  Many time-to-event studies are complicated by the presence of competing
risks. Such data are often analyzed using Cox models for the cause specific
hazard function or Fine-Gray models for the subdistribution hazard. In practice
regression relationships in competing risks data with either strategy are often
complex and may include nonlinear functions of covariates, interactions,
high-dimensional parameter spaces and nonproportional cause specific or
subdistribution hazards. Model misspecification can lead to poor predictive
performance. To address these issues, we propose a novel approach to flexible
prediction modeling of competing risks data using Bayesian Additive Regression
Trees (BART). We study the simulation performance in two-sample scenarios as
well as a complex regression setting, and benchmark its performance against
standard regression techniques as well as random survival forests. We
illustrate the use of the proposed method on a recently published study of
patients undergoing hematopoietic stem cell transplantation.

Methodology;Applications