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Updated: Dec 25, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Optimized multiple testing procedures for nested sub-populations based on a continuous biomarker.

Alexandra Christine Graf1, Dominic Magirr2, Alex Dmitrienko3

  • 1Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria.

Statistical Methods in Medical Research
|April 1, 2020
PubMed
Summary
This summary is machine-generated.

Identifying patient subgroups for targeted therapies requires optimal biomarker thresholds. This study proposes a framework to select thresholds that maximize power for detecting treatment effects in specific subgroups, improving clinical trial efficiency.

Keywords:
Nested subgroupsbiomarkergroup sequential designmultiple testingsubgroup analysis

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmacogenomics

Background:

  • Developing targeted therapies necessitates identifying patient subgroups with differential treatment effects.
  • Subgroup identification often relies on continuous biomarkers and predefined thresholds, posing challenges with limited prior information.

Purpose of the Study:

  • To propose a novel framework for selecting optimal biomarker thresholds and multiple testing procedures.
  • To maximize the power of detecting at least one subgroup with a positive treatment effect.

Main Methods:

  • Developed an optimization framework considering a prior on models relating biomarkers to outcomes.
  • Evaluated the impact of threshold number and spacing on statistical power.
  • Applied the procedure to a clinical trial example in depression.

Main Results:

  • The proposed method identifies optimal thresholds and multiple testing procedures.
  • For considered scenarios, 3 to 4 thresholds yielded optimal power.
  • Optimizing threshold spacing significantly benefits power when a small subgroup with positive effects is suspected.

Conclusions:

  • The framework provides an effective approach for optimizing subgroup identification in targeted therapy development.
  • This method enhances the ability to detect treatment effects in specific patient populations.
  • The findings have implications for improving the design and analysis of clinical trials.