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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Optimal two-stage enrichment design correcting for biomarker misclassification.

Yong Zang1, Beibei Guo2

  • 11 Department of Mathematical Sciences, Florida Atlantic University, Boca Raton, FL, USA.

Statistical Methods in Medical Research
|November 29, 2015
PubMed
Summary

This study introduces an optimal two-stage enrichment design for clinical trials using a surrogate marker to correct biomarker misclassification. This method enhances the accuracy of evaluating molecularly targeted agents in personalized medicine.

Keywords:
Biomarkerclinical trialenrichment designmeasurement erroroptimal designpersonalized medicine

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Pharmacogenomics

Background:

  • Enrichment designs are crucial for evaluating molecularly targeted agents (MTAs) in personalized medicine.
  • Biomarker misclassification in enrichment designs can introduce significant bias and affect treatment evaluation.
  • Accurate patient stratification is essential for the integrity of clinical trials.

Purpose of the Study:

  • To propose a two-stage optimal enrichment design to address biomarker misclassification.
  • To maximize the accuracy of patient biomarker status classification using surrogate marker information.
  • To develop methods for correcting bias introduced by biomarker misclassification.

Main Methods:

  • Utilizing a surrogate marker within a two-stage enrichment design.
  • Analytical derivation of bias caused by biomarker misclassification.
  • Developing a likelihood ratio test with the EM algorithm for bias correction.

Main Results:

  • The proposed design is optimal, maximizing correct patient classification.
  • The EM algorithm-based likelihood ratio test effectively corrects for bias.
  • Simulation studies confirm the desirable performance and accuracy of the proposed design.

Conclusions:

  • The novel two-stage enrichment design effectively corrects biomarker misclassification.
  • The developed statistical methods improve the reliability of treatment effect evaluation for MTAs.
  • This approach enhances the precision and integrity of personalized medicine clinical trials.