Document Type : Research articles

Authors

• Enayatollah Bakhshi ¹
• Ayeh Sheikhaliyan ²
• Keivan Atashgar ³
• Maryam Kooshesh ⁴
• Akbar Biglarian ⁵

¹ Associate Professor of Biostatistics, Department of Biostatistics, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran

² MSc of Industrial Engineering, Department of Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran

³ Assistant Professor Industrial Engineering, Department of Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran

⁴ MD, Tehran University of Medical Sciences, Tehran, Iran

⁵ PhD, Associate Professor of Biostatistics, Department of Biostatistics, Pediatric Neurorehabilitation Research Center, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran

Abstract

Background: Breast cancer (BC) is the most leading cause of cancer and the second most common cause of cancer-related death among females worldwide. The survival time of the disease and its risk factors are important for physicians.
Objectives: The current study aimed at applying the Cox, cure, and frailty models to identify the risk factors related to the survival of patients with BC.
Methods: The current historical cohort study investigated 499 patients with a confirmed diagnosis of BC, from March 2010 to March 2014, and followed-up to March 2015 in Besaat hospital in Tehran, Iran. The Cox regression, cure, and frailty models were used for the survival analysis (SA) of the patients. Data analysis was carried out by R3.2.2 software.
Results: The mean (± SD) age of the patients was 50.39 (± 11.13) years and the mean survival time was 53.44 months (95% CI: 51.41 - 55.48). In addition, the 1-year overall survival rate was 0.92 (95% CI: 0.89 - 0.94). Age at diagnosis, tumor size, and metastasis covariates were significant in all models (P < 0.05). Stage covariate were significant in frailty, cure, and failure time distribution model (P < 0.001). Familial history (P = 0.016) and pathology (P = 0.012) were significant only in the frailty model.
Conclusions: The cure and frailty models were better than the Cox model to estimate the parameters. When some patients have a long-term survival, cure models can be an interesting method to study survival and also describe the short-term and long-term effects.

Keywords

• Survival Analysis
• Breast Cancer
• Cox Proportional Hazards Models
• Cured Model