title: Cohen's kappa for capturing discrimination.
abstract: BACKGROUND
  Identification of cut-off values for key biomarkers of clinical risk is a useful clinical tool. The kappa coefficient is a popular descriptive statistical measure for summarising the cross classification of two nominal variables with identical classes. On the basis of this definition, I propose that the kappa coefficient can also be used to capture discrimination, in the same way that the receiver operating characteristic (ROC) curve is used in preventive epidemiology studies.
  
  
  METHODS
  The statistics were determined using Cohen's kappa statistics for a gold standard and a continuous biomarker. The proposed design is compared with the ROC curve by applying it to articles on the metabolic syndrome and a colon cancer clinical trial.
  
  
  RESULTS
  The two methods gave similar results. Moreover, Monte Carlo simulation results confirm that, from a power perspective, the proposed method is to be preferred. In general, the proposed method has higher power than the area under the ROC curve (AUC) for a study of positively correlative design.
  
  
  CONCLUSIONS
  Overall, the power performance of the proposed method is better that that of the AUC.
authors.affiliation: Department of Nursing, Chang Gung University of Science and Technology, Chiayi Campus, Chiayi, Taiwan 61363.
authors.forename: Chia-Hao
authors.initials: CH
authors.lastname: Chang
mesh_terms: D019540:Area Under Curve; D015415:Biomarkers; D003082:Colectomy / Q000401:mortality*; D003110:Colonic Neoplasms / Q000175:diagnosis*; D003627:Data Interpretation, Statistical; D006801:Humans; D024821:Metabolic Syndrome / Q000175:diagnosis*; D009010:Monte Carlo Method; D012016:Reference Values
chemical_list: D015415:Biomarkers
keywords: Area under the ROC curve; Cohen's kappa; Cut-off value; Power; Receiver operating characteristic curve