METHODS OF SURVIVAL ANALYSIS IN MEDICAL DATA: AN INTEGRATED REVIEW

Abstract

Survival analysis is an indispensable statistical methodology in medical research, providing critical insights into the time until the occurrence of specific events such as disease onset, recurrence, or death. This review systematically examines the foundational, advanced, and emerging methods of survival analysis applied to medical data, encompassing parametric, non-parametric, and semi-parametric approaches. We detail established techniques like the Kaplan-Meier estimator and Cox Proportional Hazards model, alongside their applications and underlying assumptions. Furthermore, this article explores the increasing integration of machine learning and deep learning algorithms, such as Random Survival Forests and DeepHit, which address the complexities of high-dimensional data and distribution shifts. We discuss the implications of these methods for clinical decision-making and personalized medicine, while also critically evaluating their limitations, including issues with proportional hazards assumptions, competing risks, and data censoring. The paper concludes with an outlook on future directions, emphasizing the continuous evolution of survival analysis in the era of big data and artificial intelligence.