Proportional Hazard

Proportional hazards modeling aims to predict the time until an event, such as death or disease recurrence, considering various factors influencing survival. Current research emphasizes overcoming limitations of traditional proportional hazards models, particularly the assumption of constant covariate effects over time, by employing flexible neural network architectures like transformers and residual networks, often incorporating techniques like frailty models to account for unobserved heterogeneity. These advancements improve predictive accuracy and allow for the analysis of complex, high-dimensional data, such as genomic profiles and medical images, leading to more precise risk stratification and personalized treatment strategies in fields like oncology.