Related Experiment Video
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.
None:
In a multi-arm trial with predefined subgroups for each intervention to target, it is often desirable to enrich assignment to an intervention by enrolling more biomarker-positive participants to the intervention. We describe how to implement a biased coin design to achieve desired allocation ratios among interventions and between the number of biomarker-positive and biomarker-negative participants assigned to each intervention. We illustrate the proposed method with the randomization algorithm implemented in the Precision Interventions for Severe and/or Exacerbation-prone Asthma (PrecISE) trial.
More Related Videos
06:55Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
03:05Influence of Emotional Factors on the Efficacy of Acupuncture Treatment for Overweight Complicated with Hyperlipidemia: A Retrospective Cohort Study
Published on: November 21, 2025
Related Concept Videos
Group Design
Blind Procedures
Randomized Experiments
Simple randomization
Simple...
Blinding
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Strategies for Assessing and Addressing Confounding
Confounding can be addressed at both the design phase of a study and through analytical methods after data...