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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Sample size methods for evaluation of predictive biomarkers.

Kevin K Dobbin1, Lisa M McShane2

  • 1Department of Epidemiology and Biostatistics, University of Georgia, Athens, Georgia, USA.

Statistics in Medicine
|May 1, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new sample size method for evaluating therapy selection biomarkers. The method ensures adequate sample sizes for estimating biomarker-guided therapy benefits compared to standard care.

Keywords:
confidence intervalrandomized clinical trialssample sizetreatment selection biomarkers

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Biomarker Research

Background:

  • Therapy selection biomarkers require rigorous evaluation to guide clinical decisions.
  • Appropriate study design, including adequate sample size, is crucial for biomarker validation.
  • Existing methods may not sufficiently address the estimation of biomarker-guided therapy benefits.

Purpose of the Study:

  • To develop a novel sample size calculation method for therapy selection biomarkers.
  • To estimate the expected benefit of biomarker-guided therapy versus standard-of-care.
  • To provide a robust method applicable to various statistical models and scenarios.

Main Methods:

  • Developed a sample size method for estimating a confidence interval of specified average width.
  • Utilized a combination of Monte Carlo simulation and regression analysis.
  • Evaluated robustness under Cox proportional hazards, accelerated failure time, and cure models.

Main Results:

  • The proposed sample size method yields adequate or conservative estimates across diverse scenarios.
  • The method demonstrates robustness to violations of underlying statistical model assumptions.
  • Software implementations in R and C++ are provided for practical application.

Conclusions:

  • The novel sample size method effectively supports the evaluation of therapy selection biomarkers.
  • This approach facilitates accurate estimation of treatment benefits in biomarker-guided studies.
  • Availability of computational tools promotes wider adoption and implementation in clinical research.