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Bayesian Adaptive Enrichment Design for Continuous Biomarkers.

Yue Tu1, Yusha Liu2, Wendy J Mack1

  • 1Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, USA.

Statistics in Medicine
|September 14, 2025
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Summary
This summary is machine-generated.

This study introduces an adaptive clinical trial design for continuous biomarkers in cancer therapy. It improves efficiency and patient-centered decisions by utilizing full biomarker information, unlike methods that simplify marker relationships.

Keywords:
Bayesian adaptive designadaptive enrichment designadaptive randomizationbiomarker‐driven designclinical trialprecision medicine

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Precision Medicine

Background:

  • Precision medicine in cancer relies on predictive biomarkers for targeted therapies.
  • Current clinical trial designs often dichotomize continuous biomarkers, losing valuable information.
  • Existing methods may oversimplify complex biomarker-treatment interactions.

Purpose of the Study:

  • To propose a novel adaptive enrichment trial design for continuous biomarkers.
  • To accommodate biomarkers with any effect shape (linear, nonlinear, non-monotone).
  • To improve statistical efficiency and patient-centered decision-making in cancer clinical trials.

Main Methods:

  • Development of a Bayesian marker-adaptive randomization design.
  • Handling continuous predictive biomarkers without upfront dichotomization.
  • Comparison with adaptive cut-point selection methods lacking adaptive randomization.

Main Results:

  • The proposed design efficiently handles continuous biomarkers with diverse effect shapes.
  • It achieves high efficiency in making marker-specific trial decisions.
  • Demonstrates improved performance over simplified cut-point selection approaches.

Conclusions:

  • Adaptive enrichment designs with Bayesian marker-adaptive randomization are effective for continuous biomarkers.
  • This approach preserves information from continuous biomarkers, leading to better trial outcomes.
  • Offers a more sophisticated and patient-centered alternative to traditional biomarker handling in clinical trials.