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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Predictive Biomarker Graphical Approach (PRIME) for Precision Medicine.

Gina D'Angelo1, Xiaowen Tian1, Chuyu Deng2

  • 1Statistical Innovation, Gaithersburg, Maryland, USA.

Pharmaceutical Statistics
|April 29, 2026
PubMed
Summary
This summary is machine-generated.

Precision medicine uses biomarkers to guide drug development. A new graphical approach, PRIME, evaluates biomarkers continuously, identifying optimal cutoffs for patient enrichment and treatment selection.

Keywords:
PRIMEbiomarkerpatient enrichmentprecision medicinepredictive graphical approach

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Area of Science:

  • Biomarker discovery and validation
  • Translational medicine
  • Pharmacogenomics

Background:

  • Precision medicine relies on biomarkers for patient stratification in drug development.
  • Accurate biomarker cutoffs are crucial for identifying patients who will benefit from specific therapies.
  • Conventional methods for determining biomarker cutoffs often use p-values from dichotomized data.

Purpose of the Study:

  • To introduce PRIME, a novel predictive biomarker graphical approach for evaluating biomarkers on a continuous scale.
  • To enable the incorporation of clinical significance into biomarker evaluation.
  • To develop a method for identifying optimal biomarker cutoffs for patient enrichment.

Main Methods:

  • Adapted a treatment selection approach and extended it using G-computation to account for covariates.
  • Developed a model incorporating the interaction between a biomarker and treatment to predict risk.
  • Utilized graphical displays of predicted risk to delineate biomarker-outcome relationships and identify cutoffs.

Main Results:

  • The PRIME approach allows for continuous biomarker evaluation from a predicted risk perspective.
  • Graphical displays facilitate the identification of biomarker cutoffs for patient stratification.
  • PRIME incorporates features for comparing biomarkers (net gain) and assessing model fit (calibration).

Conclusions:

  • PRIME offers a robust graphical method for evaluating biomarkers and determining optimal cutoffs in a continuous manner.
  • The approach can accommodate various outcomes and covariates, enhancing its applicability in precision medicine.
  • An R package has been developed to facilitate the implementation and demonstration of the PRIME approach.