Boosting 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. 1 Introduction Precision medicine, as an emerging medical approach for disease treatment and prevention, has received more and more attention among government, healthcare industry and academia in recent years. It is a well-known fact that there exists a significant heterogeneity for patients in response to treatments. For example, as demonstrated in [9], for patients who are infected with human immunodeficiency virus and tuberculosis, their optimal timing of antiretroviral therapy (ART) varies significantly.