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

Manimannan G; N. Paranjothi; A. Poongothai

doi:10.69980/cgqz9g40

Authors

Manimannan G Assistant Professor, Department of Computer Applications, St. Joseph’s College (Arts and Science), Kovur, Chennai
N. Paranjothi Assosicate Professor, Department of Statistics, Annamalai University, Chidambaram
A. Poongothai Assistant Professor, Department of Statistics, Muthayammal Arts and Science College, Rasipuram, Salem

DOI:

https://doi.org/10.69980/cgqz9g40

Keywords:

Breast Cancer, Survival Analysis, Kaplan–Meier, Cox Proportional Hazards, Clinical Parameters, Cumulative Hazar

Abstract

Background: Breast cancer continues to be a major health concern, where patient survival is influenced by multiple clinical and physiological factors. Understanding the relationship between these factors and survival outcomes is essential for improving patient management and early intervention strategies.

Objective: The present study aims to evaluate the survival patterns of breast cancer patients and to examine the influence of selected clinical parameters on time-to-event outcomes using appropriate statistical modelling techniques.

Methods: The study is based on secondary clinical data collected from 500 breast cancer patients who underwent routine health assessment. Key variables considered include age, body mass index (BMI), heart rate (average), position score (average), systolic and diastolic blood 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 to identify significant predictors, and the Nelson–Aalen estimator to assess cumulative hazard. Model performance was evaluated using the concordance index and likelihood-based measures.

Results:The median survival time was estimated to be 2.2 hours, indicating early occurrence of events within the study period. The Cox regression analysis revealed that BMI, systolic blood pressure, position score, medical progression score, and symptom severity score have a statistically significant effect on survival outcomes. Among these, medical progression score showed a strong positive association with risk, while symptom severity score demonstrated a protective effect. The concordance index value of 0.66 indicates moderate predictive performance of the model. The cumulative hazard analysis showed a steady increase in risk over time, with a noticeable rise in later intervals.

Conclusion: The findings confirm that survival outcomes in breast cancer patients are significantly influenced by selected clinical parameters. The application of survival analysis techniques provides meaningful insights into disease progression and risk patterns. The study supports the importance of early monitoring and the integration of statistical modelling in clinical decision-making for improved patient care.

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