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An alternative method to analyse the biomarker-strategy design.

Cornelia Ursula Kunz1,2,3, Thomas Jaki1, Nigel Stallard4

  • 1Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.

Statistics in Medicine
|September 28, 2018
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This study resolves issues in biomarker strategy clinical trial analysis by ensuring subgroup sample sizes meet orthogonality conditions. A new analysis strategy is proposed for accurate treatment effect and biomarker interaction assessment.

Keywords:
analysis strategybiomarkerdesigninteractionpersonalised medicine

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Genomics and Proteomics

Background:

  • Biomarker discovery enables patient subgroup identification for targeted treatments.
  • Biomarker strategy (marker-based) designs are used in clinical trials.
  • Existing analysis methods for these designs have identified limitations.

Purpose of the Study:

  • To address and resolve analytical problems in biomarker strategy designs.
  • To propose a novel analysis strategy for biomarker-guided trials.
  • To provide methods for sample size calculation and effect estimation.

Main Methods:

  • Ensuring subgroup sample sizes meet orthogonality conditions.
  • Developing a new analysis strategy for biomarker-by-treatment interaction, treatment effect, and biomarker effect.
  • Deriving sample size equations for perfect and imperfect biomarker assays.
  • Providing point estimators and confidence intervals for subgroup treatment effects.

Main Results:

  • Problems in biomarker strategy design analysis are resolved with orthogonality.
  • The proposed strategy allows for testing biomarker-by-treatment interaction, treatment effect, and biomarker effect.
  • Sample size calculation methods are derived for various biomarker assay qualities.
  • 1:1 randomization is not always optimal for minimizing sample size.

Conclusions:

  • The proposed methods enhance the analysis of biomarker strategy clinical trials.
  • Accurate estimation of treatment effects within subgroups is achievable.
  • The findings offer practical guidance for optimizing clinical trial design and sample size calculations.