Abstract:*

We propose a prognostic stratum matching framework  that uses optimization in its core 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. We applied our framework to observational data of patients with gastrointestinal stromal tumors (GIST) and validated our approach 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  a subset of patients with unique characteristics who may not require treatment. Again, we successfully validated our recommendations in an external cohort.

Co-authored with Dr. Antonis Margonis and Angelos Koulouras.