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Related Experiment Video

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Predictive biomarkers for treatment selection: statistical considerations.

James J Chen1,2, Tzu-Pin Lu3, Yu-Chuan Chen1

  • 1Division of Bioinformatics & Biostatistics, National Center for Toxicological Research, US Food & Drug Administration, Jefferson, AR 72079, USA.

Biomarkers in Medicine
|October 29, 2015
PubMed
Summary
This summary is machine-generated.

This review explores statistical methods for developing predictive biomarkers to identify patients who will benefit from specific treatments. It covers biomarker identification, subgroup selection, and clinical utility assessment for efficient clinical studies.

Keywords:
biomarker adaptive designpersonalized and precision medicinepredictive biomarkerpredictive classifiersubgroup analysissubgroup selection

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Genomic Medicine

Background:

  • Predictive biomarkers are crucial for personalized medicine, enabling treatment selection for optimal patient outcomes.
  • Developing effective predictive biomarkers enhances clinical study efficiency by focusing on likely responders.

Purpose of the Study:

  • To review and discuss statistical methods for developing predictive biomarkers for treatment selection.
  • To address key statistical challenges in biomarker identification, subgroup selection, and clinical utility assessment.

Main Methods:

  • The review outlines a three-component statistical procedure: biomarker identification, subgroup selection, and clinical utility assessment.
  • It discusses various statistical issues, including biomarker designs, identification procedures, classification models, subgroup analysis, and multiple testing.

Main Results:

  • The development of predictive biomarkers involves distinct statistical steps, each with specific challenges.
  • Efficiently identifying and validating biomarkers requires careful consideration of study design and analytical methods.

Conclusions:

  • Robust statistical methodologies are essential for the successful development and implementation of predictive biomarkers in clinical practice.
  • Addressing statistical issues in biomarker development can improve the precision and efficiency of clinical trials for targeted therapies.