In this episode of "In the Interim…", Dr. Scott Berry speaks with Dr. Michael Harhay, Associate Professor at the University of Pennsylvania and Director of the Center for Clinical Trials Innovation. The conversation explores Dr. Harhay’s progression through neuroscience, philosophy, epidemiology, and statistics, examining how this academic path shapes his work in clinical trial methodology. They discuss the Center’s role in addressing unresolved methodological questions arising from pragmatic, health system-based trials, including challenges with cluster and factorial randomized designs. The episode focuses on statistical and conceptual issues in endpoint selection for critical care, such as the analysis of informatively truncated outcomes, composite endpoints including organ support-free days, and the application of the win ratio. The increasing use of Bayesian methods in trial design is addressed.

Key Highlights

• Dr. Harhay’s academic background and transition into clinical trial methodology at Penn.
• The mission of the Center for Clinical Trials Innovation to support methodologic research and training, particularly among statisticians participating in multi-center health system trials.
• Discussion of hospital-level and provider-level randomization strategies in cluster and factorial designs within health systems.
• Ongoing challenges in analysis of composite and informatively truncated endpoints, especially in critical care, exemplified by ventilator-free and organ support-free days.
• Evaluation of analytic strategies including survival average causal effect, composite endpoints, and the win ratio, with emphasis on the need for clinical rather than purely statistical weighting of outcomes.
• Consideration of the conceptual strengths of Bayesian methods and their integration into modern trial design and decision analysis.

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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 Kert Viele deliver a quick reaction to the FDA’s draft guidance on Bayesian statistics for clinical trials of drugs and biologics.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 Highlights

• Clear 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-trials

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In this episode of "In the Interim…", Dr. Scott Berry is joined by Dr. Tanya Simuni, Arthur C. Nielsen Jr. Professor of Neurology and Director of the Parkinson’s Disease and Movement Disorders Center at Northwestern University, and Dr. Barbara Wendelberger, Senior Statistical Scientist at Berry Consultants. The conversation focuses on the Path to Prevention (P2P) platform trial—an international, multi-arm prevention study in Parkinson’s disease targeting participants defined by biological markers, specifically alpha-synuclein pathology, prior to clinical diagnosis. The discussion covers the PPMI cohort, trial operational and statistical structure, the rationale behind biomarker-driven inclusion, and the use of Bayesian platform trial design.

Key Highlights:

• Parkinson’s disease pathobiology and risk: genotype-phenotype variability, multi-system involvement, and the central roles of age, environment, and genetics.
• Michael J. Fox Foundation’s PPMI cohort: 4,000+ participants, prospective longitudinal biomarker and clinical data, high participant retention, enabling study of early Parkinson’s.
• P2P platform structure: multi-arm design, two-stage randomization with shared placebo group, integration of non-randomized PPMI cohort in Bayesian analysis for improved inference.
• Inclusion criteria: prodromal population biologically defined by CSF alpha-synuclein seed amplification and dopaminergic imaging (DAT-SPECT), highlighting regulatory nuances.
• Dual primary endpoints: biomarker (DAT-SPECT) and clinical (MDS-UPDRS Part III), 24-36 months follow-up.
• Commitment to public data sharing in line with the Michael J. Fox Foundation’s open science philosophy.

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