Abstract ROC analysis occupies an increasingly important role in technology assessment.[1] ROC curves allow one to compare a set of ordinal estimates over the entire range of estimates. Sources of such estimates may include subjective probabilities, mathematical prediction models and empiric prediction models (like the APGAR score). The area under the ROC curve measures the ability of the estimation method to discriminate between two states (usually disease and non-disease). This paper discusses how one constructs ROC curves, what the area under the curve means, and how and why one compares two ROC curves. The computer program (ROC ANALYZER) allows easy performance of these analyses on MS-DOS compatible machines.