In this episode of "In the Interim…", Dr. Scott Berry challenges the widely held belief that any interim look at trial data obligates an alpha adjustment. By constructing a two-by-two matrix: interim data (positive/negative) and adaptive action (increase/decrease sample size), Scott demonstrates that the need for statistical correction depends on precisely what actions are prespecified. He emphasizes that the need for adjustment depends on the action and the data. Technical scenarios examined include group sequential designs, “promising zone” sample size re-estimation (citing the formal results of Mehta and Pocock), and response adaptive randomization. Scott stresses that clear prespecification is required for Type I error control and regulatory compliance. He critiques common missteps, such as unnecessary allocation of alpha to futility boundaries when superiority is not planned, and reiterates that it is the adaptive action, and not mere data review, that determines the statistical impact of interim analyses.

Key Highlights

• Dissects alpha adjustment myths and their historical roots.
• Details two-by-two matrix: interim data direction and adaptive action.
• Explores group sequential, futility, promising zone, and response adaptive examples.
• Clarifies when Type I error is truly affected—action and data matter.
• Stresses prespecification’s role in trial validity and regulatory acceptance.
• Identifies pitfalls in common trial design practices.

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