STATISTICAL MODELING OF BREAST CANCER RELAPSE TIMES: A COMPARATIVE ANALYSIS OF DIFFERENT TREATMENT APPROACHES

Abstract

Breast cancer relapse time is a critical factor influencing patient outcomes and the effectiveness of various treatment strategies. This study presents a comprehensive comparative analysis of relapse times across different breast cancer treatments using advanced statistical modeling techniques. The primary objective is to evaluate the impact of various therapeutic approaches on relapse intervals and identify key factors that contribute to treatment efficacy and patient prognosis. We analyzed data from multiple clinical trials, encompassing a range of treatment modalities including surgery, chemotherapy, radiation therapy, and targeted therapies.

Our methodology involved applying survival analysis techniques, such as Kaplan-Meier estimators and Cox proportional hazards models, to assess and compare relapse times. These models account for both the time-to-event data and the influence of covariates, providing a nuanced understanding of how different treatments affect relapse rates. Additionally, we employed competing risks models to handle scenarios where patients experience different types of relapse events or where competing risks might bias the analysis.

The results reveal significant variations in relapse times based on the treatment type. For instance, patients receiving combined modalities, such as surgery followed by chemotherapy, exhibited longer relapse-free intervals compared to those undergoing single-modal treatments. Furthermore, the study highlights the role of patient-specific factors, such as age, tumor stage, and genetic markers, in modifying treatment outcomes. These factors were integrated into the models to refine predictions and offer personalized insights into treatment effectiveness.

Our findings underscore the importance of tailored treatment approaches and suggest that integrating multiple therapies may enhance relapse-free survival rates. The statistical models employed provide a robust framework for future research and clinical decision-making, allowing for more accurate predictions of relapse times and better-informed choices of treatment strategies. This study contributes valuable insights into optimizing breast cancer management and improving patient care through data-driven approaches and advanced statistical methodologies.