Adaptive enrichment trials: What are the benefits?

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7 Citations (SciVal)


When planning a Phase III clinical trial, suppose a certain subset of patients is expected to respond particularly well to the new treatment. Adaptive enrichment designs make use of interim data in selecting the target population for the remainder of the trial, either continuing with the full population or restricting recruitment to the subset of patients. We define a multiple testing procedure that maintains strong control of the familywise error rate, while allowing for the adaptive sampling procedure. We derive the Bayes optimal rule for deciding whether or not to restrict recruitment to the subset after the interim analysis and present an efficient algorithm to facilitate simulation-based optimisation, enabling the construction of Bayes optimal rules in a wide variety of problem formulations. We compare adaptive enrichment designs with traditional nonadaptive designs in a broad range of examples and draw clear conclusions about the potential benefits of adaptive enrichment.

Original languageEnglish
Pages (from-to)690-711
Number of pages22
JournalStatistics in Medicine
Issue number3
Early online date26 Nov 2020
Publication statusPublished - 10 Feb 2021

Bibliographical note

Funding Information:
The first author received financial support for this research from the UK Engineering and Physical Sciences Research Council and Hoffman‐LaRoche Ltd. The authors would like to thank to Lucy Rowell for her contributions to this project.


  • adaptive designs
  • adaptive enrichment
  • Bayesian optimization
  • phase III clinical trial
  • population enrichment

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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