Augmented Learning of Heterogeneous Treatment Effects via Gradient Boosting Trees. (arXiv:2302.01367v1 [stat.ML])

Heterogeneous treatment effects (HTE) based on patients’ genetic or clinical
factors are of significant interest to precision medicine. Simultaneously
modeling HTE and corresponding main effects for randomized clinical trials with
high-dimensional predictive markers is challenging. Motivated by the modified
covariates approach, we propose a two-stage statistical learning procedure for
estimating HTE with optimal efficiency augmentation, generalizing to arbitrary
interaction model and exploiting powerful extreme gradient boosting trees
(XGBoost). Target estimands for HTE are defined in the scale of mean difference
for quantitative outcomes, or risk ratio for binary outcomes, which are the
minimizers of specialized loss functions. The first stage is to estimate the
main-effect equivalency of the baseline markers on the outcome, which is then
used as an augmentation term in the second stage estimation for HTE. The
proposed two-stage procedure is robust to model mis-specification of main
effects and improves efficiency for estimating HTE through nonparametric
function estimation, e.g., XGBoost. A permutation test is proposed for global
assessment of evidence for HTE. An analysis of a genetic study in Prostate
Cancer Prevention Trial led by the SWOG Cancer Research Network, is conducted
to showcase the properties and the utilities of the two-stage method.