Stratified Cox Regression Approach to Identifying Prognostic Factors for Survival in Breast Cancer Patients

Abstract

The most common type of cancer that affects women is Breast cancer. In 2022, 2.3 million women were diagnosed with breast cancer, and 670,000 deaths were recorded globally. By 2040, it is estimated that breast cancer will increase by 40%, reaching 3 million annually with the number of deaths increasing by 50% to 1 million in 2020. This highlights breast cancer as a serious threat to world health. This study utilized secondary data from METABRIC or the Molecular Taxonomy of Breast Cancer International Consortium obtained from the website www.kaggle.com/datasets/raghadalharbi/breast-cancer-gene-expression-profiles-metabric/data. The independent variables analyzed were, Age at Diagnosis (X­­₁), Surgery Type (X­­₂), Chemotherapy (X­­₃), Hormone Therapy (X­­₄), Tumor Size (X­­₅), Radio Therapy (X­­₆), Pam50. The dependent variables were Survival Time (Overall Survival Month) and Patient Status. In this study, we used the Stratified Cox model to predict the predictor variables of survival time. The total number of patients used was 18886, with 1080 censored patients and 788 uncensored patients. The Stratified Cox model without interaction revealed that the patients who underwent breast-conserving surgery had a 1.35 times higher risk of death compared to those who underwent mastectomy. Patients who received chemotherapy had a 2.01 times higher risk of death than those who did not, while patients who did hormone therapy had a 1.83 times higher risk of death than those who did not undergo this therapy.