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Adaptive enrichment designs with a continuous biomarker.

Nigel Stallard1

  • 1Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK.

Biometrics
|February 17, 2022
PubMed
Summary
This summary is machine-generated.

Adaptive enrichment trials face statistical challenges. This study provides methods to control Type I error rates when selecting subgroups based on early trial data, ensuring reliable treatment effect inference.

Keywords:
multiple cut-pointssequential analysisstratified medicine clinical trial designsubgroup selection

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Genomic Medicine

Background:

  • Two-stage adaptive enrichment designs are popular for targeted therapies.
  • Biomarker selection in stage 2 based on stage 1 data poses statistical inference challenges.

Purpose of the Study:

  • To develop statistical methods for controlling familywise Type I error rate (FWER) in adaptive enrichment trials.
  • To address challenges in treatment effect inference when using data-dependent subgroup selection.

Main Methods:

  • Utilized group-sequential trial distribution properties for nested subgroups.
  • Proposed multivariate normal and Brownian motion approximation methods for statistical testing.
  • Applied methods to survival data from a breast cancer clinical trial.

Main Results:

  • Developed tests controlling FWER for six subgroup selection rules.
  • Identified one rule that controls FWER for any selection strategy.
  • Demonstrated applicability to asymptotically normal test statistics.

Conclusions:

  • The proposed methods provide valid statistical inference for adaptive enrichment designs.
  • These approaches enhance the reliability of treatment effect estimation in biomarker-selected subgroups.
  • The study offers practical solutions for complex clinical trial designs.