Adaptive enrichment designs in clinical trials

Research output: Contribution to journalReview articlepeer-review


Adaptive enrichment designs for clinical trials may include rules that use interim data to identify treatment-sensitive patient subgroups, select or compare treatments, or change entry criteria. A common setting is a trial to compare a new biologically targeted agent to standard therapy. An enrichment design's structure depends on its goals, how it accounts for patient heterogeneity and treatment effects, and practical constraints. This article first covers basic concepts, including treatment-biomarker interaction, precision medicine, selection bias, and sequentially adaptive decision making, and briefly describes some different types of enrichment. Numerical illustrations are provided for qualitatively different cases involving treatment-biomarker interactions. Reviews are given of adaptive signature designs; a Bayesian design that uses a random partition to identify treatment-sensitive biomarker subgroups and assign treatments; and designs that enrich superior treatment sample sizes overall or within subgroups, make subgroup-specific decisions, or include outcome-adaptive randomization.

Original languageEnglish (US)
Pages (from-to)393-411
Number of pages19
JournalAnnual Review of Statistics and Its Application
StatePublished - Mar 7 2021


  • adaptive signature design
  • Bayesian design
  • biomarker
  • clinical trial
  • group sequential design
  • precision medicine
  • subset selection
  • targeted therapy
  • variable selection

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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