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Auxiliary variable-enriched biomarker-stratified design.

Ting Wang1, Xiaofei Wang2, Haibo Zhou1

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.

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|September 18, 2018
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Summary

Precision medicine trials can be costly. An auxiliary variable-enriched biomarker-stratified design (AEBSD) improves efficiency by oversampling patients likely to be biomarker-positive, reducing costs and waiting times.

Keywords:
Bayesian methodadaptive designauxiliary variablesbiomarker-stratified designcost minimizationenrichment strategyprecision medicinesurvival timetreatment selection

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Precision Medicine

Background:

  • Biomarker assessment is crucial for precision medicine clinical trials to identify patient subgroups for targeted therapies.
  • Standard biomarker-stratified designs (BSD) can be inefficient and costly, particularly with low prevalence subgroups or high biomarker assessment costs.

Purpose of the Study:

  • To introduce and evaluate an auxiliary variable-enriched biomarker-stratified design (AEBSD) for improving clinical trial efficiency.
  • To reduce costs and patient waiting times in biomarker-driven clinical trials.

Main Methods:

  • Proposed an auxiliary variable-enriched biomarker-stratified design (AEBSD) using an inexpensive, correlated auxiliary variable for enrichment.
  • Developed an adaptive Bayesian method to dynamically adjust the positive predictive value (PPV) during the trial.
  • Conducted numerical studies and presented an illustrative example.

Main Results:

  • AEBSD reduces total trial costs compared to standard BSD when biomarker prevalence is low and the auxiliary variable's PPV exceeds prevalence.
  • AEBSD allows immediate patient randomization post-screening, decreasing treatment waiting times.
  • The adaptive Bayesian method effectively adjusts for uncertainty in the PPV.

Conclusions:

  • AEBSD offers a more efficient and cost-effective approach for biomarker-stratified clinical trials, especially in precision medicine.
  • This design facilitates faster patient enrollment and treatment initiation.
  • An R package is available to implement the proposed methodology.