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Published on: October 11, 2018

Biomarker-adaptive threshold design: a procedure for evaluating treatment with possible biomarker-defined subset

Wenyu Jiang1, Boris Freidlin, Richard Simon

  • 1Biometric Research Branch, Division of Cancer Treatment and Diagnosis, EPN-8122, National Cancer Institute, Bethesda, MD 20892, USA.

Journal of the National Cancer Institute
|June 29, 2007
PubMed
Abstract

Insights

This study introduces a biomarker-adaptive trial design to improve the efficiency of identifying effective anticancer agents in sensitive patient subsets. This method enhances power when only a small group benefits, unlike traditional trials.

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Oncology

Background:

  • Many targeted cancer therapies benefit only specific patient groups.
  • Traditional trials may miss effective agents due to diluted treatment effects in broad populations.
  • Biomarkers can identify sensitive subpopulations for targeted therapies.

Purpose of the Study:

  • To propose a statistically rigorous biomarker-adaptive threshold Phase III design.
  • To address challenges in evaluating targeted therapies with patient subsets.
  • To identify sensitive patient populations using continuous or graded biomarkers.

Main Methods:

  • Combined overall treatment effect testing with biomarker cut-point establishment and validation.
  • Evaluated biomarker-adaptive design performance against traditional designs via simulation.
  • Applied the biomarker-adaptive design to a prostate cancer clinical trial dataset.

Main Results:

  • The adaptive design maintained power for overall treatment effects in broadly effective treatments.
  • Significant efficiency gains were observed with the adaptive design when the sensitive population was small.
  • Provided recommendations for sample size and implementation of the adaptive design.

Conclusions:

  • A biomarker-defined subset effect test can be integrated into Phase III designs.
  • This integration does not compromise the ability to detect overall treatment benefits.
  • The proposed design offers a statistically valid approach for targeted therapy evaluation.

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