From diagnostic accuracy to accurate diagnosis: interpreting a test result with confidence.
BACKGROUND
The Standard for Reporting of Diagnostic Accuracy statement promotes the reporting of confidence intervals (CIs) for indices of diagnostic test accuracy. However, these indices must be combined with an estimate of pretest probability to properly interpret the results of such tests, thus yielding positive and negative predictive values. For small sample sizes, CI estimation for predictive values based on the classical logit transformation has been found to be very conservative. A method based on computer simulation has therefore been suggested as an alternative.


METHODS
ACI procedure for predictive values that yields limits completely contained in those provided by the logit transformation is proposed and evaluated.


RESULTS
The proposed approach to CI construction maintains nominal coverage very well even when sample sizes are small.


CONCLUSIONS
Accurate CIs for positive and negative predictive values can be obtained without using computer simulation.
Zou
Guangyong
G
Robarts Research Institute and the Department of Epidemiology and Biostatistics, University of Western Ontario, London, Ontario, Canada. gzou@robarts.ca
D001499:Bayes Theorem; D002170:Canada; D016001:Confidence Intervals; D003933:Diagnosis; D006801:Humans; D012680:Sensitivity and Specificity