An alternative metric for evaluating the potential patient benefit of response-adaptive randomization procedures

Jennifer Proper, Thomas A. Murray

Research output: Contribution to journalArticlepeer-review


When planning a two-arm group sequential clinical trial with a binary primary outcome that has severe implications for quality of life (e.g., mortality), investigators may strive to find the design that maximizes in-trial patient benefit. In such cases, Bayesian response-adaptive randomization (BRAR) is often considered because it can alter the allocation ratio throughout the trial in favor of the treatment that is currently performing better. Although previous studies have recommended using fixed randomization over BRAR based on patient benefit metrics calculated from the realized trial sample size, these previous comparisons have been limited by failures to hold type I and II error rates constant across designs or consider the impacts on all individuals directly affected by the design choice. In this paper, we propose a metric for comparing designs with the same type I and II error rates that reflects expected outcomes among individuals who would participate in the trial if enrollment is open when they become eligible. We demonstrate how to use the proposed metric to guide the choice of design in the context of two recent trials in persons suffering out of hospital cardiac arrest. Using computer simulation, we demonstrate that various implementations of group sequential BRAR offer modest improvements with respect to the proposed metric relative to conventional group sequential monitoring alone.

Original languageEnglish (US)
Pages (from-to)1433-1445
Number of pages13
Issue number2
StatePublished - Jun 2023

Bibliographical note

Funding Information:
The authors thank the three anonymous referees, the associate editor, and the coeditor for their helpful comments that substantially improved the quality of this paper. The authors also thank Medtronic Inc. for their support in the form of a Biostatistics Faculty Fellowship. This work was supported by the NIH/NCATS under Grant UL1TR002494 and by the NHLBI under award number T32HL129956.

Publisher Copyright:
© 2022 The Authors. Biometrics published by Wiley Periodicals LLC on behalf of International Biometric Society.


  • Bayesian adaptive design
  • binary outcomes
  • decision theory
  • group sequential
  • phase II

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't
  • Research Support, N.I.H., Extramural


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