Cox Model

The Cox proportional hazards model is a fundamental statistical method for analyzing time-to-event data, primarily aiming to identify factors influencing the risk of an event occurring. Current research focuses on enhancing the model's predictive power and addressing limitations like the proportional hazards assumption through techniques such as incorporating neural networks (including deep learning architectures like DeepSurv), stochastic gradient descent optimization, and federated learning for distributed data analysis. These advancements improve accuracy and allow for handling large, complex datasets while also exploring methods to maintain data privacy and enhance model interpretability for improved clinical decision-making and personalized medicine.