A randomized trial of ways to describe test accuracy: the effect on physicians' post-test probability estimates.
BACKGROUND
Some people believe that likelihood ratios provide diagnostic information that is more useful than sensitivity and specificity estimates.


OBJECTIVE
To assess how physicians' estimates about probability of illness are affected by the presentation of a diagnostic test's value as an estimate of sensitivity and specificity versus a likelihood ratio or an inexact numerical graphic.


METHODS
Random assignment of vignettes with different presentation formats of diagnostic test accuracy.


METHODS
Auditorium at a continuing medical education conference.


METHODS
183 physicians.


METHODS
After estimating probabilities of 6 common illnesses described in patient vignettes, physicians reviewed pertinent test results presented in 1 of 3 formats.


METHODS
Physicians' probability estimates of illness before and after receiving test information, and post-test probability estimates based on the Bayes theorem.


RESULTS
Absolute percentage point differences between the physicians' estimated and the Bayes-based post-test probabilities varied from -7 to 31, from -7 to 28, and from 1 to 29 for the sensitivity and specificity, likelihood ratio, and graphical groups, respectively. Mean differences of probability estimates between the sensitivity and specificity and the likelihood ratio groups were small for all vignettes (-2 to 3 percentage points; summary mean z value across the 6 vignettes, 0.04 [95% CI, -0.14 to 0.21]).


CONCLUSIONS
The small pool of participants (who were potentially selected) and the limited number of vignettes prevented a more detailed analysis of relationships between the interpreted strength of diagnostic evidence and estimations of illness probability.


CONCLUSIONS
These findings suggest that presenting diagnostic test accuracy with likelihood ratios does not affect some physicians' estimates of illness probability compared with presenting diagnostic test results as sensitivity and specificity.
Puhan
Milo A
MA
University of Zurich, Zurich, Switzerland. milo.puhan@evimed.ch
Steurer
Johann
J
Bachmann
Lucas M
LM
ter Riet
Gerben
G
D000328:Adult; D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D003933:Diagnosis; D019937:Diagnostic Techniques and Procedures / Q000592:standards*; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D008875:Middle Aged; D010820:Physicians / Q000592:standards*; D011237:Predictive Value of Tests