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/