Selection and combination of biomarkers using ROC method for disease classification and prediction

Huazhen Lin, Ling Zhou, Heng PENG, Xiao Hua Zhou*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)

Abstract

Based on the SCAD penalty and the area under the ROC curve (AUC), we propose a new method for selecting and combining biomarkers for disease classification and prediction. The proposed estimator for the combination of the biomarkers has an oracle property; that is, the estimated combination of the biomarkers performs as well as it would have been if the biomarkers significantly associated with the outcome had been known in advance, in terms of discriminative power. The proposed estimator is computationally feasible, n1/2-consistent and asymptotically normal. Simulation studies show that the proposed method performs better than existing methods. We illustrate the proposed methodology in the acoustic startle response study.

Original languageEnglish
Pages (from-to)324-343
Number of pages20
JournalCanadian Journal of Statistics
Volume39
Issue number2
DOIs
Publication statusPublished - Jun 2011

Scopus Subject Areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • Generalized linear model
  • ROC curve
  • SCAD penalty
  • Selection and combination of biomarker

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