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Leveraging machine learning: Covariate-adjusted Bayesian adaptive randomization and subgroup discovery in multi-arm
Wenxuan Xiong1, Jason Roy1, Hao Liu2
1Department of Biostatistics and Epidemiology, Rutgers University School of Public Health, Piscataway, NJ, USA.
Contemporary Clinical Trials
|April 30, 2024
Summary
This study introduces a novel Bayesian adaptive design for clinical trials to personalize treatments. It identifies patient subgroups who benefit most from specific therapies, improving treatment selection and trial power.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Personalized Medicine
Background:
- Clinical trials are essential for evaluating treatment safety and efficacy.
- Personalized medicine necessitates understanding how treatment effects vary across patient subgroups, often defined by biomarkers.
- Existing trial designs may not adequately address patient heterogeneity, potentially hindering the identification of optimal treatments for specific individuals.
Purpose of the Study:
- To introduce a novel Bayesian adaptive design for multi-arm clinical trials with time-to-event endpoints.
- To enhance treatment allocation by incorporating causal effect estimates and identifying patient subgroups with differential treatment benefits.
- To improve the precision and efficiency of clinical trials in the era of personalized medicine.
Main Methods:
- A covariate-adjusted response-adaptive randomization strategy is proposed.
- Treatment allocation probabilities are updated using causal effect estimates derived from a random intercept accelerated failure time BART model.
- A multi-response decision tree is utilized post-trial to identify subgroups with varying treatment impacts.
Main Results:
- The proposed Bayesian adaptive design demonstrates flexibility in identifying patient subgroups that respond differently to treatments.
- The covariate-adjusted randomization maintains trial power while adapting to emerging treatment effect data.
- Simulations confirm the design's ability to pinpoint subgroups benefiting most from specific interventions.
Conclusions:
- The developed Bayesian adaptive design offers a powerful and flexible approach for modern clinical trials.
- This design facilitates personalized medicine by identifying patient subgroups with differential treatment effects.
- The methodology enhances the potential to select optimal treatments for individuals based on their characteristics.
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