Comparison of model performance with different targets and sets of features, using Random Forests.

Overlapping features are the 100 features in both Canadian Biomarker Integration Network in Depression’s CAN-BIND-1’s trial and Sequenced Treatment Alternatives to Relieve Depression (STAR*D), while Full uses all 480 features from STAR*D. Clustering-χ2 Selection (30 features) and Elastic Net Selection (31 features) refer to using these feature selection techniques as defined in Methods. Targets include antidepressant response, remission, or treatment-resistant depression (TRD), as defined in Methods. Models trained and evaluated using cross-validation (CV) on STAR*D, and we also report again the results of externally validating models on the CAN-BIND-1 dataset after being trained on STAR*D. We report balanced accuracy and area-under-curve (AUC). Additional performance metrics and statistics are documented in S10 and S11 Tables.