Time to Event

Time-to-event analysis, also known as survival analysis, focuses on predicting the time until a specific event occurs, considering factors like censoring (incomplete data due to the event not happening within the observation period). Current research emphasizes developing sophisticated models, including neural networks (e.g., deep learning approaches, neural ODEs), and adapting existing methods like Cox proportional hazards and accelerated failure time models to handle complex data structures and competing risks (multiple potential events). This field is crucial for various applications, particularly in healthcare (e.g., predicting disease progression, patient mortality), where accurate time-to-event predictions improve risk stratification, treatment decisions, and resource allocation.