In this episode of "In the Interim...", Dr. Scott Berry hosts Dr. Stephen Senn, award-winning statistician and author, for a discussion on advanced challenges in adaptive and platform trial methodology. Senn draws on experience in academic, pharmaceutical, and regulatory settings to address the recent draft guidance on Bayesian statistics from the FDA and multiple controversies in clinical trial design.
Key Highlights
- Emphasizes understanding data origin and regression to the mean as essential for trial interpretation, above adherence to Bayesian or frequentist frameworks.
- Details methodological considerations for time adjustments and model complexity, highlighting that model specification and parameter handling are critical regardless of statistical school.
- Identifies the limitations of non-concurrent controls in platform trials, focusing on evolving background therapy, site participation, and protocol changes that reduce validity of historical or pooled control data.
- Analyzes blinding difficulties in trials with multiple treatments and administration modes, using “veiled” blinding as a case and noting the implications for placebo response comparability.
- Clarifies that operational efficiencies are the principal advantage of adaptive and platform trials, while purported statistical efficiencies can be exaggerated.
- Stresses the importance of presenting interim analyses transparently to DSMBs when using complex models for time or covariate adjustment, to ensure oversight and interpretation remain rigorous.
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