Bayesian estimation of cluster-level test accuracy based on different sampling schemes

Chun Lung Su, Ian Gardner, Wesley O. Johnson

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


We develop Bayesian models to estimate cluster-level test characteristics, sensitivity, specificity, prevalence, and predictive values, based on four different sampling schemes: a single test case and three sequential test cases. The corresponding cluster-level characteristics are calculated and compared for different sample sizes, sampling schemes, individual-level sensitivities, specificities, and cut-off values. We compared posterior estimates of individual-level and cluster-level characteristics for these four sampling schemes with simulated data. Two illustrations, one for Johne's disease in cattle and another for Salmonella in pig herds, are used to demonstrate application of the methods.

Original languageEnglish (US)
Pages (from-to)250-271
Number of pages22
JournalJournal of Agricultural, Biological, and Environmental Statistics
Issue number2
StatePublished - Jun 1 2007


  • Cluster test
  • Markov chain Monte Carlo
  • Prevalence
  • Sensitivity
  • Sequential test
  • Specificity

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Statistics, Probability and Uncertainty
  • Environmental Science(all)
  • Applied Mathematics
  • Statistics and Probability


Dive into the research topics of 'Bayesian estimation of cluster-level test accuracy based on different sampling schemes'. Together they form a unique fingerprint.

Cite this