In the Interim...

• A Discussion with Michael Proschan on Response-Adaptive Randomization

  In this episode of "In the Interim…", Dr. Scott Berry and NIH’s Dr. Michael Proschan conduct a detailed discussion from opposing viewpoints on response-adaptive randomization (RAR) in clinical trials. The discussion focuses on where they agree – on the positives and negatives of RAR, and where they disagree on its scientific use. Key HighlightsPotential issues of using RAR: Potential temporal trends, unblinding, reduction in statistical efficiency in 2-arm trialsPotential benefits include improved statistical efficiency in multi-arm trials  depending on the goals (e.g. dose-finding trials).Potential unblinding of results in non-blinded trials and the need for operational excellence.Ethical and Bayesian perspectives are considered, but emphasis remains empirical.For more visit: https://www.berryconsultants.com/

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• STEP Statistical Modeling

  In this episode of "In the Interim…", Dr. Scott Berry, Dr. Elizabeth Lorenzi, and Dr. Amy Crawford discuss the STEP platform trial’s statistical methodology for evaluating which acute stroke patients benefit and which do not from endovascular therapy (EVT). The discussion critiques the inadequacy of traditional clinical trials powered for a single population to show benefit, as the goal of the trial is to identify who benefits, not if the entire population has a net benefit. The team walks through the development and simulation of a Bayesian change point model, addressing heterogeneous treatment responses across the NIH Stroke Scale. The adaptive platform design leverages scheduled interim analyses to draw timely, data-driven conclusions about patient subgroups, improving trial efficiency and relevance. The episode also previews scaling to two-dimensional modeling, incorporating both stroke severity and time since last known well, and emphasizes ongoing clinical trial simulation and close integration between clinicians and statisticians throughout trial design and execution.Key HighlightsSTEP platform master protocol and the NIH StrokeNet collaborative infrastructureClinical rationale for Bayesian change point modeling of the effect of EVT across the patientsShift from single to dual change point models to reflect regions of equivalenceDevelopment of custom C code and MCMC samplers due to limits of standard toolsInterim analyses direct adaptive enrollment and define actionable conclusionsFuture extensions to multidimensional change point curves modeling 

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• Bayesian Approach in Clinical Trials

  This episode of "In the Interim…" features Dr. Scott Berry, Dr. Kert Viele, and Dr. Melanie Quintana of Berry Consultants dissecting the technical and operational landscape of Bayesian statistics in clinical trial design. The episode discussed what is Bayesian statistics, the impact of informative and non-informative priors, and clarifies when and why Bayesian approaches surpass frequentist analyses—especially in adaptive, platform, and rare disease trial settings. The discussion directly challenges the misconception that Bayesian methods “lower the bar," presenting evidence that they often require broader data synthesis and can raise evidentiary standards.Key regulatory developments at FDA and EMA are reviewed, with attention to updated guidance and increased adoption. Case studies illustrate Bayesian methods in practice, including the prospectively combined phase 2 and 3 analysis for REBYOTA approval; hierarchical modeling in GNE myopathy; shared controls and endpoint integration in the HEALEY ALS Platform Trial; and robust subgroup borrowing in the ROAR basket trial. The team also addresses technical challenges such as multiplicity, subgroup analysis, complexity in endpoint modeling, and appropriate strategies for blending Bayesian and frequentist approaches for maximum regulatory and scientific clarity.Key HighlightsClear explanation and real-world examples of Bayesian analysis in clinical trials.Theoretical and practical distinctions from frequentist methodsPractical breakdown of control sharing, endpoint integration, and subgroup borrowing.Regulatory position and the increasing acceptance of Bayesian trial designs and analyses.Case examples: REBYOTA, GNE myopathy, HEALY ALS Platform Trial, ROAR basket trial.

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• The Time Machine

  Dr. Scott Berry and Dr. Kert Viele discuss the origins and implementation of the “time machine” modeling approach, beginning with sports analytics and progressing to adaptive platform clinical trials. The episode focuses on how techniques for comparing athletes across eras translate into methodology for platform trials. Key HighlightsSports analytics as foundation: Early work of modelling athlete comparisons across eras using bridging methodologies.Platform trial application: The time machine model in I-SPY 2 enabled efficient control allocation through overlapping arms over extended trial periods.Core modeling principles: Additive treatment effect assumptions and the necessity of sufficient temporal overlap for reliable era comparisons.Statistical implementation: Approaches include categorical era adjustment and Bayesian smoothing splines for modeling change over time.Limitations and disease specificity: In conditions with rapid clinical or epidemiologic change, such as COVID-19, non-concurrent controls are avoided due to high risk of era by treatment interaction.Regulatory and methodological distinction: The model leverages within-trial overlapping data collected under a unified protocol, contrasting sharply with external or historical controls.

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• The Legend of I-SPY 2 - Part B

  In this episode, Dr. Don Berry and Dr. Scott Berry provide an in-depth account of I-SPY 2, focusing on the trial’s use of the “time machine” methodology—a Bayesian solution allowing bridging across arms to inform ongoing analyses. The discussion details how predictive probabilities and adaptive randomization shaped pivotal decisions, including the handling of Pertuzumab’s approval and Neratinib’s subtype-specific performance. This episode also documents the technical and operational contributions of Laura Esserman, Anna Barker, Janet Woodcock, Meredith Buxton, and Ashish Sanil, clarifying the roles that enabled the platform’s success and broader impact on subsequent adaptive trials.Key HighlightsIntroduction of the “time machine” concept, enabling valid comparison between experimental and control arms even when enrollment periods differ—a pragmatic solution originally utilized in sports examples for evolving platform trials as treatments and control arms change.Ongoing trial conduct driven by a Bayesian adaptive algorithm, developed and maintained by Berry Consultants statisticians, which computes predictive probabilities to guide arm graduation, futility, and real-time adjustment of randomization probabilities.Neratinib serves as a case study in subtype-specific adaptive randomization: the platform set randomization probability to zero in subtypes without signal, while effective subtypes increased randomization and advanced to graduation.I-SPY 2’s methodologies shaped subsequent adaptive platform trials (GBM AGILE, Precision Promise, COVID-19 ACTIV networks), with regulatory acceptance reflected in FDA guidance and Janet Woodcock’s public recognition of adaptive randomization as “adequate and well controlled” for registration studies.Specific recognition: Laura Esserman (trial leadership), Anna Barker (funding and strategic input), Janet Woodcock (FDA guidance and adaptive methods support), Meredith Buxton (logistics; GCAR leadership), and Ashish Sanil (Berry Consultants; ongoing algorithm implementation).

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