Generalized Bayesian Additive Regression Trees for Restricted Mean Survival Time Inference

Music City Center 

We introduce a generalized Bayes framework for predicting individual-level restricted mean survival times (RMST) without relying on strict survival model assumptions. Our method employs an RMST-targeted loss function using inverse probability of censoring weights (IPCW), enabling the handling of informative censoring by modeling only the censoring distribution. We incorporate a flexible additive tree regression model and construct pseudo-Bayesian posteriors via model-averaging IPCW-conditional loss functions. Through simulations and application to a multi-site breast cancer cohort, we demonstrate improved predictive performance over standard survival machine learning methods. Additionally, we will describe how this framework can be extended to perform dynamic RMST prediction.

dependent censoring, ensemble methods, Gibbs posterior, inverse weighting, loss function, survival analysis. 

Section on Bayesian Statistical Science