{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"In the Interim...","title":"A Discussion on Virtual Controls","html":"","width":"100%","height":180,"duration":2848,"description":"In this episode of \"In the Interim…\", Dr. Scott Berry shares the framework he presented at a CSRC regulatory science hackathon on digital twins and synthetic controls in cardiovascular clinical trials. He separates three kinds of models that are often given the same name: population-level statistical models (a historical control rate), patient-level statistical models (propensity matching, Cox models, hot-deck imputation, and black-box machine learning predictions), and system-level digital twins of human biology. Drawing on ARPA-H's CIRCLE program for ICU immune modulation, the Framingham risk model, the EMA-qualified use of AI-generated prognostic scores as covariates, and Novartis's recent Lp(a) outcomes trial, Scott examines what each type of model can and cannot replace in a trial. Dr. Berry argues that virtual controls should provide a full predictive distribution for each patient rather than a single forecast, and that Bayesian hierarchical models with prospectively enrolled controls allow a trial to verify those predictions as it runs. He closes with how much smaller trials could become, and why clinical trial science must adapt as natural history models improve.\nKey Highlights\nReserving \"digital twin\" for system-level models of biology, not statistical predictions such as the Framingham risk model.\nARPA-H's CIRCLE program: immune-system digital twins to guide ICU therapy, targeting a 25% reduction in ICU stay.\nWhy a validated system-level digital twin would remove the need for a clinical trial.\nPrognostic scores as covariates versus virtual controls, and the counterfactual a control group is meant to supply.\nFrom a single historical control rate to patient-specific predictions built on patient-level data.\nPredictive distributions, not point forecasts, as the basis for comparing treated patients with virtual controls.\nBayesian hierarchical models, MAP priors, and prospective controls to verify and calibrate virtual controls during the trial.\n\nFor more,...","thumbnail_url":"https://img.transistorcdn.com/toFATB0JXvqQXRqzUqrK1EWCJCLOA_Qvjkb5ml3q-s8/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9jM2M0/ZDE2YzA1N2FhNjkx/NDk1NDczNjYzM2E5/NjlmYS5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}