Combining multiple markers for classification using ROC.
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, n(1/2)-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.
Ma
Shuangge
S
Division of Biostatistics, Yale University, New Haven, Connecticut 06520, USA. shuangge.ma@yale.edu
Huang
Jian
J
D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000032:analysis*; D001699:Biometry / Q000379:methods*; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D009363:Neoplasm Proteins / Q000032:analysis*; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity