Regression Trees for Cumulative Incidence Functions
This work addresses the need for machine learning methods in competing risks analysis, which is important for fields like medical research, but it is incremental as it extends existing regression tree techniques to this specific domain.
The authors tackled the problem of estimating cumulative incidence curves in competing risks settings by developing a novel regression tree approach using augmented Brier score risk estimators, and demonstrated its utility through simulation studies and application to clinical trial data.
The use of cumulative incidence functions for characterizing the risk of one type of event in the presence of others has become increasingly popular over the past decade. The problems of modeling, estimation and inference have been treated using parametric, nonparametric and semi-parametric methods. Efforts to develop suitable extensions of machine learning methods, such as regression trees and related ensemble methods, have begun only recently. In this paper, we develop a novel approach to building regression trees for estimating cumulative incidence curves in a competing risks setting. The proposed methods employ augmented estimators of the Brier score risk as the primary basis for building and pruning trees. The proposed methods are easily implemented using the R statistical software package. Simulation studies demonstrate the utility of our approach in the competing risks setting. Data from the Radiation Therapy Oncology Group (trial 9410) is used to illustrate these new methods.