Published Paper Details:

Statistical methods for survival data analysis have continued to flourish in the last two decades. Applications of the methods have broadened from their historical use in cancer and reliability research used in the fields of business, criminology, epidemiology, and social and behavioral sciences. Survival analysis consists of studies of the survival time of a subject (days, weeks, months, or years), which is the time that elapses between the baseline and the moment an adverse event occurs, or the subject drops out of the trial. A retrospective study of 468 breast cancer patterns over a period of one year, i.e., from December, 2021 to November 2022, was collected from the medical records at the Jakka Sujathamma Cancer Detection Centre, Nellore, Andhra Pradesh, India. The log-rank test revealed a statistically significant difference in patient survival times, and it can be deduced that tumor size, family history, advanced stage, and age all have an impact on the disease. The three main risk factors for breast cancer death are stage, grade, and tumor size. Age is one of the most significant risk factors in the Cox PH model analysis; as age rises, so does the likelihood of developing breast cancer.