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.

Key Highlights

• Reserving "digital twin" for system-level models of biology, not statistical predictions such as the Framingham risk model.
• ARPA-H's CIRCLE program: immune-system digital twins to guide ICU therapy, targeting a 25% reduction in ICU stay.
• Why a validated system-level digital twin would remove the need for a clinical trial.
• Prognostic scores as covariates versus virtual controls, and the counterfactual a control group is meant to supply.
• From a single historical control rate to patient-specific predictions built on patient-level data.
• Predictive distributions, not point forecasts, as the basis for comparing treated patients with virtual controls.
• Bayesian hierarchical models, MAP priors, and prospective controls to verify and calibrate virtual controls during the trial.

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In this episode of "In the Interim…", Dr. Scott Berry recounts a presentation he gave to the Speak Foundation, a patient organization focused on limb-girdle muscular dystrophy and makes the case for master protocols directly to the stakeholder trials exist to serve. Opening with the deliberately absurd premise of a sports league that constructs a four-billion-dollar stadium for every single game and demolishes it at the final whistle, Scott reframes the conventional one-off trial as a two-year construction project dismantled the moment the contest ends. The episode distinguishes platform trials and basket trials as standing "arenas," explains the modular role of the master protocol described in Woodcock and LaVange's 2017 New England Journal of Medicine paper, and draws on worked examples including I-SPY 2, the HEALEY ALS Platform Trial, GBM AGILE, Precision Promise, and the ROAR basket trial. Scott takes each stakeholder in turn from the treatment arm sponsor, the site, the regulator, and the patient. Scott lays out how three-to-one randomization lets an arm fund 133 patients while reading out inferences with more than 200, how 667 patients in a single platform can do the work of 1,000 across five separate trials, and why far fewer patients are assigned to placebo. He closes on the genuine bottleneck: the first arena is the heavy lift, and in rare disease it is frequently patients themselves who make it happen.

Key Highlights

• The one-game stadium analogy of two years to build a trial, one contest, then demolition.
• Platform trials and basket trials as standing arenas for multiple arms and multiple subtypes.
• Master protocols as modular documents, with arms plugging in and out as appendices.
• Worked examples from I-SPY 2, HEALEY ALS, GBM AGILE, Precision Promise, and the ROAR basket trial.
• A stakeholder-by-stakeholder view: arm sponsor, site, regulator, and patient.
• 667 patients in one platform versus 1,000 across five separate trials, with far fewer on placebo.
• Bayesian borrowing across 39 limb-girdle subtypes, and the rare-disease shots never otherwise taken.

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In this episode of "In the Interim…", Dr. Scott Berry talks with Dr. Alun Bedding, a professional certified coach and statistician who spent decades in pharmaceutical statistics, including as global head of statistical methods, collaboration, and outreach at Roche, before founding his own coaching and consulting practice. The conversation traces Alun's path from statistician to coach and unpacks what coaching is: a forward-facing partnership built on exploring a client's own thinking, not on advice, mentoring, or therapy. Alun and Scott draw a direct line between good coaching and good statistical consulting as both begin by questioning the stated goal rather than accepting the first request at face value, whether that request is "I want to be a better manager" or "I need a power calculation." The episode covers why coaching must be uncomfortable to produce real change, why self-coaching runs into the limits of blind spots and self-bias and where AI coaching tools genuinely help and where they fall short.

Key Highlights

• Coaching is a partnership exploring a client's own thinking, not advice or mentoring.
• The parallel between coaching and statistical consulting: determining the real goal behind a request.
• Why effective coaching must feel uncomfortable to produce genuine progress.
• The limits of self-coaching, and why blind spots require an outside challenger.
• How sports and performance coaching shifted from instruction to inquiry.
• Where AI coaching helps and where human presence and challenge remain irreplaceable.
• The difference between coaching and therapy: forward-facing versus backward-facing.

For more, visit us at https://www.berryconsultants.com/