Abstract
No single biomarker for cancer is considered adequately sensitive and specific for cancer screening. It is expected that the results of multiple markers will need to be combined in order to yield adequately accurate classification. Typically the objective function that is optimized for combining markers is the likelihood function. In this paper we consider an alternative objective function -- the area under the empirical receiver operating characteristic curve (AUC). We note that it yields consistent estimates of parameters in a generalized linear model for the risk score but does not require specifying the link function. Like logistic regression it yields consistent estimation with case-control or cohort data. Simulation studies suggest that AUC-based classification scores have performance comparable with logistic likelihood based scores when the logistic regression model holds. Analysis of data from a proteomics biomarker study shows that performance can be far superior to logistic regression derived scores when the logistic regression model does not hold. Model fitting by maximizing the AUC rather than the likelihood should be considered when the goal is to derive a marker combination score for classification or prediction.
Disciplines
Clinical Epidemiology | Epidemiology | Multivariate Analysis | Statistical Models
Suggested Citation
Pepe, Margaret S.; Cai, Tianxi; Zhang, Zheng; and Longton, Gary M., "Combining Predictors for Classification using the Area Under the ROC Curve" (January 2005). UW Biostatistics Working Paper Series. Working Paper 238.
https://biostats.bepress.com/uwbiostat/paper238
Included in
Clinical Epidemiology Commons, Epidemiology Commons, Multivariate Analysis Commons, Statistical Models Commons