  In this review, we present an overview of the main aspects related to the
statistical evaluation of medical tests for diagnosis and prognosis. Measures
of diagnostic performance for binary tests, such as sensitivity, specificity,
and predictive values, are introduced, and extensions to the case of
continuous-outcome tests are detailed. Special focus is placed on the receiver
operating characteristic (ROC) curve and its estimation, with the topic of
covariate adjustment receiving a great deal of attention. The extension to the
case of time-dependent ROC curves for evaluating prognostic accuracy is also
touched upon. We apply several of the approaches described to a dataset derived
from a study aimed to evaluate the ability of HOMA-IR (homeostasis model
assessment of insulin resistance) levels to identify individuals at high
cardio-metabolic risk and how such discriminatory ability might be influenced
by age and gender. We also outline software available for the implementation of
the methods.
