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Updated: May 7, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Covariate-adaptive biased coin randomization for master protocols with multiple interventions and
Tianhao Song1, Lisa M LaVange1, Anastasia Ivanova1
1Department of Biostatistics, CB #7420, the University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7420, USA.
This study introduces a biased coin design for clinical trials to improve intervention allocation. It ensures desired ratios of biomarker-positive and biomarker-negative participants are assigned to specific treatments.
Area of Science:
- Clinical Trials
- Biostatistics
- Genomic Medicine
Background:
- Enriching clinical trial arms with biomarker-positive participants can optimize intervention assignment.
- Achieving desired allocation ratios in multi-arm trials with subgroups is a complex challenge.
Purpose of the Study:
- To describe a biased coin design for optimizing intervention assignment in multi-arm trials.
- To achieve desired allocation ratios for interventions and biomarker status within subgroups.
Main Methods:
- Implementation of a biased coin randomization algorithm.
- Stratified randomization based on biomarker status and intervention groups.
- Application within the Precision Interventions for Severe and/or Exacerbation-prone Asthma (PrecISE) trial.
Main Results:
- The biased coin design allows for targeted enrollment of biomarker-positive individuals.
- Demonstrated achievement of desired allocation ratios in a real-world trial setting.
- Facilitates efficient and precise intervention assignment based on participant biomarkers.
Conclusions:
- Biased coin designs offer a flexible approach to enhance intervention enrichment in clinical trials.
- This method supports precise targeting of interventions to specific participant subgroups.
- The PrecISE trial exemplifies the successful application of this randomization strategy.
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