Boosting Algorithms for Estimating Optimal Individualized Treatment Rules
This work addresses the need for accurate treatment rule estimation in clinical settings, though it is incremental as it adapts existing boosting methods to this domain.
The authors tackled the problem of estimating optimal individualized treatment rules by developing nonparametric algorithms based on XGBoost, which overcome model specification challenges in parametric methods and show superior performance in simulations and a real diabetes trial.
We present nonparametric algorithms for estimating optimal individualized treatment rules. The proposed algorithms are based on the XGBoost algorithm, which is known as one of the most powerful algorithms in the machine learning literature. Our main idea is to model the conditional mean of clinical outcome or the decision rule via additive regression trees, and use the boosting technique to estimate each single tree iteratively. Our approaches overcome the challenge of correct model specification, which is required in current parametric methods. The major contribution of our proposed algorithms is providing efficient and accurate estimation of the highly nonlinear and complex optimal individualized treatment rules that often arise in practice. Finally, we illustrate the superior performance of our algorithms by extensive simulation studies and conclude with an application to the real data from a diabetes Phase III trial.