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Multiplicity issues in exploratory subgroup analysis.

Ilya Lipkovich1, Alex Dmitrienko2, Christoph Muysers3

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Journal of Biopharmaceutical Statistics
|November 28, 2017
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Summary
This summary is machine-generated.

This study reviews methods for subgroup identification in clinical trials, emphasizing multiplicity adjustments to ensure reliability in personalized medicine. It highlights principled approaches like SIDES to address biases in exploratory subgroup analysis.

Keywords:
Clinical trialsmultiple testingpredictive biomarkerssubgroup identification

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Area of Science:

  • Clinical trial methodology
  • Biostatistics
  • Personalized medicine

Background:

  • Subgroup identification is crucial for developing tailored therapies and personalized medicine.
  • Exploratory subgroup analysis in clinical trials has faced criticism for unreliability.
  • Advances in machine learning offer principled approaches to subgroup identification.

Purpose of the Study:

  • To review multiplicity issues in exploratory subgroup analysis.
  • To illustrate multiplicity corrections using SIDES methods.
  • To present a case study of subgroup search in an oncology trial.

Main Methods:

  • Review of multiplicity issues in exploratory subgroup analysis.
  • Illustration of multiplicity corrections with SIDES (subgroup identification based on differential effect search) methods.
  • Application of subgroup search algorithms with resampling-based multiplicity adjustments in a Phase III oncology trial.

Main Results:

  • Principled subgroup search methods, incorporating multiplicity adjustments, enhance the reliability of exploratory analyses.
  • SIDES methods provide a framework for addressing selection bias in subgroup identification.
  • Resampling-based multiplicity adjustments are effective in subgroup search algorithms.

Conclusions:

  • Addressing multiplicity is essential for robust subgroup identification in clinical trials.
  • Principled subgroup search, like SIDES, improves the validity of personalized medicine approaches.
  • The presented methods and case study offer practical guidance for reliable subgroup analysis.