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

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.