Free Daily Podcast Summary
In the Interim...: Daily Summaries Delivered
by Berry
A podcast on statistical science and clinical trials. Explore the intricacies of Bayesian statistics and adaptive clinical trials. Uncover methods that push beyond conventional paradigms, ushering in data-driven insights that enhance trial outcomes while ensuring safety and efficacy. Join us as we dive into complex medical challenges and regulatory landscapes, offering innovative solutions tailored for pharma pioneers. Featuring expertise from industry leaders, each episode is crafted to provide clarity, foster debate, and challenge mainstream perspectives, ensuring you remain at the forefront of clinical trial excellence.
Latest Episodes
The most recent episodes — sign up to get AI-powered summaries of each one.
- Yesterday47 min
Response-Adaptive Randomization in Clinical Trials
In this episode of "In the Interim…", Dr. Scott Berry and Dr. Kert Viele examine response-adaptive randomization (RAR) in clinical trials, dissecting its statistical rationale, common criticisms, and implementation challenges. Drawing on extensive experience with trials such as BAN2401 (lecanemab), ICECAP, dulaglutide seamless Phase 2/3, I-SPY2, REMAP-CAP, PROSPECT, and the historical ECMO trial, they discuss the scientific advantages and disadvantages and ethical impact. RAR reallocates patient assignments during interim analyses to direct more patients to better-performing arms, but this can reduce power in two-arm trials, introduce complexity from temporal trends, and create operational complexity. The ECMO trial and "play-the-winner" approaches are discussed as cautionary examples emphasizing the need for thorough simulation before deployment. The hosts highlight RAR’s strengths for dose-finding, multi-arm, and some platform designs, but underscore its limitations in confirmatory two-arm settings. Operational demands, data reliability, simulation across scenarios, and resistance to overgeneralization are recurrent themes. The episode concludes by situating RAR within the broader context of adaptive platform trials and learning healthcare systems.Key HighlightsDefinition and mechanics of RAR, with interim analysis guiding allocation updatesMulti-arm adaptive and platform trial experiences (BAN2401, ICECAP, dulaglutide, I-SPY2, REMAP-CAP, PROSPECT)Critique of RAR in two-arm trials (power loss), temporal trends, unblinding, and overgeneralized literatureECMO/play-the-winner: risks of poorly simulated RARNecessity for rigorous pre-trial simulation and robust data flowsContextualization of RAR’s role in both traditional and learning healthcare environmentsFor more, visit us at https://www.berryconsultants.com/
- 1 weeks ago53 min
REMAP-CAP: The Origin
In this episode of "In the Interim…", Dr. Scott Berry explores the origins of REMAP-CAP with Prof. Steve Webb, former chair of the REMAP-CAP International Trial Steering Committee. This episode examines how pandemic preparedness efforts after 2009 H1N1 shaped the design of an international, adaptive platform trial to be able to respond rapidly to new infectious threats. Steve and Scott explain the sequence of strategy meetings, the role of the PREPARE consortium in securing EU funding and subsequent federation across Australia and Canada. The discussion details REMAP-CAP’s technical foundations: a modular master protocol, domain architecture, Bayesian adaptive methods, and frequent interim analyses. When COVID-19 emerged, these core elements permitted immediate platform activation to combat the pandemic infection with assessment of treatments across multiple domains—including steroids, immune modulation, and anticoagulation—generating actionable evidence in weeks. The episode also addresses international data harmonization, multi-platform trial collaboration, and the capacity to adapt trial structure as infectious disease threats evolve.Key HighlightsResponse to H1N1 and feckless pandemic trialsInternational strategy meetings—origins of platform conceptPREPARE consortium and cross-continental fundingModular master protocol, factorial allocation, and domain-specific appendicesBayesian triggers and response adaptive randomizationPivot to COVID-19 and rapid data generationMulti-platform international collaborationFor more, visit us at https://www.berryconsultants.com/
- 2 weeks ago56 min
Fighting Time in Adaptive Trials
In this episode of "In the Interim…", Dr. Scott Berry explores the challenge of protracted endpoint timelines in adaptive clinical trials and the statistical strategies used to increase the rate of actionable information gain. Drawing on detailed case studies from breast cancer (I-SPY 2), Alzheimer’s disease (BAN 2401), diabetes (AWARD-5/Trulicity), and cardiac arrest, Scott addresses the technical demands of longitudinal modeling and interim data imputation for accelerating learning. The discussion prioritizes a critical, empirical perspective of demonstrating how carefully constructed statistical models, simulation, and Bayesian methods can convert interim patient data into more robust estimates of delayed outcomes and support key design adaptations. The episode is a direct account of the methods, uncertainties, and real-world impact of fighting time in adaptive trials.Key HighlightsAnalyzes how delayed primary endpoints challenge adaptive trial efficiency, and how adaptive trial designs use accumulating in-trial data to inform adaptive allocation, arm graduation, and early trial conclusions.Dissects the use of longitudinal models in I-SPY 2, in which interim MRI measurements at one and three months are mapped to predicted six-month pathologic complete response, through an ordinal stratified, pre-specified modeling approach—illustrating both the strengths and limits of interim forecasting.Reviews the BAN 2401 adaptive Alzheimer’s trial, where early cognitive assessments were modeled to forecast 12-month outcomes enabling response adaptive randomization and sample size adaptation based on projections from interim data.Details the AWARD-5 seamless trial for dulaglutide (Trulicity), where strategic enrollment pacing, predictive modeling of early HbA1c and weight loss, and a utility function across four endpoints supported both dose selection and seamless transition to phase 3 without requiring full cohort maturation.Summarizes recent cardiac arrest trial (ICECAP), using 30-day ordinal scales and multiple imputation to predict 90-day outcomes and improve interim decision-making.Unpacks the importance of prior-data-driven modeling, simulation, and strict robustness checks in the construction of all predictive models used for interim adaptation.For more, visit us at https://www.berryconsultants.com/
- 3 weeks ago51 min
ICECAP: The Adaptive Design
In this episode of "In the Interim…", Dr. Scott Berry is joined by Dr. Will Meurer, professor of Emergency Medicine and Neurology at the University of Michigan, for an in-depth discussion of the ICECAP trial’s adaptive Bayesian design. The discussion breaks down the scientific rationale for hypothermia after cardiac arrest, critiques legacy studies, and explores the justification for including both shockable and non-shockable rhythm types. The episode provides a detailed account of ICECAP’s methodological strategies: a weighted mRS primary endpoint, Bayesian adaptive trial structure, response-adaptive randomization (governed by strict allocation guardrails), a unique Bayesian model for duration-response, and futility rules. The trial’s development is described in the context of the ADAPT-IT initiative, an FDA/NIH partnership, and the operational leadership of the MUSC Data Coordinating Center. Results are pending publication which will be highlighted in a future episode of “In the interim…”.Key HighlightsRationale for exploring duration of hypothermia after cardiac arrest with review of prior evidence.Enrollment of shockable and non-shockable populations to address clinical uncertainty.Primary endpoint: weighted mRS, independently developed for ICECAP.Bayesian adaptive design with response-adaptive randomization, interim analyses, and allocation guardrails.Management of missing data with multiple imputation from 30-day outcomes.For more, visit us at https://www.berryconsultants.com/
- 4 weeks ago32 min
Multi-Platform RCT
In this episode of "In the Interim…", Dr. Scott Berry details the design, execution, and results of the multi-platform randomized clinical trial (mpRCT) pioneered during the COVID-19 pandemic. He describes how REMAP-CAP, ATTACC, and ACTIV-4a—each developed independently—pooled data prospectively for joint analysis to address therapeutic anticoagulation in hospitalized COVID-19 patients. Scott outlines the operational rigor required to harmonize endpoints, establish monthly adaptive analyses, and stratify patients by disease severity and D-dimer level. He examines the unified Bayesian hierarchical modeling approach, dynamic borrowing across strata, and the process for simultaneous DSMB reviews coordinated across all platforms. The mpRCT framework enabled real-time, evidence-based adaptations and rigorous distinction of treatment effect by patient subgroup. Results were incorporated into clinical guidelines because prospectively specified analysis revealed benefit for moderate patients and futility or harm for severe patients—findings that would have been missed by standard post hoc pooling.Key HighlightsIntegration of REMAP-CAP, ATTACC, and ACTIV-4a under a prospectively unified analysis plan.Primary endpoint and stratified patient subgroups defined in advance.Monthly adaptive analyses using a shared Bayesian hierarchical model.Simultaneous oversight by joint statistical and DSMB committees.Superiority of therapeutic anticoagulation in moderate, non-critically ill groups; futility and possible harm in severe patients.mpRCT model established a framework for future global multi-platform trials.For more, visit us at https://www.berryconsultants.com/
- May 11, 202651 min
Sports and Clinical Trials: The 1927 Yankees, 15 Tarzans, and Modern Athletes
In this episode of "In the Interim…", Dr. Scott Berry examines the analytical challenges of comparing performance across eras in both sports and clinical research. Drawing from statistically robust family debates and published research, Scott details how overlapping competitors—such as athletes who played with both Babe Ruth, played with the next generation, who played with … all the way to playing with Aaron Judge—enable the estimation of temporal effects and allow for objective comparisons between generations. He translates this approach directly into platform clinical trials, demonstrating how overlapping trial arms or shared control groups make it possible to quantify and adjust for time trends. Scott distinguishes between observable, model-based comparisons and subjective judgments, rigorously addressing limitations such as interactions between treatments and era, and emphasizing the foundational importance of empirical overlap over speculative claims.Key HighlightsDeconstruction of time-machine thought experiments: analyzing how teams like the 1927 Yankees or athletes such as Johnny Weissmuller and Jesse Owens compare to present-day counterparts using statistical benchmarks.Technical explanation of connecting eras empirically through players or trial arms who span multiple time periods, thereby supporting quantitative estimation of temporal shifts.Detailed account of linear and hierarchical modeling strategies, with covariate adjustment for player age, period effects, and evolving population composition across baseball, hockey, and golf data.Translation of these statistical constructs to adaptive and platform clinical trials, exemplified by I-SPY 2, where overlapping treatment and control arms permit rigorous assessment of evolving treatment effects over a trial’s lifespan.Critical discussion of the rare but important possibility of treatment-by-era interactions, and the necessity of data-driven assessment rather than assumption.Consideration of how these methods inform not just debates about athletic greatness and Hall of Fame inclusion, but also robust interpretation of treatment effects in longitudinal clinical studies.For more, visit us at https://www.berryconsultants.com/
- May 4, 202651 min
AI @ Berry
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/
- Apr 27, 202651 min
Drug Development and Sports: The 10-Run Rule and Futility
In this episode of "In the Interim…", Dr. Scott Berry and Dr. Nick Berry investigate how futility in clinical trials and stopping rules in sports illuminate very similar decision problems, albeit with very different consequences. Drawing from baseball’s 10-run rule, tournament cuts in golf, the discussion confronts traditional and Bayesian strategies for interim decisions. The episode explains why simulation, not historical trial review, provides the empirical backbone for futility boundaries in clinical trials, and details the mechanics and consequences of aggressive stopping criteria. Using the Biogen aducanumab Alzheimer’s trials, the conversation exposes how a futility rule based on 20% predictive probability halted trials even when meaningful probability of success remained. Scott and Nick address the influence of ethical considerations, cost, regulatory priorities, and statistical rigor, and contrast Bayesian predictive probability’s strengths over conditional power.Key HighlightsDissects sports futility rules (10-run rule, golf cuts, Bill James heuristic) and their application to clinical trial designArgues for prospective simulation to define adaptive futility thresholdsExplains how Bayesian predictive probability provides a more robust framework than conditional probability for interim adaptive decisionsDetails how aggressive futility criteria may prematurely stop trials and risk missing beneficial treatments, as in the aducanumab caseExplores the intersection of ethics, patient safety, operational efficiency, regulatory standards, and trial cost
About In the Interim...
A podcast on statistical science and clinical trials. Explore the intricacies of Bayesian statistics and adaptive clinical trials. Uncover methods that push beyond conventional paradigms, ushering in data-driven insights that enhance trial outcomes while ensuring safety and efficacy. Join us as we dive into complex medical challenges and regulatory landscapes, offering innovative solutions tailored for pharma pioneers. Featuring expertise from industry leaders, each episode is crafted to provide clarity, foster debate, and challenge mainstream perspectives, ensuring you remain at the forefront of clinical trial excellence.
Customized Recaps
AI-powered recaps with compact key takeaways, quotes, and insights.
Straight to Your Inbox
Get key takeaways from In the Interim... in a 5-minute read.
Save Hours Every Week
Stay current on your favorite podcasts without falling behind.
Frequently Asked Questions
What is Podzilla's In the Interim... daily summary?
It's a free AI-powered email that summarizes new episodes of In the Interim... as soon as they're published. You get the key takeaways, notable quotes, and links & mentions — all in a quick read.
How does the In the Interim... podcast summary work?
When a new episode drops, our AI transcribes and analyzes it, then generates a personalized summary tailored to your interests and profession. It's delivered to your inbox every morning.
Is this an official In the Interim... product?
No. Podzilla is an independent service that summarizes publicly available podcast content. We're not affiliated with or endorsed by Berry.
Can I get summaries of other podcasts too?
Absolutely! The free plan covers up to 3 podcasts. Upgrade to Pro for 15, or Premium for 50. Browse our full catalog at /podcasts.
How often does In the Interim... release new episodes?
In the Interim... publishes weekly. Our AI generates a summary within hours of each new episode.
What topics does In the Interim... cover?
In the Interim... covers topics including Science, Medicine, Fitness, Mathematics, Health & Fitness. Our AI identifies the specific themes in each episode and highlights what matters most to you.
Start getting In the Interim... summaries tomorrow morning.
Free forever for up to 3 podcasts. No credit card required.
Free forever for up to 3 podcasts. No credit card required.