Podcast Episode
June 15, 2026
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66: Response-Adaptive Randomization in Clinical Trials

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Scott Berry, Ph.D.
President & Senior Statistical Scientist
Kert Viele, Ph.D
Director & Senior Statistical Scientist
In this episode of "In the Interim…", Dr. Scott Berry and Dr. Kert Viele examine response-adaptive randomization (RAR) in clinical trials, dissecting its statistical rationale, common criticisms, and implementation challenges.

In this episode of "In the Interim…", Dr. Scott Berry and Dr. Kert Viele examine response-adaptive randomization (RAR) in clinical trials, dissecting its statistical rationale, common criticisms, and implementation challenges. Drawing on extensive experience with trials such as BAN2401 (lecanemab), ICECAP, dulaglutide seamless Phase 2/3, I-SPY2, REMAP-CAP, PROSPECT, and the historical ECMO trial, they illustrate where RAR adds scientific and ethical value—and where it introduces complications. The conversation clarifies that RAR reallocates patient assignments during interim analyses to direct more patients to better-performing arms, but this can reduce power in two-arm trials, introduce bias from temporal trends, and create operational complexity. The ECMO trial and "play-the-winner" approach are discussed as cautionary examples emphasizing the need for thorough simulation before deployment. The hosts highlight RAR’s strengths for dose-finding, multi-arm, and some platform designs, but underscore its limitations in confirmatory two-arm settings. Operational demands, data reliability, simulation across scenarios, and resistance to overgeneralization are recurrent themes. The episode concludes by situating RAR within the broader context of adaptive platform trials and learning healthcare systems.

Key Highlights

  • Definition and mechanics of RAR, with interim analysis guiding allocation updates

  • Multi-arm adaptive and platform trial experiences (BAN2401, ICECAP, dulaglutide, I-SPY2, REMAP-CAP, PROSPECT)

  • Critique of RAR in two-arm trials (power loss), temporal trends, unblinding, and overgeneralized literature

  • ECMO/play-the-winner: risks of poorly simulated RAR

  • Necessity for rigorous pre-trial simulation and robust data flows

  • Contextualization of RAR’s role in both traditional and learning healthcare environments

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