# Response-adaptive randomization in clinical trials: from myths to practical considerations

arXiv: https://arxiv.org/abs/2005.00564 · HTML: https://mlsanity.com/paper/2005.00564/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2020.

## Built on by (most cited first, 10 of 10)

- [Modern Bayesian Experimental Design](https://mlsanity.com/paper/2302.14545/) (2023)
- [Multi-disciplinary fairness considerations in machine learning for clinical trials](https://mlsanity.com/paper/2205.08875/) (2022)
- [Screening for Diabetes Mellitus in the U.S. Population Using Neural Network Models and Complex Survey Designs](https://mlsanity.com/paper/2403.19752/) (2024)
- [Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions](https://mlsanity.com/paper/2503.09226/) (2025)
- [Some performance considerations when using multi-armed bandit algorithms in the presence of missing data](https://mlsanity.com/paper/2205.03820/) (2022)
- [Designing Time Series Experiments in A/B Testing with Transformer Reinforcement Learning](https://mlsanity.com/paper/2602.01853/) (2026)
- [Adaptive Experimentation for Censored Survival Outcomes](https://mlsanity.com/paper/2605.18459/) (2026)
- [On Response-Adaptive Targeting Strategies for Multi-Treatment Experiments](https://mlsanity.com/paper/2606.17777/) (2026)
- [Full Bayesian Reinforcement Learning via LF-IBIS](https://mlsanity.com/paper/2607.01741/) (2026)
- [Longitudinal Adaptive Experimental Design for Learning Multiple Target Estimands with Semiparametric Efficient Inference](https://mlsanity.com/paper/2607.29421/) (2026)

## Abstract (arXiv)

Response-Adaptive Randomization (RAR) is part of a wider class of data-dependent sampling algorithms, for which clinical trials are typically used as a motivating application. In that context, patient allocation to treatments is determined by randomization probabilities that change based on the accrued response data in order to achieve experimental goals. RAR has received abundant theoretical attention from the biostatistical literature since the 1930's and has been the subject of numerous debates. In the last decade, it has received renewed consideration from the applied and methodological communities, driven by well-known practical examples and its widespread use in machine learning. Papers on the subject present different views on its usefulness, and these are not easy to reconcile. This work aims to address this gap by providing a unified, broad and fresh review of methodological and practical issues to consider when debating the use of RAR in clinical trials.