In the Interim...

In the 60th episode of “In the Interim…”, Dr. Scott Berry, Dr. Nick Berry, and Dr. Joe Marion discuss how Berry Consultants uses AI in clinical trial design and software development. The conversation addresses current applications, limitations, implications for productivity, and the ongoing need for human expertise in clinical trial design. The team examines both promising use cases and the risks associated with security, compliance, and AI-generated statistical work.Key HighlightsAI is used to develop user interfaces and code modules, notably expediting tasks like R Shiny app development and software prototyping.Statistical coding for complex modeling and simulation—such as numerical integration and predictive probability calculations—remains unreliable when delegated to AI and still requires direct oversight and manual review.Attention to security and confidentiality is central; Berry prohibits the use of client-sensitive or patient data within AI tools.Generative AI assists with drafting and editing documents, but the output tends to be non-specific, generic, and sometimes imprecise, requiring expert editorial input before use.While embracing AI to improve efficiency, the discussion is critical of current AI hype, especially around black-box modeling and pushes back against the perception that current AI can replace domain-specific statistical design or strategic judgment.For more, visit us at https://www.berryconsultants.com/

In this episode of "In the Interim…", host Dr. Scott Berry undertakes a detailed, methodical critique of ICH-E20 draft guidance language as applied to adaptive clinical trial design. Focusing on an innocuous but corruptible paragraph in Section 3.1, Scott scrutinizes the logic behind regulatory reluctance to appreciate multiple or complex adaptations in confirmatory trials. Drawing on extensive experience, he highlights how such restrictive interpretations do not reflect practical development realities, instead setting up “false choices” where alternative designs desired by regulators are infeasible. Through operational scenarios—including the SEPSIS-ACT trial, an enrichment design, and sample size re-estimation examples—Scott illustrates the empirical benefits of seamless and multi-adaptive trials for sponsors, patients, and regulators. Technical discussion addresses misconceptions about complexity and bias and stresses the value of presenting realistic alternatives when engaging with regulatory authorities. The episode ultimately encourages a more nuanced dialogue to advance efficient and scientifically robust clinical trials.Key HighlightsDiscussion of ICH-E20 section 3.1 guidance and its operational impact on adaptive designs.Dissection of “false choice” dilemmas in regulatory interactions, referencing real adaptive trial submissions.Case-based examples: SEPSIS-ACT, enrichment, and sample size adaptation trials.Highlighting myths regarding bias and operational burden from multiple interim analyses.Emphasis on practical strategies for more effective regulatory communication about adaptive trials and realistic alternatives.For more, visit us at https://www.berryconsultants.com/

In this episode of "In the Interim…", Dr. Scott Berry connects with Dr. Byron Gajewski, professor of biostatistics and data science at the University of Kansas Medical Center (KUMC), for a detailed discussion on the design, simulation, and operational realities of Bayesian adaptive clinical trials in academic environments. Gajewski discusses his academic background, training at Texas A&M, and progressive adoption of Bayesian principles based on direct experiential advantages in complex data settings. The conversation highlights KUMC’s Fixed and Adaptive Clinical Trial Simulator Working Group, which utilizes FACTS for faculty, staff, and student collaboration, enabling practical simulation, trial protocol development, and in-house applied statistical training. The PAIN-CONTRoLS Trial serves as a practical example of multi-arm Bayesian adaptive design, using response-adaptive randomization for comparative effectiveness in neuropathy research. The NIH-funded HOBIT trial is examined in detail: multi-arm structure, adaptive allocation among investigational arms, fixed control randomization, group-sequential interim analyses, and sliding dichotomy methodology for the Glasgow Outcome Scale Extended. The discussion stresses a shift to probabilistic, evidence-driven interpretation and reporting, shaping operational choices and academic culture for both investigators and trainees.Key HighlightsGajewski describes how practical challenges in real-world problems catalyzed his transition to Bayesian modeling.KUMC’s working group integrates FACTS software in collaborative simulation and operational trial planning.The PAIN-CONTRoLS Trial: multi-arm Bayesian adaptive design, response-adaptive randomization, real-time analysis, and endpoint-driven allocation.HOBIT trial: Adaptive allocation, fixed control arm proportion, group-sequential interims, ordinal endpoint modeling.Emphasis on probabilistic, quantitative reporting over binary outcomes in trial analysis and interpretation.Cultural shift observed among academic collaborators and trainees embracing Bayesian adaptive strategies.

In this episode of "In the Interim…", Dr. Scott Berry is joined by statisticians Dr. Amy Crawford, Dr. Cora Allen-Savietta, and Dr. Jessica Overbey for a technical deep dive into hierarchical composite endpoints and the win ratio in clinical trial design. The group addresses clinical and statistical justifications for layered endpoint structures, demonstrates the mechanics of pairwise win ratio analysis, and explores operational and interpretive consequences in both conventional and adaptive trials. The panel scrutinizes analytic limitations, regulatory concerns, and emerging modeling strategies—all grounded in real-world trial examples.Key HighlightsPrecise definition and use case for hierarchical composite endpoints in cardiovascular and related trials.Stepwise breakdown of win ratio mechanics, tie-handling, and the distinction between effect estimation (win ratio) and hypothesis testing (FS-test).Discussion of endpoint prevalence and dominance, risk of clinical interpretation being tied to lower-order outcomes, the role of patient exposure, and methods to parse component contributions.Overview of statistical power, role of simulation, and comparative advantages over other composite approaches.Identification of core limitations: interpretive complexity, opaque weighting, and mutable meaning of wins with maturing data.Review of predictive probability for adaptive interim analysis and modeling using ordinal regression.Opinions of US and European regulatory perspectives including support, reservations, and expectations for transparency with graphics and complementary analyses.For more, visit us at https://www.berryconsultants.com/

In this episode of "In the Interim…", Dr. Scott Berry and Dr. Kert Viele examine Bayesian borrowing in Phase 3 clinical trials, focusing on statistical handling of prior information and real-world FDA interactions. The episode opens with an analogy, comparing prior probability in Bayesian analysis to interpreting a home pregnancy test, succinctly demonstrating the effect of prior knowledge on trial interpretation. The discussion addresses technical challenges—how borrowing inflates Type I errors and why this is addressed differently under Bayesian operating characteristics. Concrete examples include dynamic versus static borrowing approaches, and formal integration of prior evidence in regulatory submissions. Case studies center on the WATCHMAN device (PROTECT AF and PREVAIL trials) and REBYOTA, illustrating FDA engagement, relevant trial design tactics, and published outcomes. The episode also critiques common pitfalls such as selective data use and improper prior construction, emphasizing the FDA’s focus on comprehensive and unbiased historical sources.Key HighlightsPregnancy test analogy used to clarify prior probability in trial interpretation.Bayesian borrowing’s effects on Type I error and statistical thresholds.Case studies: WATCHMAN device (PROTECT AF, PREVAIL) and REBYOTA approvals.Dynamic borrowing versus static borrowing strategies in regulatory settings.Risks of cherry-picking and importance of unbiased, relevant prior data.FDA guidance and review procedures for Bayesian trials.For more, visit us at https://www.berryconsultants.com/

In this episode of "In the Interim…", Dr. Scott Berry and Dr. Lindsay Berry investigate the statistical foundations and clinical implications of analyzing ordinal endpoints, drawing on experience from major stroke and COVID-19 trials. Discussion centers on the Modified Rankin Scale, DAWN, MR CLEAN, and REMAP-CAP, demonstrating that methods such as proportional odds, dichotomization, and utility weighting all impose explicit or implicit clinical weights on the outcome categories. The episode presents direct mathematical derivations, exposes the equivalence between proportional odds models and value-weighted analysis, and uses real trial data to explore how statistical and clinical perspectives on endpoint weighting may diverge. Emphasis remains on transparency and the need for clinically relevant weight assignment in trial endpoints.Key HighlightsStructural overview and clinical significance of the Modified Rankin Scale scores.Illustration that proportional odds models and dichotomized analyses apply hidden, prevalence-driven or threshold-based weights.Utility weighting in DAWN, formulated from EQ-5D patient utilities and economic studies, with observed alignment.MR CLEAN investigators' critique of utility weighting; empirical data demonstrated relative consistency and challenged the claim that statistical approaches resolve variation across patients.REMAP-CAP platform trial: Organ Support Free Days endpoint analyzed with proportional odds imposed weights on the scale from death to free of organ support .Extension of these arguments to win ratio/rank-based approaches, with caution that all methods encode clinical assumptions.For more, visit us at https://www.berryconsultants.com/

Dr. Nathan O’Hara (University of Maryland), Dr. Gerard Slobogean (UC Irvine), and Dr. Sheila Sprague (McMaster University) describe the launch and design of the Musculoskeletal Adaptive Platform Trial (MAPT)—the first major adaptive platform trial in orthopaedic surgery. The discussion covers MAPT’s master protocol structure, patient-centered endpoint framework, and operational strategies for multinational implementation. Focus areas include the FASTER-HIP domain’s use of Bayesian modeling with a hierarchical clinical endpoint and the standards established for adaptation, data coordination, and future scalability. Listeners gain insight into a trial infrastructure designed to lower barriers for evidence generation and facilitate ongoing evidence generation in musculoskeletal trauma care.Key HighlightsMAPT as a scalable, master protocol for orthopaedic intervention evaluationHierarchical, patient-centered endpoint (survival, 4-level ambulation, days alive/out of hospital), analyzed with a Bayesian-modeled, non-parametric win ratioDomain-specific adaptation thresholds based on clinical differentiationInterim analyses after 100 patients, then every 50, informing early adaptation40 sites across US, Canada, and Europe, centralized data management at McMasterA unified DSMB structure with capacity for domain-specific expertise as neededTiered protocol access: open sharing, collaboration, direct integrationInfrastructure enables rapid domain addition and multi-investigator participationFor more, visit us at https://www.berryconsultants.com/

In this episode of "In the Interim…", Dr. Scott Berry and Dr. Kert Viele deliver a quick reaction to the FDA’s draft guidance on Bayesian statistics for clinical trials of drugs and biologics. Their assessment addresses the structure, content, and impact of the document, emphasizing evidence-based requirements and guidance scope. The episode breaks down regulatory language, technical expectations, and workflow implications for clinical trial sponsors and statisticians.Key HighlightsClear distinction between trials justified by type 1 error control and trials justified by agreement on Bayesian priors and decision rule.Explanation of how informative priors can be created based on external or historical data.Technical explanation of dynamic discounting/borrowing, especially in Bayesian hierarchical models for rare populations, pediatric-adult extrapolation, related disease subgroups, and platform and basket trials (e.g., ROAR).In-depth look at the necessity of sensitivity and robustness checks for different priors, and the FDA’s design prior and analysis prior terminology.FDA’s requirements for accepting external data sources: data provenance, patient-level comparability, recency, and appropriate covariate adjustments.Comparison with ICH E20 on adaptive designs, providing context for ongoing regulatory harmonization and possible influence on international regulatory directions.Direct warning against attempts to misuse Bayesian methodology as a substitute for scientific rigor; legitimate uses must meet FDA standards and not simply serve to lower evidentiary bars.Resource: FDA News Release: https://www.fda.gov/news-events/press-announcements/fda-issues-guidance-modernizing-statistical-methods-clinical-trialsFor more, visit us at https://www.berryconsultants.com/

In this episode of “In the Interim…,” host Dr. Scott Berry examines the challenge of communicating complex statistical concepts to non-statistical audiences. Drawing from firsthand experiences in agriculture, professional golf, and clinical development, as well as examples involving historical and scientific figures, Scott reflects on why technical rigor alone often fails to influence. The discussion focuses on the consequences of mismatched language, the importance of empathy, and the utility of simulation when bridging the gap between analysis and stakeholder understanding.Key HighlightsIllustrated barriers to statistical communication using stories from farming, golf, and early career encounters.Examples involving John Glenn, Ada Lovelace, and Charles Babbage show how communication, not just science, determines impact.Insights from Alan Alda on empathy as a foundational tool for scientists presenting technical ideas.Clinical trial simulations revealed knowledge gaps—such as misunderstanding of power—when communicating with decision-makers.Emphasizes the necessity of translating analytic outputs into operational, financial, or clinical language for meaningful impact.For more, visit us at https://www.berryconsultants.com/

In this episode of "In the Interim…", Dr. Jenny Devenport, Global Head of Methods, Collaboration, and Outreach at Roche, joins Dr. Scott Berry for a detailed discussion on career evolution, statistical culture, and communication in the pharmaceutical industry. Dr. Devenport describes her transition from psychology in New Mexico to statistical leadership in Basel, emphasizing the formative role of early academic mentors and her experience working across the US and Europe. She outlines her current functions in methods development, internal collaboration, and industry outreach, highlighting active engagement with academic and regulatory communities. The episode scrutinizes differences in workplace culture, such as the emphasis on debate and long-term collaboration in Europe, and differences in educational backgrounds among statisticians. The conversation covers practical barriers and slow adoption of Bayesian methods and the importance of communication in the acceptance of futility analyses in pharma, the importance of scale in problem-solving, and the emergence of AI as a tool for statisticians. Dr. Devenport provides pragmatic strategies for statisticians to improve their influence through tailored, audience-specific communication.Key HighlightsDr. Devenport’s academic and geographic move from the US to EuropeResponsibilities in methods development, collaboration, and outreach at RocheContrasts in US and European pharmaceutical statistics culturesMeasured perspective on AI’s effect on statisticians’ responsibilitiesPractical guidance for statisticians on communication and influence

In episode 40 of "In the Interim…", Dr. Scott Berry examines the statistical, operational, and behavioral challenges of using interim analyses as triggers for funding in adaptive and seamless Phase II/III clinical trials. The episode presents a typical hypothetical scenario for rare disease drug development, contrasting conventional two-stage development with a seamless design and highlighting efficiency gains in sample size, patient allocation, and trial duration. Scott details the construction of administrative (financial) interim analyses, underscoring their distinction from futility analyses and their role in funding decisions when complete funding is not secured upfront. He addresses FDA operational bias concerns, emphasizing blinding and limiting information sharing to protect trial integrity. Finally, the episode focuses on developing objective interim funding criteria—using Bayesian predictive probability and assurance—and on leveraging illustrative simulation outputs and sample datasets to bridge the “I’ll know it when I see it” divide between scientists and funders. Practical, empirical, and tailored to real funding barriers in clinical research.Key HighlightsStatistical structure and efficiency of seamless Phase II/III trial designsAdministrative (financial) interim analysis setup as funding decision triggers, distinct from futility analysesFDA operational bias guidance and requirements for trial blindingPredictive probability and assurance as objective interim criteriaSample data and simulation outputs to facilitate stakeholder alignmentFor more, visit us at https://www.berryconsultants.com/

In this episode of "In the Interim…" guest host Cooper Berry moderates a detailed discussion on the evolution and practice of Bayesian methodology in clinical trials with fellow family members Dr. Don Berry, Dr. Scott Berry, Dr. Lindsay Berry, and Dr. Nick Berry. The panel outlines the foundational principles of Bayesian decision-making in medical research, ethical debates informed by historical reports like the Belmont Report, and the shift in regulatory acceptance. Computational developments such as Markov Chain Monte Carlo (MCMC) are examined for their role in enabling applied Bayesian models. Panelists give practical accounts of implementing adaptive and platform trials, including I-SPY 2 and REMAP-CAP, and analyze challenges faced during the COVID-19 pandemic. The implications of Bayesian statistics in artificial intelligence and contemporary clinical decision-making are explored, highlighting ongoing shifts in trial design and evidence synthesis. Each discussion is grounded in direct experience and technical rigor, providing insight into both the operational realities and future trajectory of Bayesian-driven methods in clinical research.Key Highlights:Historical development of Bayesian clinical trial design and foundational influence from Leonard J. Savage to current methodsEthical tension in trial conduct, referencing the Belmont Report and equipoiseAdvances in computation and Markov Chain Monte Carlo (MCMC)Regulatory frameworks for Bayesian adaptive trials, including FDA guidanceImplementation details from I-SPY 2 and REMAP-CAP platform trialsBayesian methodology in the context of artificial intelligence, precision medicine, and future data integrationFor more, visit us at https://www.berryconsultants.com/

In Episode 36 of "In the Interim…", Dr. Scott Berry and Dr. Don Berry analyze the Phase II trial of Lecanemab (BAN2401) in Alzheimer’s disease, focusing on the application of adaptive Bayesian methods following persistent failures in Alzheimer’s drug development. The conversation covers the specific design features of five active arms, response adaptive randomization, and a longitudinal Bayesian model driving interim decisions, as well as direct operational and statistical challenges encountered during the trial. The hosts address regulatory proceedings, critique from "experts" regarding adaptive methods on noisy cognitive endpoints, and the direct alignment of the trial’s Bayesian 18-month efficacy estimates with the subsequent Phase III results and regulatory approvals.Key HighlightsAlzheimer’s drug development context: Widespread Phase III failures prompted a retreat from conventional trial designs and a demand for greater rigor and adaptability.Lecanemab Phase II methodology: Five active arms, two dosing schedules, response adaptive randomization, and adaptive interim analyses at every 50 patients enabled real-time adjustment and efficient dose evaluation.Bayesian modeling and imputation: Use of a longitudinal model to address missing data, forecast 12- and 18-month outcomes, and inform both allocation and stopping criteria.Operational adaptations: The design accommodated unplanned safety restrictions, such as stratified randomization for APOE4-positive participants after ARIA signals.Expert skepticism: Addressed Paul Aisen’s concerns about adapting to noisy interim cognitive data, emphasizing safeguards against erroneous stopping or success.Regulatory outcome: The 18-month efficacy estimates from Bayesian modeling during Phase II matched Phase III findings; FDA granted accelerated approval based on amyloid reduction and later full approval after Phase III confirmation.For more, visit us at https://www.berryconsultants.com/