Survival Analysis with Cox Proportional Hazards Model in Predicting Patient Outcomes

Abstract

Survival analysis is crucial for understanding the factors that influence patient outcomes across time. The objective is to predict the outcomes of patient survival under various circumstances using the Cox Proportional Hazards Model. The main objectives are to assess the model's predictive ability and to determine the impact of different parameters on survival lengths. As a result of these efforts to enhance clinical prediction models, patient treatment strategies that are personalized to each person's individual risk profile will be improved. Integrating diverse patient data and evaluating the importance of various elements might improve insights into therapy success and prognosis accuracy. Improved healthcare delivery and individualized medical treatments are possible outcomes. This approach brings attention to the use of advanced statistical techniques in medical diagnosis and decision-making, which may improve patients' quality of life and raise their chances of survival. Age range: 58–72, stages: 1–4, comorbidities: yes (1, no), Karnofsky score: 60–90, follow-up: 12–30 months, confidence interval: 0.2% to 10.3%, hazard ratio: 0.8-3.5, p-value: 0.08-0.67, according to the Patient Survival Analysis Dataset, Stage-Specific Cancer Survival Analysis Dataset.