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Biomarker threshold adaptive designs for survival endpoints.

Guoqing Diao1, Jun Dong2, Donglin Zeng3

  • 1a Department of Statistics , George Mason University , Fairfax , Virginia , USA.

Journal of Biopharmaceutical Statistics
|February 14, 2018
PubMed
Summary

This study introduces a new adaptive design using biomarkers to find patient subgroups most likely to benefit from a new treatment. This precision medicine approach optimizes treatment selection for better outcomes.

Keywords:
Adaptive enrichment designpredictive biomarkersurvival endpointtwo-stage design

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Area of Science:

  • Clinical trial design
  • Biostatistics
  • Precision medicine

Background:

  • Identifying optimal patient subgroups is crucial for effective precision medicine.
  • Biomarkers are key tools for patient stratification in clinical research and practice.
  • Current methods may not fully leverage biomarker information for adaptive treatment selection.

Purpose of the Study:

  • To propose a novel biomarker threshold adaptive design for clinical trials with survival endpoints.
  • To identify patient subgroups that exhibit the greatest benefit from a new therapeutic intervention.
  • To enhance patient stratification for personalized treatment strategies.

Main Methods:

  • A two-stage adaptive design is described, focusing on biomarker thresholds.
  • Stage one involves identifying responsive subgroups using historical or pilot data.
  • Stage two involves randomizing subjects from identified subgroups to treatment or control arms.

Main Results:

  • Extensive simulations were performed to evaluate the proposed design's performance.
  • The design demonstrates effectiveness in identifying patient subgroups with differential treatment benefits.
  • A real data example illustrates the practical implementation of the first-stage algorithms.

Conclusions:

  • The biomarker threshold adaptive design offers a robust framework for precision medicine.
  • This approach facilitates the selection of patients most likely to respond to novel treatments.
  • The design supports efficient and targeted clinical trial execution for personalized therapies.