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Biomarker-guided adaptive enrichment design with threshold detection for clinical trials with time-to-event outcome.

Kaiyuan Hua1, Hwanhee Hong1, Xiaofei Wang1

  • 1Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, USA.

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Summary
This summary is machine-generated.

This study introduces a novel biomarker-guided adaptive design for clinical trials using restricted mean survival time (RMST) to efficiently evaluate personalized treatments in severe diseases. The proposed method optimizes patient enrichment and biomarker threshold selection for improved treatment effect analysis.

Keywords:
Biomarker adaptive designcontinuous biomarkerpatient enrichmentrestricted mean survival timethreshold detection

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Area of Science:

  • Clinical trial design
  • Biostatistics
  • Personalized medicine

Background:

  • Biomarker-guided adaptive enrichment designs enhance clinical trial efficiency by focusing on biomarker-positive patients.
  • Time-to-event outcomes are crucial for severe diseases, but research on adaptive designs using restricted mean survival time (RMST) is limited.
  • Hazard ratio methods are standard but lack the interpretability and assumptions of RMST.

Purpose of the Study:

  • To propose a novel two-stage biomarker-guided adaptive restricted mean survival time (RMST) design.
  • To develop methods for optimal biomarker threshold and subgroup identification.
  • To provide tools for treatment effect estimation, error rate control, power, and sample size calculations in adaptive enrichment trials.

Main Methods:

  • A two-stage adaptive enrichment design incorporating biomarker threshold detection.
  • Development of treatment effect estimators based on RMST.
  • Methods for type I error rate control, power analysis, and sample size determination.
  • Numerical example of an oncology trial re-design and extensive simulation studies.

Main Results:

  • The proposed design effectively identifies optimal biomarker thresholds and enriches biomarker-positive subgroups.
  • The methods provide accurate treatment effect estimation and robust statistical power.
  • Simulation studies validate the performance of the adaptive RMST design.

Conclusions:

  • The developed biomarker-guided adaptive RMST design offers an efficient and interpretable approach for personalized medicine trials.
  • This design facilitates better evaluation of treatments in biomarker-defined subgroups for severe diseases.
  • The methodology provides a valuable framework for future clinical trial designs in oncology and other fields.