Journal article
Denghuang Zhan, Yongdong Ouyang, F. Vila-Rodriguez, Mohammad Ehsanul Karim, Hubert Wong
Comput. Methods Programs Biomed., 2025
Comput. Methods Programs Biomed., 2025
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APA
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Zhan, D., Ouyang, Y., Vila-Rodriguez, F., Karim, M. E., & Wong, H. (2025). Bayesian adaptive enrichment design in multi-arm clinical trials: The BayesAET package for R users. Comput. Methods Programs Biomed.
Chicago/Turabian
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Zhan, Denghuang, Yongdong Ouyang, F. Vila-Rodriguez, Mohammad Ehsanul Karim, and Hubert Wong. “Bayesian Adaptive Enrichment Design in Multi-Arm Clinical Trials: The BayesAET Package for R Users.” Comput. Methods Programs Biomed. (2025).
MLA
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Zhan, Denghuang, et al. “Bayesian Adaptive Enrichment Design in Multi-Arm Clinical Trials: The BayesAET Package for R Users.” Comput. Methods Programs Biomed., 2025.
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@article{denghuang2025a,
title = {Bayesian adaptive enrichment design in multi-arm clinical trials: The BayesAET package for R users},
year = {2025},
journal = {Comput. Methods Programs Biomed.},
author = {Zhan, Denghuang and Ouyang, Yongdong and Vila-Rodriguez, F. and Karim, Mohammad Ehsanul and Wong, Hubert}
}
Abstract
BACKGROUND Randomized controlled trials seldom assess treatment effect heterogeneity across subpopulations, potentially leading to suboptimal treatment recommendations and inefficient use of healthcare resources. Adaptive enrichment designs seek to identify patient subpopulations most likely to benefit from the treatment. This manuscript introduces BayesAET, an R package developed to support Bayesian adaptive enrichment trial designs. The package helps identify optimal treatments for pre-specified subpopulations within a broader patient population, improving the efficiency and relevant inference of clinical trials.
METHODS BayesAET integrates Bayesian multi-arm multi-stage designs with adaptive enrichment strategies. It allows for the incorporation of historical data through Bayesian priors, supports adaptive randomization and interim analyses. These features facilitate flexible but robust modifications to trial parameters based on accumulated data, including early stopping, dropping ineffective treatments, and adjusting randomization probabilities. The package supports various outcome types, including continuous, binary, and count outcomes.
RESULTS We showcase BayesAET through a case study of a trial evaluating repetitive transcranial magnetic stimulation for depression and anxiety. The trial involved three treatment protocols and two subpopulations (with and without benzodiazepine use). Simulations demonstrate that BayesAET effectively identifies differential treatment effects, adapts trial parameters based on interim data, and improves precision in treatment effect estimation.
CONCLUSION BayesAET provides a comprehensive tool for designing and analyzing Bayesian adaptive enrichment trials to identify the optimal treatments with pre-specified subpopulations.