Statistical models for quantifying diagnostic accuracy with multiple lesions per patient.
We propose random-effects models to summarize and quantify the accuracy of the diagnosis of multiple lesions on a single image without assuming independence between lesions. The number of false-positive lesions was assumed to be distributed as a Poisson mixture, and the proportion of true-positive lesions was assumed to be distributed as a binomial mixture. We considered univariate and bivariate, both parametric and nonparametric mixture models. We applied our tools to simulated data and data of a study assessing diagnostic accuracy of virtual colonography with computed tomography in 200 patients suspected of having one or more polyps.
Zwinderman
Aeilko H
AH
Department of Clinical Epidemiology and Biostatistics, J2-203, Amsterdam Medical Center, University of Amsterdam, PO Box 22700, 1100 DE Amsterdam, The Netherlands. a.h.zwinderman@amc.uva.nl
Glas
Afina S
AS
Bossuyt
Patrick M
PM
Florie
Jasper
J
Bipat
Shandra
S
Stoker
Jaap
J
D003111:Colonic Polyps / Q000175:diagnosis*; D023881:Colonography, Computed Tomographic; D003113:Colonoscopy; D003951:Diagnostic Errors / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D012372:ROC Curve; D012680:Sensitivity and Specificity