ArticleBiometrics2020
A response-adaptive randomization procedure for multi-armed clinical trials with normally distributed outcomes.
Article in Biometrics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Response adaptive randomisation in clinical trials: Current practice, gaps and future directions.Statistical methods in medical research · 2025Article
- Adaptive randomization methods for sequential multiple assignment randomized trials (smarts) via thompson sampling.Biometrics · 2024Article
- Response-adaptive randomization in clinical trials: from myths to practical considerations.Statistical science : a review journal of the Institute of Mathematical Statistics · 2023Article
- A novel statistical test for treatment differences in clinical trials using a response-adaptive forward-looking Gittins Index Rule.Biometrics · 2023Article
- Generalisations of a Bayesian decision-theoretic randomisation procedure and the impact of delayed responses.Computational statistics & data analysis · 2022Article
- Some performance considerations when using multi-armed bandit algorithms in the presence of missing data.PloS one · 2022Article
- The Contribution of Artificial Intelligence in Achieving the Sustainable Development Goals (SDGs): What Can Eye Health Can Learn From Commercial Industry and Early Lessons From the Application of Machine Learning in Eye Health Programmes.Frontiers in public health · 2021Article
- Efficient Adaptive Designs for Clinical Trials of Interventions for COVID-19.Statistics in biopharmaceutical research · 2020Article
- A response-adaptive randomization procedure for multi-armed clinical trials with normally distributed outcomes.Biometrics · 2020Article
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Authors and funding
2 authors.
Funding
Abstract
We propose a novel response-adaptive randomization procedure for multi-armed trials with continuous outcomes that are assumed to be normally distributed. Our proposed rule is non-myopic, and oriented toward a patient benefit objective, yet maintains computational feasibility. We derive our response-adaptive algorithm based on the Gittins index for the multi-armed bandit problem, as a modification of the method first introduced in Villar et al. (Biometrics, 71, pp. 969-978). The resulting procedure can be implemented under the assumption of both known or unknown variance. We illustrate the proposed procedure by simulations in the context of phase II cancer trials. Our results show that, in a multi-armed setting, there are efficiency and patient benefit gains of using a response-adaptive allocation procedure with a continuous endpoint instead of a binary one. These gains persist even if an anticipated low rate of missing data due to deaths, dropouts, or complete responses is imputed online through a procedure first introduced in this paper. Additionally, we discuss how there are response-adaptive designs that outperform the traditional equal randomized design both in terms of efficiency and patient benefit measures in the multi-armed trial context.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.