Survival Analysis Method

Survival analysis aims to model the time until an event occurs, accounting for censoring (incomplete event data), a common challenge in fields like healthcare. Recent research emphasizes developing flexible models that relax traditional assumptions, such as proportional hazards, and improve prediction accuracy using machine learning techniques like deep learning (e.g., DeepSurv, DeepHit), random survival forests, and gradient boosting machines, often compared against established methods like Cox proportional hazards models. These advancements are crucial for improving prognostic predictions in various applications, particularly in healthcare where accurate risk stratification and personalized treatment strategies are paramount. Furthermore, there's a growing focus on developing more interpretable models and addressing challenges inherent in decentralized data settings, such as federated learning approaches.