Estimation of diagnostic-test sensitivity and specificity through Bayesian modeling

Prev Vet Med. 2005 May 10;68(2-4):145-63. doi: 10.1016/j.prevetmed.2004.12.005.


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, can be altered readily to conform to different data.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Animals
  • Bayes Theorem*
  • Cattle
  • Cattle Diseases / diagnosis
  • Cattle Diseases / parasitology
  • Classical Swine Fever / diagnosis
  • Coccidiosis / diagnosis
  • Coccidiosis / veterinary
  • Cross-Sectional Studies
  • Diagnostic Tests, Routine / methods
  • Diagnostic Tests, Routine / standards
  • Diagnostic Tests, Routine / veterinary*
  • Fish Diseases / diagnosis
  • Fish Diseases / parasitology
  • Microsporidiosis / diagnosis
  • Microsporidiosis / veterinary
  • Models, Biological*
  • Oncorhynchus mykiss
  • Sensitivity and Specificity
  • Swine
  • Swine Diseases / diagnosis
  • Swine Diseases / parasitology
  • Toxoplasmosis, Animal / diagnosis