Receiver-operating characteristic (ROC) curve analysis in diagnostic, prognostic and predictive biomarker research

Abstract

From a clinical perspective, biomarkers may have a variety of functions, which correspond to different stages in the disease development, e.g. in the progression of cancer. Biomarkers can assist in the care of patients for screening, diagnosis, prognosis, prediction and surveillance. Fundamental for the use of biomarkers in all situations is biomarker accuracy – the ability to correctly classify one condition and/or outcome from another. Receiver-operating characteristic (ROC) curve analysis is a useful tool in assessment of biomarker accuracy. Its advantages include testing accuracy across the entire range of scores and thereby not requiring a predetermined cut-off point, in addition to easily examined visual and statistical comparisons across tests or scores, and, finally, independence from outcome prevalence. Further, ROC curve analysis is a useful tool for evaluating the accuracy of a statistical model that classifies subjects into one of two categories. Diagnostic models are different from predictive and prognostic models in that the latter incorporate time-to-event analysis, for which censored data may pose a weakness of the model, or the reference standard. However, with the appropriate use of ROC curves, investigators of biomarkers can improve their research and presentation of results. ROC curves help identify the most appropriate classification rules. ROC curves avoid confounding resulting from varying thresholds with subjective ratings. The ROC curve results should always be put in perspective, because a good classifier does not guarantee the eventual clinical outcome, in particular for time-dependant events in screening, prediction, and/or prognosis studies where particular statistical precautions and methods are needed.