Regression Trees for Cumulative Incidence Functions

TL;DR

This paper introduces a new regression tree method for estimating cumulative incidence functions in competing risks scenarios, enhancing predictive accuracy and interpretability in medical risk modeling.

Contribution

It develops a novel tree-based approach using augmented Brier score estimators for better estimation of cumulative incidence functions in competing risks.

Findings

Effective in simulation studies

Applicable to real clinical data

Easily implemented in R

Abstract

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.