BDNNSurv: Bayesian deep neural networks for survival analysis using pseudo values
This work addresses the problem of providing uncertainty quantification for survival predictions, which is crucial for medical decision-making.
This paper proposes a Bayesian hierarchical deep neural network model, BDNNSurv, for survival analysis. It provides both point estimates of survival probability and quantifies the corresponding uncertainty, demonstrating favorable statistical properties in simulations and real data.
There has been increasing interest in modeling survival data using deep learning methods in medical research. In this paper, we proposed a Bayesian hierarchical deep neural networks model for modeling and prediction of survival data. Compared with previously studied methods, the new proposal can provide not only point estimate of survival probability but also quantification of the corresponding uncertainty, which can be of crucial importance in predictive modeling and subsequent decision making. The favorable statistical properties of point and uncertainty estimates were demonstrated by simulation studies and real data analysis. The Python code implementing the proposed approach was provided.