Competing Risk

Competing risks analysis addresses situations where individuals face multiple potential events, such as death from different causes or disease progression, and only the first event is observed. Current research focuses on improving the accuracy and efficiency of predicting these competing events using various models, including gradient boosting trees, dynamic survival analysis frameworks, and deep neural networks tailored to handle censored and clustered data. These advancements are crucial for improving prognostication in diverse fields like healthcare (e.g., predicting patient outcomes after cardiac arrest) and for developing more robust risk assessment tools in various applications.