SURVIVAL ANALYSIS OF BREAST CANCER PATIENTS USING CLINICAL RISK PARAMETERS: A STATISTICAL APPROACH BASED ON TIME-TO-EVENT MODELLING

ABSTRACTBackground: Breast cancer continues to be a major health concern, where patient survival isinfluenced by multiple clinical and physiological factors. Understanding the relationshipbetween these factors and survival outcomes is essential for improving patient managementand early intervention strategies.Objective: The present study aims to evaluate the survival patterns of breast cancer patientsand to examine the influence of selected clinical parameters on time-to-event outcomes usingappropriate statistical modelling techniques.Methods: The study is based on secondary clinical data collected from 500 breast cancerpatients who underwent routine health assessment. Key variables considered include age,body mass index (BMI), heart rate (average), position score (average), systolic and diastolicblood pressure (average), oxygen saturation (average), medical progression score (average),and symptom severity score (average). Survival analysis was carried out using the Kaplan–Meier estimator to estimate survival probabilities, the Cox proportional hazards model toidentify significant predictors, and the Nelson–Aalen estimator to assess cumulative hazard.Model performance was evaluated using the concordance index and likelihood-basedmeasures.Results: The median survival time was estimated to be 2.2 hours, indicating early occurrenceof events within the study period. The Cox regression analysis revealed that BMI, systolicblood pressure, position score, medical progression score, and symptom severity score have astatistically significant effect on survival outcomes. Among these, medical progression scoreshowed a strong positive association with risk, while symptom severity score demonstrated aprotective effect. The concordance index value of 0.66 indicates moderate predictiveperformance of the model. The cumulative hazard analysis showed a steady increase in riskover time, with a noticeable rise in later intervals.Conclusion: The findings confirm that survival outcomes in breast cancer patients aresignificantly influenced by selected clinical parameters. The application of survival analysistechniques provides meaningful insights into disease progression and risk patterns. The studysupports the importance of early monitoring and the integration of statistical modelling inclinical decision-making for improved patient care.