Fig. 3

Phenomapping-derived tools for personalized effect estimates. Phenomaps enable a visual and topological representation of the baseline phenotypic variance of a trial population while accounting for many pre-randomization features. As shown in an analysis of the Canagliflozin Cardiovascular Assessment (CANVAS) trial [138], a phenomap representation of all enrolled patients shows that the study arms are randomly distributed in the phenotypic space (A). Through a series of iterative analyses centered around each patient’s unique phenotypic location, a machine learning model can learn phenotypic signatures associated with distinct responses to canagliflozin versus placebo therapy (B, C). An extreme gradient boosting algorithm trained to describe this heterogeneity in treatment effect in CANVAS successfully stratified the independent CANVAS-R population into high- (D) and low-responders (E). Panels reproduced with permission from Oikonomou et al. [12]