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Updated: Jul 10, 2026
07:35
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Integrating the predictiveness of a marker with its performance as a classifier
Margaret S Pepe1, Ziding Feng, Ying Huang
1Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA. mspepe@u.washington.edu
American Journal of Epidemiology
|November 6, 2007
Summary
This study introduces a new predictiveness curve to evaluate risk markers and models. Integrating this curve with classification performance offers a comprehensive assessment, resolving contradictions between statistical approaches.
Area of Science:
- Biostatistics
- Biomarker Discovery
- Medical Informatics
Background:
- Two statistical approaches, risk modeling and classification performance, are used for biomarker evaluation.
- Controversy exists regarding the appropriateness of each method, and they can yield conflicting results.
- Existing methods like the receiver operating characteristic (ROC) curve lack essential risk information.
Purpose of the Study:
- To introduce a novel graphic, the predictiveness curve, to complement risk modeling.
- To assess the population-level usefulness of risk models.
- To propose an integrated approach for comprehensive biomarker assessment.
Main Methods:
- Development of the predictiveness curve graphic.
- Application of the predictiveness curve alongside classification performance measures.
- Demonstration using prostate-specific antigen (PSA) data from the Prostate Cancer Prevention Trial.
Main Results:
- The predictiveness curve assesses risk model utility in a population.
- It displays essential risk information absent in ROC curves.
- An integrated plot of predictiveness and classification performance provides cohesive biomarker assessment.
Conclusions:
- The predictiveness curve enhances risk modeling by showing population utility.
- Combining predictiveness and classification performance offers a complete biomarker evaluation.
- This integrated approach resolves contradictions between statistical methods.
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