Combining multiple markers for classification using ROC

Shuangge Ma, Jian Huang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

66 Citations (Scopus)


In biomedical studies, it is of great interest to develop methodologies for combining multiple markers for the purpose of disease classification. The receiving operating characteristic (ROC) technique has been widely used, where classification performance can be measured with the area under the ROC curve (AUC). In this article, we study a ROC-based method for effectively combining multiple markers for disease classification. We propose a sigmoid AUC (SAUC) estimator that maximizes the sigmoid approximation of the empirical AUC. The SAUC estimator is computationally affordable, n1/2-consistent and achieves the same asymptotic efficiency as the AUC estimator. Inference based on the weighted bootstrap is investigated. We also propose Monte Carlo methods to assess the overall prediction performance and the relative importance of individual markers. Finite sample performance is evaluated using simulation studies and two public data sets.
Original languageEnglish
Pages (from-to)751-757
Number of pages7
Issue number3
Publication statusPublished - 1 Sept 2007
Externally publishedYes


  • Area under the curve
  • Classification
  • ROC
  • Sigmoid approximation

ASJC Scopus subject areas

  • Statistics and Probability
  • Medicine(all)
  • Immunology and Microbiology(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics


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