Hierarchical models for estimating herd prevalence and test accuracy in the absence of a gold standard

Timothy Hanson, Wesley O. Johnson, Ian Gardner

Research output: Contribution to journalArticle

59 Scopus citations

Abstract

A common assumption made in studies involving two or more binary diagnostic tests in the absence of a gold standard is one of conditional independence among tests given disease status. Although reasonable in some cases, often this assumption is untenable or untested and may lead to biased results. We propose a class of hierarchical models for the purpose of estimating the herd-level prevalence distribution and the accuracies of two tests in the absence of a gold standard when several exchangeable populations with differing disease prevalence are available for sampling, relaxing the assumption of conditional independence between tests. The models are used to estimate the prevalence of bovine brucellosis in Mexican cow herds and to estimate the error rates of two tests for the detection of swine pneumonia.

Original languageEnglish (US)
Pages (from-to)223-239
Number of pages17
JournalJournal of Agricultural, Biological, and Environmental Statistics
Volume8
Issue number2
DOIs
StatePublished - Jun 1 2003

Keywords

  • Sensitivity
  • Specificity

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'Hierarchical models for estimating herd prevalence and test accuracy in the absence of a gold standard'. Together they form a unique fingerprint.

  • Cite this