The augmented decision tree-based method presented here is a procedure that uses cross-validated, variance-bias trade-off to develop treatment rules for optimizing health outcomes. This method chooses the most refined level of stratification in order to minimize misclassification rates by incorporating differential impacts for a false positive (FP) versus false negative (FN) errors. The new tree-based estimator is characterized by a tuning parameter ?, which is a loss matrix composed of user-supplied weights (FP; FN). Our optimized CV method directly optimizes the weighted FP to FN ratio while capitalizing on a SuperLearning based cross-validation to limit the risk of overfitting. This yields an estimator that minimizes the cross-validated risk estimates.
Clinical applications of this approach suggest this method has great promise as a statistical tool for precision medicine.