The Role of Survival Analysis in Modeling Time-to-Event Data and Predicting Hazard Rates in Medical Studies

Posted: Jan 19, 2024

• Zoey Martin
• Mia Flores
• Sarah Torres

Abstract

Survival analysis represents a cornerstone methodology in medical research, providing essential tools for understanding time-to-event data across diverse clinical contexts. The fundamental challenge in medical studies lies in accurately predicting when specific health events will occur, whether considering disease progression, treatment response, or adverse outcomes. Traditional survival methods have served the research community well, but the increasing complexity of medical data and the need for more precise predictions demand innovative approaches. This research addresses critical gaps in current survival analysis methodologies by developing a hybrid framework that integrates the interpretability of classical statistical methods with the predictive power of modern machine learning techniques. The novelty of our approach stems from several key innovations including dynamic feature engineering, integration of Cox proportional hazards models with gradient boosting machines, and addressing the challenge of handling high-dimensional medical data while maintaining clinical interpretability.