Description

Measuring the accuracy of diagnostic tests is crucial in many application areas in particular medicine and health care. Good methods for determining diagnostic accuracy provide useful guidance on selection of patient treatment according to the severity of their health status. The receiver operating characteristic (ROC) curve, for example, has proven to be a useful tool to assess the ability of a diagnostic test to discriminate among different groups.

There are many interesting topics you can choose from, to name a few;

• Combination and pooling of Biomarkers
• Bayesian ROC methods
• Multireader ROC analysis
• ROC analysis to compare machine learning models for biomedicine
• Free-response ROC analysis
• Convex hull ROC curves
• Meta-analysis and ROC curves
• ROC hyper-surface

Prerequisites

• Statistical Concepts II (essential)
• Statistical Methods III (recommended)
• Familiarity with the statistical software R (recommended)

References

• Zhou, X.H. and Obuchowski, N.A. and McClish, D.K. Statistical Methods in Diagnostic Medicine. Wiley, New York, 2002.
• Pepe, M.S. The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press, Oxford, 2003.
• Zou, Liu, Bandos, Ohno-Machado, Rockette. Statistical Evaluation of Diagnostic Performance: Topics in ROC Analysis. Chapman and Hall/CRC, 2001. 
• Krzanowski, W.J.  and Hand, D.J.  ROC Curves for Continuous Data Chapman and Hall/CRC, 2009. 
• Nakas, C. T. Developments in roc surface analysis and assessment of diagnostic markers in three-class classification problems. REVSTAT Statistical Journal, 12, 43–65, 2014.