Estimation of diagnostic-test sensitivity and specificity through Bayesian modeling.
We review recent Bayesian approaches to estimation (based on cross-sectional sampling designs) of the sensitivity and specificity of one or more diagnostic tests. Our primary goal is to provide veterinary researchers with a concise presentation of the computational aspects involved in using the Bayesian framework for test evaluation. We consider estimation of diagnostic-test sensitivity and specificity in the following settings: (i) one test in one population, (ii) two conditionally independent tests in two or more populations, (iii) two correlated tests in two or more populations, and (iv) three tests in two or more populations, where two tests are correlated but jointly independent of the third test. For each scenario, we describe a Bayesian model that incorporates parameters of interest. The WinBUGS code used to fit each model, which is available at http://www.epi.ucdavis.edu/diagnos-tictests/, can be altered readily to conform to different data.
Branscum
A J
AJ
Department of Statistics, University of California, One Shields Ave, Davis, CA 95616, USA.
Gardner
I A
IA
Johnson
W O
WO
D000818:Animals; D001499:Bayes Theorem; D002417:Cattle; D002418:Cattle Diseases / Q000175:diagnosis / Q000469:parasitology; D006691:Classical Swine Fever / Q000175:diagnosis; D003048:Coccidiosis / Q000175:diagnosis / Q000662:veterinary; D003430:Cross-Sectional Studies; D003955:Diagnostic Tests, Routine / Q000379:methods / Q000592:standards / Q000662:veterinary*; D005393:Fish Diseases / Q000175:diagnosis / Q000469:parasitology; D016881:Microsporidiosis / Q000175:diagnosis / Q000662:veterinary; D008954:Models, Biological; D017686:Oncorhynchus mykiss; D012680:Sensitivity and Specificity; D013552:Swine; D013553:Swine Diseases / Q000175:diagnosis / Q000469:parasitology; D014124:Toxoplasmosis, Animal / Q000175:diagnosis