We review the principles and practical application of receiver-operating characteristic (ROC) analysis for diagnostic tests. ROC analysis can be used for diagnostic tests with outcomes measured on ordinal, interval or ratio scales. The dependence of the diagnostic sensitivity and specificity on the selected cut-off value must be considered for a full test evaluation and for test comparison. All possible combinations of sensitivity and specificity that can be achieved by changing the test's cut-off value can be summarised using a single parameter; the area under the ROC curve. The ROC technique can also be used to optimise cut-off values with regard to a given prevalence in the target population and cost ratio of false-positive and false-negative results. However, plots of optimisation parameters against the selected cut-off value provide a more-direct method for cut-off selection. Candidates for such optimisation parameters are linear combinations of sensitivity and specificity (with weights selected to reflect the decision-making situation), odds ratio, chance-corrected measures of association (e. g. kappa) and likelihood ratios. We discuss some recent developments in ROC analysis, including meta-analysis of diagnostic tests, correlated ROC curves (paired-sample design) and chance- and prevalence-corrected ROC curves.