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. 2025 Sep;34(9):1851-1874.
doi: 10.1177/09622802251348183. Epub 2025 Jun 18.

Response adaptive randomisation in clinical trials: Current practice, gaps and future directions

Isabelle Wilson  1 , Steven Julious  1 , Christina Yap  2 , Susan Todd  3 , Munyaradzi Dimairo  1
Affiliations

Response adaptive randomisation in clinical trials: Current practice, gaps and future directions

Isabelle Wilson et al. Stat Methods Med Res. 2025 Sep.

Abstract

Introduction: Adaptive designs (ADs) offer clinical trials flexibility to modify design aspects based on accumulating interim data. Response adaptive randomisation (RAR) adjusts treatment allocation according to interim results, favouring promising treatments. Despite scientific appeal, RAR adoption lags behind other ADs. Understanding methods and applications could provide insights and resources and reveal future research needs. This study examines RAR application, trial results and achieved benefits, reporting gaps, statistical tools and concerns, while highlighting examples of effective practices. Methods: RAR trials with comparative efficacy, effectiveness or safety objectives, classified at least phase I/II, were identified via statistical literature, trial registries, statistical resources and researcher-knowledge. Search spanned until October 2023, including results until February 2024. Analysis was descriptive and narrative. Results: From 652 articles/trials screened, 65 planned RAR trials (11 platform trials) were identified, beginning in 1985 and gradually increasing through to 2023. Most trials were in oncology (25%) and drug-treatments (80%), with 63% led by US teams. Predominantly Phase II (62%) and multi-arm (63%), 85% used Bayesian methods, testing superiority hypotheses (86%). Binary outcomes appeared in 55%, with a median observation of 56 days. Bayesian RAR algorithms were applied in 83%. However, 71% of all trials lacked clear details on statistical implementation. Subgroup-level RAR was seen in 23% of trials. Allocation was restricted in 51%, and 88% was included a burn-in period. Most trials (85%) planned RAR alongside other adaptations. Of trials with results, 92% used RAR, but over 50% inadequately reported allocation changes. A mean 22% reduction in sample size was seen, with none over-allocating to ineffective arms. Conclusion: RAR has shown benefits in conditions like sepsis, COVID-19 and cancer, enhancing effective treatment allocation and saving resources. However, complexity, costs and simulation need limit wider adoption. This review highlights RAR's benefits and suggests enhancing statistical tools to encourage wider adoption in clinical research.

Keywords: Response adaptive randomisation; adaptive allocation; adaptive design; outcome adaptive randomisation; randomised controlled trial; reporting; unequal treatment allocation.

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Conflict of interest statement

Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Publications relating to RAR identified from Web of Science up to the end of 2023.
Figure 2.
Figure 2.
Information sources flow diagram.
Figure 3.
Figure 3.
PRISMA flow diagram.
Figure 4.
Figure 4.
Number of RAR trials over the years.
Figure 5.
Figure 5.
Location of lead investigator(s) by continent.
Figure 6.
Figure 6.
Visualisation of recruiting sites and combinations.
Figure 7.
Figure 7.
Visualisation of trial blinding and combinations.
Figure 8.
Figure 8.
Distribution of the number of treatment arms, including comparator(s), in non-platform (left) and platform (right) trials.
Figure 9.
Figure 9.
Visualisation of trial adaptations and combinations (alongside RAR).
Figure 10.
Figure 10.
Flow diagram for included platform trials (with some/all results available).
Figure 11.
Figure 11.
Sample size saving by (non-platform) trial (n = 37).
Figure 12.
Figure 12.
Planned versus actual sample size for non-platform trials (n = 27, excludes negative savings).
See this image and copyright information in PMC

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