Regression Trees and Ensembles for Cumulative Incidence Functions

TL;DR

This paper introduces a new machine learning approach using regression trees and ensembles to estimate cumulative incidence functions in competing risks scenarios, improving modeling flexibility and implementation ease.

Contribution

It develops a novel method for estimating cumulative incidence curves with regression trees and ensembles, based on augmented Brier score estimators, suitable for practical use.

Findings

Effective estimation of cumulative incidence functions demonstrated on clinical data.

Methods are easily implemented using existing R packages.

Provides a flexible alternative to traditional parametric and nonparametric models.

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 comparatively recently. In this paper, we propose a novel approach to estimating cumulative incidence curves in a competing risks setting using regression trees and associated ensemble estimators. The proposed methods employ augmented estimators of the Brier score risk as the primary basis for building and pruning trees, and lead to methods that are easily implemented using existing R packages. Data from the Radiation Therapy Oncology Group (trial 9410) is used to illustrate these new methods.