title: ROC analysis in biomarker combination with covariate adjustment.
abstract: OBJECTIVE
  Receiver operating characteristic (ROC) analysis is often used to find the optimal combination of biomarkers. When the subject level covariates affect the magnitude and/or accuracy of the biomarkers, the combination rule should take into account of the covariate adjustment. The authors propose two new biomarker combination methods that make use of the covariate information.
  
  
  METHODS
  The first method is to maximize the area under the covariate-adjusted ROC curve (AAUC). To overcome the limitations of the AAUC measure, the authors further proposed the area under covariate-standardized ROC curve (SAUC), which is an extension of the covariate-specific ROC curve. With a series of simulation studies, the proposed optimal AAUC and SAUC methods are compared with the optimal AUC method that ignores the covariates. The biomarker combination methods are illustrated by an example from Alzheimer's disease research.
  
  
  RESULTS
  The simulation results indicate that the optimal AAUC combination performs well in the current study population. The optimal SAUC method is flexible to choose any reference populations, and allows the results to be generalized to different populations.
  
  
  CONCLUSIONS
  The proposed optimal AAUC and SAUC approaches successfully address the covariate adjustment problem in estimating the optimal marker combination. The optimal SAUC method is preferred for practical use, because the biomarker combination rule can be easily evaluated for different population of interest.
authors.affiliation: Biostatistics and Bioinformatics Branch, Division of Epidemiology, Statistics & Prevention Research, NIH/NICHD, 6100 Executive Blvd., Bethesda, MD 20892, USA. danping.liu@nih.gov
authors.forename: Danping
authors.initials: D
authors.lastname: Liu
authors.forename: Xiao-Hua
authors.initials: XH
authors.lastname: Zhou
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000175:diagnosis*; D019540:Area Under Curve; D015415:Biomarkers; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D012372:ROC Curve
chemical_list: D015415:Biomarkers