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 methods
• Ethical tension in trial conduct, referencing the Belmont Report and equipoise
• Advances in computation and Markov Chain Monte Carlo (MCMC)
• Regulatory frameworks for Bayesian adaptive trials, including FDA guidance
• Implementation details from I-SPY 2 and REMAP-CAP platform trials
• Bayesian methodology in the context of artificial intelligence, precision medicine, and future data integration

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

In episode 37 of "In the Interim…", Dr. Jeff Saver, Director of the UCLA Comprehensive Stroke and Vascular Neurology Program, details his shift from behavioral neurology to clinical stroke research after early engagement with multicenter trials like TOAST. The discussion covers the biology of acute ischemic stroke, quantifying neuronal loss, and the scientific underpinnings of “time is brain.” Dr. Saver outlines the evolution of endovascular therapy, from early device challenges to current reperfusion success rates exceeding 85%. Key methodological issues in stroke trial analyses are presented, including debate over endpoint selection—dichotomous versus ordinal approaches and the limitations therein. Special focus is placed on the utility-weighted modified Rankin Scale, which assigns empirically derived, patient-centered health values to each disability state, providing a comprehensive measure that captures both benefit and harm. The episode explores regulatory hesitancy, differing analytic preferences within the field, and the design prospects for neuroprotectant interventions. Heterogeneity in patient outcomes and implications for public health and trial methodology are addressed. The episode provides an empirical account of clinical trial endpoint selection, interpretation, and future directions in cerebrovascular research.

Key Highlights

• Early career influences and pivotal trial participation.
• Pathophysiology and quantification of acute stroke injury.
• Endovascular device development and clinical impact.
• Comparative analysis of endpoint methods: dichotomous, ordinal, and utility-weighted approaches.
• Technical derivation and application of utility-weighted mRS.
• Ongoing regulatory and methodological debate.
• Heterogeneity in ischemic vulnerability and future trial directions.

For 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 Highlights

• Alzheimer’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/

On this episode of “In the Interim…”, which is co-sponsored by the Journal of Statistics and Data Science Education, Dr. Scott Berry talks with Dr. Jim Albert, Professor Emeritus at Bowling Green State University, whose extensive work encompasses Bayesian statistics and computation, sports analytics, and decades of exemplary teaching. Dr. Albert shares insights on integrating sports into statistics education and discusses his transition from academic roots to consulting for the Houston Astros. This episode highlights the evolution of sports statistics—from manual data collection to sophisticated analytics—and critiques traditional metrics in favor of advanced systems. The dialogue explores career opportunities in sports statistics as well as the need for open research avenues in sports analytics, facilitating broader access and distribution of statistical insights.

Key Highlights

• Use of sports to contextualize statistical concepts, providing practical illustrations over abstract textbook issues
• Exposing misconceptions about randomness, streakiness, and “clutch ability” perpetuated by both public myths and sports simulations
• Analytical evolution from traditional metrics like batting average to advanced assessments like OPS and on-base percentage
• Regression-to-the-mean explained with sports scenarios and its analogous application in clinical trial progression
• Challenges in adopting a unified approach to teaching statistics given students’ diverse cultural and sports familiarity
• Barriers in publishing sports analytics research, prompting initiatives for accessible, open publications

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

In this episode of "In the Interim…", Dr. Scott Berry examines the concept of “digital twins” in clinical trials. He details how simulation of clinical trials is a direct analog of digital twin methodology, allowing for the in-silico modeling of the physical trial conduct, enrollment, dropouts, and patient outcomes under varied assumptions. Scott discusses model-based patient prediction and highlights scenarios where prediction of counterfactual outcomes can increase efficiency, particularly in rare disease or limited-data settings. He provides a systematic comparison of Unlearn’s PROCOVA neural network approach with traditional covariate adjustment, noting that proprietary models must demonstrate clear improvement over standard methods, which is unlikely. There is great potential in the simulation of many digital twins for a patient as a potential augmentation or substitute for controls.

Key Highlights

• Defines digital twins using NASA history and Wikipedia.
• Describes clinical trial simulation as a digital twin methodology.
• Examines patient-level model-based prediction and covariate adjustment.
• Compares Unlearn’s PROCOVA with traditional approaches.
• Highlights transparency and reproducibility concerns with proprietary algorithms.
• Asserts that future trial efficiency demands integration of predictive modeling with randomization and large external datasets.

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

In this episode of "In the Interim…", Dr. Scott Berry interviews Dr. Andrew Thomson, owner and lead consultant of Regnitio. Thomson discusses his academic progression from mathematics at Cambridge to a Master’s at Southampton and advanced study with Prof. Sylvia Richardson at Imperial College, followed by doctoral work in cluster randomized trials at the London School of Hygiene and Tropical Medicine. He recounts the realities of regulatory roles, including contemplative study of data, working within multidisciplinary teams, and delivering regulatory assessments to senior committees. The episode contrasts EMA’s collaborative cross-country structure against the more centralized FDA process and explores methodological challenges faced by both. Scott and Andrew discuss regulatory expectations for interim analyses, the definition and metrics of trial complexity, and differing approaches to Type I error control across agencies. The conversation also covers the rapid adoption and adaptation of platform trials during COVID-19, and the impact on trial evaluation frameworks. Concluding, Thomson explains the motivation for launching Regnitio, emphasizing how regulatory perspective and multidisciplinary insight can support informed decision-making throughout clinical development.

Key Highlights

• Academic and professional pathway: Cambridge, Southampton, Imperial College, London School of Hygiene and Tropical Medicine
• Roles as a statistical assessor: analysis, collaborative review, expert panel presentations
• EMA vs. FDA: consensus-driven versus centralized approaches, harmonization challenges
• Trial complexity, Interim analyses, and diversity in regulatory interpretations
• Adoption and practicalities of platform trials during the COVID-19 response
• Consulting goals: integrating regulatory perspective and broad expertise for drug development decisions

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

In this episode of "In the Interim…", Dr. Scott Berry and Dr. Kert Viele analyze how regulatory, editorial, and science community standards often impose additional, inconsistent requirements for novel methods in clinical trial design, rarely applied to standard approaches. Examples from oncology, enrichment trials, platform studies, and endpoint analysis illustrate how adaptive and Bayesian designs are frequently subject to higher scrutiny, shifting metrics, or distinct evidentiary demands. The episode covers technical and regulatory issues, such as the selective application of Type 1 error controls, evolving multiplicity guidance, and challenges in ethical reasoning with adaptive allocation. Scott and Kert frame the discussion with empirical comparisons and advocate for the use of clinical trial simulation to ensure fair, metric-driven evaluation of both novel and legacy designs.

Key Highlights:

• Oncology combination therapy trial with Bayesian borrowing facing heightened regulatory caution versus single-arm historical controls.
• Hierarchical versus pooled analysis in enrichment/basket trials, with focus on error definitions and subgroup effects that have always existed.
• ICH E20 guidance potentially discourages use of enrichment by imposing new subgroup comparison burdens absent from standard trials.
• Platform trial multiplicity rules contrasted with parallel single-arm trials; regulatory stance continues to evolve.
• Ethical debate on adaptive allocation: questioning rationale behind adaptive randomizing may be ethically challenging, but fixed allocation is okay despite same interim data.
• Critical review of explicit utility weighting in the DAWN trial, despite alternative methods having the same issues

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

In this episode of "In the Interim…", Dr. Scott Berry examines the mathematical foundations and efficiency claims of the promising zone design for adaptive sample size in clinical trials. Scott unpacks the conditional power thresholds that trigger sample size increases without the need to adjust alpha, as originally presented by Mehta & Pocock. He systematically demonstrates, via simulation, that the promising zone rarely provides meaningful efficiency gains over fixed designs and is consistently outperformed by group sequential designs that allocate alpha across multiple analyses. Using a driving-route analogy, Scott highlights the practical flaw in making pivotal trial decisions earlier than necessary due to arbitrary statistical rules rather than observing current data. He underlines that at Berry; simulation efforts have yet to reveal a scenario where the promising zone design is more efficient than a thoughtfully constructed group sequential or Goldilocks trial. The episode urges trialists to simulate, compare, and optimize—not to accept appealing mathematical tricks without rigorous evaluation.

Key Highlights

• Explanation of the promising zone’s conditional power mechanism and alpha control.
• Simulation-based comparison of power and average sample size across design types.
• Direct comparison of group sequential vs. promising zone designs.
• Discussion of futility rules and their impact on design choice.
• Commentary on Goldilocks designs for incomplete data.

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

Episode 30 of “In the Interim…” features Dr. Janet Wittes, Fellow of the American Statistical Association, past president of the Society of Clinical Trials, and founder of Statistics Collaborative, in discussion with Dr. Scott Berry. Dr. Wittes details her progression from Radcliffe biochemistry to Harvard statistics, shaped by targeted mentorship and her family’s insistence on advanced scientific training. She describes teaching at Hunter College, her NIH/NHLBI tenure overseeing extensive DSMB work, and the launch of Statistics Collaborative 32 years ago, building the business with her children and their peers. The episode explores her consulting on clinical trial design for orphan and neglected diseases—malaria, dengue, leishmania, ALS—and vaccine development, with technical commentary on adaptive trial methods, operational issues in low-resource contexts, and decision-making for small-sample trials. Dr. Wittes reflects on statistical leadership, ongoing DSMB involvement, and the importance of evidence-driven public health. She underscores the need for contextual and cultural awareness in trial design, illustrated by her Lilith magazine story on kosher certification and challenges in stakeholder understanding. Discussion covers career obstacles, the evolution of clinical science, vaccine advocacy, and the critical role of diversity and practical on-site knowledge in advancing statistical research.

Key Highlights

• Early academic transition from biochemistry to statistics.
• Serendipitous transition from academic career at Hunter College to Branch Chief of biostatistics at NIH/NHLBI.
• Founding Statistics Collaborative, business growth with children, and specialization in orphan disease trials.
• Consulting expertise in adaptive design, small-sample challenges, tropical and vaccine studies.
• Continued advocacy for vaccines, scientific rigor, and ethical public health practice.
• Importance of representation and context in science, demonstrated by real-world consulting examples.

In this episode of "In the Interim…", Dr. Scott Berry chats with Frank Harrell, a professor of Biostatistics at Vanderbilt University and W.J. Dixon Award winner. Harrell describes his transition from frequentist to Bayesian clinical trial design, prompted by a decisive meeting with Dr. Don Berry, informed by David Spiegelhalter’s published work. The dialogue addresses persistent academic opposition to Bayesian methods, operational constraints in trial implementation, regulatory work at FDA, and technical Bayesian modeling details.

Key Highlights

• Harrell credits Don Berry’s direct influence for converting him to Bayesian methods during his early career at Duke, despite entrenched academic resistance.
• Discusses early cardiovascular research at Duke, experiences with large multicenter trials, and later founding Vanderbilt’s Biostatistics department.
• Details the compromise of using Bayesian interim monitoring and frequentist primary analyses under NIH and regulatory mandates.
• Outlines design and publication of the ORBITA cardiovascular trial (Imperial College London), using all-Bayesian longitudinal ordinal methodology—Lancet reviewers required all analyses remain Bayesian, rejecting inclusion of a mixing frequentist and Bayesian analyses.
• Critiques simulation of Type 1 error within Bayesian trial designs.
• Addresses deficiencies in eliciting utilities for clinical endpoints, underscoring operational challenges in longitudinal ordinal modeling and ethical imperatives for efficient early stopping.