Comment: A Quarter Century of Methodological Research in Response-Adaptive Randomization

Ivanova, Anastasia; Rosenberger, William F.

doi:10.1214/23-sts865a

Cited by 2 publications

References 10 publications

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Doubly adaptive biased coin design to improve Bayesian clinical trials with time‐to‐event endpoints

Cao,

Zhu,

Wang

et al. 2024

Statistics in Medicine

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Clinical trialists often face the challenge of balancing scientific questions with other design features, such as improving efficiency, minimizing exposure to inferior treatments, and simultaneously comparing multiple treatments. While Bayesian response adaptive randomization (RAR) is a popular and effective method for achieving these objectives, it is known to have large variability and a lack of explicit theoretical results, making its use in clinical trials a subject of concern. It is desirable to propose a design that targets the same allocation proportion as Bayesian RAR and achieves the above objectives but addresses the concerns over Bayesian RAR. We propose the frequentist doubly adaptive biased coin designs (DBCD) targeting ethical allocation proportions from the Bayesian framework to satisfy different objectives in clinical trials with time‐to‐event endpoints. We derive the theoretical properties of the proposed adaptive randomization design and show through comprehensive numerical simulations that it can achieve ethical objectives without sacrificing efficiency. Our combined theoretical and numerical results offer a strong foundation for the practical use of RAR in real clinical trials.

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Doubly adaptive biased coin design to improve Bayesian clinical trials with time‐to‐event endpoints

Cao,

Zhu,

Wang

et al. 2024

Statistics in Medicine

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Response-Adaptive Randomization Designs Based on Optimal Allocation Proportions

Pin,

Villar,

Rosenberger

2024

Biostatistics in Biopharmaceutical Research and Development

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This book chapter discusses response-adaptive randomization procedures which are based on optimal allocation proportions, e.g. derived from theoretical optimization problems. We elucidate the objectives of these proportions, emphasizing their role in maximizing statistical power and/or enhancing patient benefit. Moreover, practical insights on estimation and targeting of these allocation proportions are provided. A clinical trial example, the CALISTO trial, illustrates the application of these procedures. We aim to provide an overview that serves as a basis for discussions about optimal allocation proportions, assists in their implementation in practice and highlights future research areas.

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Comment: A Quarter Century of Methodological Research in Response-Adaptive Randomization

Cited by 2 publications

References 10 publications

Doubly adaptive biased coin design to improve Bayesian clinical trials with time‐to‐event endpoints

Doubly adaptive biased coin design to improve Bayesian clinical trials with time‐to‐event endpoints

Response-Adaptive Randomization Designs Based on Optimal Allocation Proportions

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