  We propose a prognostic stratum matching framework that addresses the
deficiencies of Randomized trial data subgroup analysis and transforms
ObservAtional Data to be used as if they were randomized, thus paving the road
for precision medicine. Our approach counters the effects of unobserved
confounding in observational data by correcting the estimated probabilities of
the outcome under a treatment through a novel two-step process. These
probabilities are then used to train Optimal Policy Trees (OPTs), which are
decision trees that optimally assign treatments to subgroups of patients based
on their characteristics. This facilitates the creation of clinically intuitive
treatment recommendations. We applied our framework to observational data of
patients with gastrointestinal stromal tumors (GIST) and validated the OPTs in
an external cohort using the sensitivity and specificity metrics. We show that
these recommendations outperformed those of experts in GIST. We further applied
the same framework to randomized clinical trial (RCT) data of patients with
extremity sarcomas. Remarkably, despite the initial trial results suggesting
that all patients should receive treatment, our framework, after addressing
imbalances in patient distribution due to the trial's small sample size,
identified through the OPTs a subset of patients with unique characteristics
who may not require treatment. Again, we successfully validated our
recommendations in an external cohort.
