Methods for evaluating the performance of diagnostic tests in the absence of a gold standard: A latent class model approach

Elizabeth S. Garrett, William W. Eaton, Scott Zeger

Research output: Contribution to journalArticle

Abstract

In many areas of medical research, 'gold standard' diagnostic tests do not exist and so evaluating the performance of standardized diagnostic criteria or algorithms is problematic. In this paper we propose an approach to evaluating the operating characteristics of diagnoses using a latent class model. By defining 'true disease' as our latent variable, we are able to estimate sensitivity, specificity and negative and positive predictive values of the diagnostic test. These methods are applied to diagnostic criteria for depression using Baltimore's Epidemiologic Catchment Area Study Wave 3 data.

Original languageEnglish (US)
Pages (from-to)1289-1307
Number of pages19
JournalStatistics in Medicine
Volume21
Issue number9
DOIs
StatePublished - May 15 2002

Keywords

  • Depression
  • Diagnostic criteria
  • Latent class models
  • Markov chain Monte Carlo
  • Operating characteristics
  • Validation

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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