Semiparametric Analysis of Cure Rate Models using Soft Bayesian Additive Regression Tree

Music City Center 

Many existing parametric and semi-parametric techniques for cure rate survival models struggle to adequately capture the complex effects of covariates. To address this limitation, adopting more flexible modelling approaches is crucial for improving the accuracy of survival predictions. Survival data often involve right-censored observations, which present additional challenges. To handle these complexities, we focus on Bayesian methodologies for survival prediction leveraging a novel approach called Soft Bayesian Additive Regression Trees (SBART). This method combines multiple trees into a unified framework using Bayesian principles. Using this method we discuss two distinct cases: one where the data is unclustered and another where the data exhibits a clustered structure. To enhance computational efficiency, we introduce a data augmentation scheme to support the Bayesian backfitting algorithm. We illustrate the advantage of our model by simulation study and analysis of Melanoma data from e1684 clinical trial and Florida Cancer Registry Data.

Bayesian additive regression trees

Survival analysis

Cure rate model

Semiparametric 

Biometrics Section