Yet another paper is out!!! Team effort is super important for companies, but also for causal-ML algorithms for clinical trials :) When a few Causal-ML algorithms are synergised together, the prediction of subgroup in clinical trials is much more robust. Read more about our novel ensemble models and how they can make precision medicine more feasible than ever. Congrats to the incredible PhaseV team! Oshri Machluf Tzviel Frostig Gal Shoham Levin Tomer Milo Elad Berkman
We're thrilled to share our latest research on Conditional Average Treatment Effects (CATE) estimation in clinical trials. CATE estimation provides crucial value of understanding treatment effect heterogeneity, which is inherent to designing patient-centered trials. Our extensive simulation study demonstrates the limitation of traditional methods and introduces novel ensemble methods, which present better results across diverse use cases, varying in complexity, sample size, and underlying mechanisms. These robust methods reveal the factors driving heterogeneity in the presence of multiple covariates and complex biological signals, across therapeutic areas and trial types. Understanding the factors driving the heterogeneity enables us to predict treatment effects and probability of success and extract actionable, clinically significant, and business-meaningful insights to maximize the success of the next trial. Oshri Machluf Raviv Pryluk Elad Berkman Tzviel Frostig Gal Shoham Levin Tomer Milo
Execution machine!
Congrats Raviv Pryluk Elad Berkman!
Great work
Amazing!
Nice! Congrats, Raviv!
Amazing progress!
Amazing! Congrats to the whole team!
A seasoned and results driven CX/CS & Ops Executive and Consultant | MSc. in Business and Organizational Consulting
1moIncredible.