The use of information graphs to evaluate and compare diagnostic tests.
OBJECTIVE
The purpose of this communication is to demonstrate the use of "information graphs" as a means of characterizing diagnostic test performance.


METHODS
Basic concepts in information theory allow us to quantify diagnostic uncertainty and diagnostic information. Given the probabilities of the diagnoses that can explain a patient's condition, the entropy of that distribution is a measure of our uncertainty about the diagnosis. The relative entropy of the posttest probabilities with respect to the pretest probabilities quantifies the amount of information gained by diagnostic testing. Mutual information is the expected value of relative entropy and, hence, provides a measure of expected diagnostic information. These concepts are used to derive formulas for calculating diagnostic information as a function of pretest probability for a given pair of test operating characteristics.


RESULTS
Plots of diagnostic information as a function of pretest probability are constructed to evaluate and compare the performance of three tests commonly used in the diagnosis of coronary artery disease. The graphs illustrate the critical role that the pretest probability plays in determining diagnostic test information.


CONCLUSIONS
Information graphs summarize diagnostic test performance and offer a way to evaluate and compare diagnostic tests.
Benish
W A
WA
Department of Internal Medicine, Case Western Reserve University, Cleveland, OH, USA. wab4@po.cwru.edu
D003198:Computer Simulation; D003327:Coronary Disease / Q000175:diagnosis; D003661:Decision Support Techniques; D019937:Diagnostic Techniques and Procedures / Q000706:statistics & numerical data*; D019277:Entropy; D006801:Humans; D007257:Information Theory; D012680:Sensitivity and Specificity; D013223:Statistics as Topic