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Related Experiment Video

Updated: Jul 11, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Combining multiple markers for classification using ROC.

Shuangge Ma¹, Jian Huang

¹Division of Biostatistics, Yale University, New Haven, Connecticut 06520, USA. shuangge.ma@yale.edu

|September 11, 2007

Summary

This study introduces a new Sigmoid Area Under the ROC Curve (SAUC) estimator for combining multiple disease markers. This method offers efficient and affordable disease classification performance assessment.

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Area of Science:

Biostatistics
Medical Informatics
Machine Learning in Healthcare

Background:

Accurate disease classification using multiple biomarkers is crucial in biomedical research.
The Receiving Operating Characteristic (ROC) curve and its Area Under the Curve (AUC) are standard metrics for evaluating binary classification performance.
Existing methods for combining markers may lack computational efficiency or optimal performance.

Purpose of the Study:

To develop and evaluate a novel ROC-based method for effectively combining multiple markers for disease classification.
To propose a computationally affordable and statistically efficient Sigmoid AUC (SAUC) estimator.
To introduce methods for assessing overall prediction performance and individual marker importance.

Main Methods:

Development of the Sigmoid AUC (SAUC) estimator, which approximates the empirical AUC using a sigmoid function.
Investigation of inference procedures using weighted bootstrap methods.
Application of Monte Carlo simulations to evaluate finite sample performance and marker importance.

Main Results:

The proposed SAUC estimator is computationally affordable and n(1/2)-consistent.
The SAUC estimator achieves the same asymptotic efficiency as traditional AUC estimators.
Simulation studies and analysis of public datasets demonstrate the method's effectiveness.

Conclusions:

The SAUC estimator provides an efficient and practical approach for combining multiple markers in disease classification.
The proposed methodology enhances the assessment of classification performance and marker relevance.
This work contributes to improved diagnostic and prognostic tools in biomedical studies.