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Heterogeneous treatment effects (HTE) based on patients' genetic or clinicalfactors are of significant interest to precision medicine. Simultaneouslymodeling HTE and corresponding main effects for randomized clinical trials withhigh-dimensional predictive markers is challenging. Motivated by the modifiedcovariates approach, we propose a two-stage statistical learning procedure forestimating HTE with optimal efficiency augmentation, generalizing to arbitraryinteraction model and exploiting powerful extreme gradient boosting trees(XGBoost). Target estimands for HTE are defined in the scale of mean differencefor quantitative outcomes, or risk ratio for binary outcomes, which are theminimizers of specialized loss functions. The first stage is to estimate themain-effect equivalency of the baseline markers on the outcome, which is thenused as an augmentation term in the second stage estimation for HTE. Theproposed two-stage procedure is robust to model mis-specification of maineffects and improves efficiency for estimating HTE through nonparametricfunction estimation, e.g., XGBoost. A permutation test is proposed for globalassessment of evidence for HTE. An analysis of a genetic study in ProstateCancer Prevention Trial led by the SWOG Cancer Research Network, is conductedto showcase the properties and the utilities of the two-stage method.

Augmented Learning of Heterogeneous Treatment Effects via Gradient Boosting Trees