Ready for the Clinic? A Survey of Open-Source Software for Response-Adaptive Randomization in Clinical Trials
Abstract
Response-adaptive randomization (RAR) modifies treatment allocation probabilities during a clinical trial as response/outcome data accumulate, with the aim of improving patient benefit, statistical efficiency, or both. Despite substantial methodological development, adoption of RAR in clinical practice has remained limited, and the software available to support its design and implementation has not previously been reviewed. We identified 16 publicly available, open-source software packages implementing RAR methods, spanning urn-based, target-allocation, Bayesian, Markov decision process (MDP)-based, and dose-finding approaches, and evaluated them with respect to their methodological and practical characteristics. We found that while several software packages exist, most are method-specific, and only a small number provide broader, general-purpose adaptive-trial design and analysis capabilities. Explicit support for practically relevant features, including delayed and missing outcome data, temporal trends in response rates, flexible operating-characteristic evaluation, and platform or multi-arm multi-stage trial designs, is rare or absent across the identified software. These findings suggest that while a diverse set of tools exists for exploring RAR designs, gaps remain between the methodological literature and the software available to implement it in practice. Continued development of flexible, practically oriented, and validated software is important for the wider adoption of RAR in clinical research, and we highlight interesting areas of further work.
Related
- Adaptive stopping and forecasting of screening, rescreening and randomization in multicentre clinical trials
- Discretization in covariate-adaptive randomization: gains and losses
- Strong Simultaneous Inference for Restricted Mean Survival Time Processes in Multi-Arm Trials under Covariate-Adaptive Randomization
- Covariate-adaptive randomization using historical data and bootstrap-based inference
- Overlapped We Stand, Separated We Fall: Software Approaches to Diagnosing Separation Phenomena in Categorical Response Models
BibTeX
@misc{zetterstrom2026ready,
title = {{Ready for the Clinic? A Survey of Open-Source Software for Response-Adaptive Randomization in Clinical Trials}},
author = {Stina Zetterstrom and David S. Robertson and Sofía S. Villar},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2609.35939},
eprint = {2609.35939},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.35939},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Ready for the Clinic? A Survey of Open-Source Software for Response-Adaptive Randomization in Clinical Trials
AU - Stina Zetterstrom
AU - David S. Robertson
AU - Sofía S. Villar
PY - 2026
DA - 2026/10/01
PB - arXiv
DO - 10.48550/arxiv.2609.35939
UR - https://arxiv.org/abs/2609.35939
AB - Response-adaptive randomization (RAR) modifies treatment allocation probabilities during a clinical trial as response/outcome data accumulate, with the aim of improving patient benefit, statistical efficiency, or both. Despite substantial methodological development, adoption of RAR in clinical practice has remained limited, and the software available to support its design and implementation has not previously been reviewed. We identified 16 publicly available, open-source software packages implementing RAR methods, spanning urn-based, target-allocation, Bayesian, Markov decision process (MDP)-based, and dose-finding approaches, and evaluated them with respect to their methodological and practical characteristics. We found that while several software packages exist, most are method-specific, and only a small number provide broader, general-purpose adaptive-trial design and analysis capabilities. Explicit support for practically relevant features, including delayed and missing outcome data, temporal trends in response rates, flexible operating-characteristic evaluation, and platform or multi-arm multi-stage trial designs, is rare or absent across the identified software. These findings suggest that while a diverse set of tools exists for exploring RAR designs, gaps remain between the methodological literature and the software available to implement it in practice. Continued development of flexible, practically oriented, and validated software is important for the wider adoption of RAR in clinical research, and we highlight interesting areas of further work.
ER -