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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Response adaptive randomisation in clinical trials: Current practice, gaps and future directions.

Isabelle Wilson¹, Steven Julious¹, Christina Yap²

¹SCHARR, The University of Sheffield, UK.

Statistical Methods in Medical Research

|June 18, 2025

Summary

Response adaptive randomization (RAR) trials show promise in optimizing treatment allocation and reducing sample sizes, particularly in oncology. Despite benefits, complexity and reporting gaps hinder wider adoption of this adaptive design method.

Keywords:

Response adaptive randomisation adaptive allocation adaptive design outcome adaptive randomisation randomised controlled trial reporting unequal treatment allocation

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Area of Science:

Clinical Trial Design
Biostatistics
Medical Research Methodology

Background:

Adaptive designs (ADs) allow clinical trials to modify parameters based on interim data.
Response adaptive randomization (RAR) is an AD that adjusts treatment allocation to favor more effective treatments.
Despite its advantages, RAR adoption is limited compared to other ADs.

Purpose of the Study:

To examine the application, results, and benefits of Response Adaptive Randomization (RAR) trials.
To identify reporting gaps, statistical tools, and concerns associated with RAR.
To highlight effective practices and future research needs in RAR implementation.

Main Methods:

A narrative and descriptive analysis of RAR trials with comparative efficacy, effectiveness, or safety objectives (Phase I/II or higher).
Searched statistical literature, trial registries, and researcher knowledge up to October 2023 (results until February 2024).
Screened 652 articles/trials, identifying 65 planned RAR trials (11 platform trials).

Main Results:

Most RAR trials were in oncology (25%) and drug treatments (80%), predominantly Phase II (62%) and multi-arm (63%), using Bayesian methods (85%).
71% of trials lacked clear statistical implementation details; 51% restricted allocation, and 88% included a burn-in period.
Trials with results showed a mean 22% sample size reduction, with no over-allocation to ineffective arms, though over 50% inadequately reported allocation changes.

Conclusions:

RAR has demonstrated benefits in sepsis, COVID-19, and cancer by improving treatment allocation and conserving resources.
Wider adoption of RAR is hindered by complexity, costs, and the need for robust simulation.
Enhancing statistical tools and addressing reporting gaps are crucial for promoting RAR in clinical research.