survival of breast cancer patients. Methods: The study included 770 women diagnosed with early-stage breast cancer and treated with either breast conserving therapy or modified radical mastectomy with post-surgical radiation between 2005 and 2015. All study participants were followed up until September 2022. The review of the medical records of these patients was carried out at Shahid Sadoughi Hospital and the Ramazan Zadeh Radiotherapy Center in Yazd. Kaplan-Meier method was used to estimate disease-free survival and distant metastatic-free survival rates. Distant metastasis and prognostic factors were modeled by Cox proportional hazards and Semiparametric Mixture Cure models. Data analysis was done by Rstudio software. Results: In this study, 218(28.3) patients died, and metastasis occurred in 198(25.7) patients until the end of the follow-up period. Based on the univariable and multivariable Semiparametric Mixture Cure model, the stage of disease was significant, and the risk of short-term distant metastatic-free survival for patients with stage IIIa was 2.7 times higher than patients with stage I and II of disease through multivariable Semiparametric Mixture Cure model. Conclusion: The Semiparametric Mixture Cure model could estimate the long-term and short-term distant metastatic-free survival based on prognostic factors. Copyright © 2023 Fallah et al. Published by Breast Cancer Recearch Center, ACECR.