Podcast Episode
November 9, 2026
No items found.

84: Mailbag: Learning Healthcare, Multiplicities, and the Pace of Innovation

Play video
Scott Berry, Ph.D.
President & Senior Statistical Scientist
In this mailbag episode of "In the Interim…", Dr. Scott Berry answers two listener questions and weighs in on the pace of innovation in clinical trial science.

In this mailbag episode of "In the Interim…", Dr. Scott Berry answers two listener questions and weighs in on the pace of innovation in clinical trial science. Asked what infrastructure a learning healthcare system would require, Scott defines one through the REMAP structure (randomized, embedded, multifactorial, adaptive platform) that Derek Angus named for REMAP-CAP, and uses REMAP Periop at UPMC to argue that embedding randomization and data collection in routine care keeps the cost low while letting a health system learn from tens of thousands of patients a year. On multiplicities, Scott explains why he interprets subgroups, endpoints, and related trials within a Bayesian hierarchical model rather than by adjusting alpha, with the assumed variability across units (tau) setting how far each estimate shrinks toward the overall effect, and he critiques hierarchical testing as non-Bayesian. The episode closes on the contrast between fast-moving innovation, including the AI-accelerated redesign of FACTS, Berry's clinical trial simulation platform, and ARPA-H's SURPASS call for adaptive platform trials, and the conservatism of the EMA's draft reflection paper on platform trials and ICH E20.

Key Highlights

  • REMAP (randomized, embedded, multifactorial, adaptive platform) as a working definition of a learning healthcare system.

  • Embedding randomization and data collection in routine care to remove a large share of trial costs.

  • REMAP Periop and learning from tens of thousands of elective surgeries a year.

  • A Bayesian view of multiplicities: hierarchical models in place of alpha adjustment.

  • Tau, shrinkage, and reading a subgroup result against the overall effect.

  • AI, digital twins, and ARPA-H's SURPASS call for continuous adaptive platform trials.

  • The contrast with the EMA's draft platform trial reflection paper and ICH E20.

Download PDF
View