In the Interim...

In this mailbag episode of "In the Interim…", Dr. Scott Berry answers two listener questions and weighs in on the pace of innovation in clinical trial science. Asked what infrastructure a learning healthcare system would require, Scott defines one through the REMAP structure (randomized, embedded, multifactorial, adaptive platform) that Derek Angus named for REMAP-CAP, and uses REMAP Periop at UPMC to argue that embedding randomization and data collection in routine care keeps the cost low while letting a health system learn from tens of thousands of patients a year. On multiplicities, Scott explains why he interprets subgroups, endpoints, and related trials within a Bayesian hierarchical model rather than by adjusting alpha, with the assumed variability across units (tau) setting how far each estimate shrinks toward the overall effect, and he critiques hierarchical testing as non-Bayesian. The episode closes on the contrast between fast-moving innovation, including the AI-accelerated redesign of FACTS, Berry's clinical trial simulation platform, and ARPA-H's SURPASS call for adaptive platform trials, and the conservatism of the EMA's draft reflection paper on platform trials and ICH E20.Key HighlightsREMAP (randomized, embedded, multifactorial, adaptive platform) as a working definition of a learning healthcare system.Embedding randomization and data collection in routine care to remove a large share of trial costs.REMAP Periop and learning from tens of thousands of elective surgeries a year.A Bayesian view of multiplicities: hierarchical models in place of alpha adjustment.Tau, shrinkage, and reading a subgroup result against the overall effect.AI, digital twins, and ARPA-H's SURPASS call for continuous adaptive platform trials.The contrast with the EMA's draft platform trial reflection paper and ICH E20.For more, visit us at https://www.berryconsultants.com/

In this episode of "In the Interim…", Dr. Scott Berry and Dr. Kert Viele, Director of Research at Berry Consultants, review the European Medicines Agency's draft reflection paper on methodological aspects in confirmatory platform trials. They walk through the paper's definition of a platform trial and explain why the shared control arm is the source of both the efficiency and the complexity of these designs. Scott and Kert discuss the paper's preference for constant allocation ratios between each arm and control, its position that response adaptive randomization is not supported, two-stage randomization with multiple placebos, and the strong preference given to concurrent controls. They make the case that non-concurrent controls, adjusted for time, are high-quality controls, and welcome the paper's view that they are preferable to external controls. The episode closes with the risk of multiple false positive decisions from a common control, disjunctive and conjunctive power, and when multiplicity adjustment is warranted, with overall praise for a clear and well-constructed paper.Key HighlightsThe shared control arm as the source of both efficiency and complexity in platform trials.Constant allocation ratios, and the paper's hard no on response adaptive randomization.Two-stage randomization, multiple placebos, and testing whether placebo groups can be pooled.A strong preference for concurrent controls, and the case for time-adjusted non-concurrent controls.Internal non-concurrent controls preferred over external controls.Multiple false positive decisions, and marginal, disjunctive, and conjunctive power.When multiplicity adjustment applies across arms, doses, and related families of hypotheses.For more, visit us at https://www.berryconsultants.com/

In this episode of "In the Interim…", Scott Berry is joined by Jenny Devenport and Kaspar Rufibach of Merck Group, co-authors of the EFSPI Statistical Methodology Leaders paper "Statistical Methodology Groups in the Pharmaceutical Industry," and May Mo of Amgen, a co-author of the commentary on the paper from the Statistical Innovation Community, an ASA Biopharmaceutical working group. The guests describe methodology groups within a pharmaceutical company as a strategic investment, and innovation as the product of invention and commercialization, where publication, regulatory dialogue, and validated software ("statistical engineering") turn new methods into common practice. They discuss why new mechanisms of action and therapies such as CAR-T need more than a t-test or a log-rank test, and how estimands, causal inference, adaptive designs, and external data borrowing are reaching clinical trial statistics. The conversation addresses the concern that a dedicated group divides methodologists from project statisticians, how these groups demonstrate their value, why quantitative decision-making is a core competence of a pharmaceutical company, and why curiosity and collaboration matter as much as technical skill.Key HighlightsInnovation as the product of invention and commercialization, and the role of methodology groups in scaling new methods.New mechanisms of action, from delayed immune activation to CAR-T, that a t-test or log-rank test cannot handle.Estimands, causal inference, adaptive designs, and external data borrowing entering clinical trial statistics.Avoiding a divide between methodologists and project statisticians, and why there is "no unicorn statistician."Showing value to leadership, from study involvement and due diligences to gains in probability of success.Quantitative decision-making: presenting design options with their risks, costs, and trade-offs.Curiosity, collaboration, and communication as the traits of an influential methodologist.For more, visit us at https://www.berryconsultants.com/

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

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