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Nonparametric Biomarker Based Treatment Selection With Reproducibility Data.

Sara Byers1, Xiao Song1

  • 1Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, Georgia, USA.

Statistics in Medicine
|September 18, 2024
PubMed
Summary
This summary is machine-generated.

Biomarker assay modification can impact treatment selection. This study introduces a nonparametric approach for optimal biomarker evaluation and treatment selection, even with measurement errors between platforms.

Keywords:
SIMEXassay modificationbiomarkermeasurement errorplatform migration

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Area of Science:

  • Biostatistics
  • Genomics
  • Translational Medicine

Background:

  • Evaluating biomarkers for cancer treatment selection is crucial.
  • Assay modification, such as migrating gene expression data from Affymetrix to Illumina platforms, presents challenges.
  • Existing methods for biomarker migration may rely on assumptions that do not hold in practice, potentially leading to suboptimal treatment decisions.

Purpose of the Study:

  • To develop and evaluate a robust method for assessing biomarkers under assay modification.
  • To ensure optimal treatment selection despite potential measurement errors introduced during biomarker platform migration.
  • To address limitations of classical measurement error models in biomarker evaluation.

Main Methods:

  • Utilized nonparametric logistic regression to model the relationship between event rates and biomarkers.
  • Assumed a nonparametric relationship between original and migrated biomarkers.
  • Employed B-spline approximation for estimation.
  • Validated the approach through simulation studies and application to lung cancer data.

Main Results:

  • Nonparametric modeling provides optimal marker-based treatment selection.
  • Error-contaminated biomarkers, compared to error-free ones, lead to suboptimal treatment selection.
  • The proposed method effectively handles deviations from classical measurement error models.

Conclusions:

  • The developed nonparametric approach offers a more accurate and optimal strategy for biomarker-based treatment selection following assay modification.
  • This method enhances the reliability of biomarker migration and reduces the risk of suboptimal clinical decisions.
  • The findings are applicable to lung cancer and potentially other diseases requiring biomarker-guided therapy.