A general approach to analyzing epidemiology data that contain misclassification errors

M. A. Espeland, S. L. Hui

Research output: Contribution to journalArticle

72 Scopus citations

Abstract

Misclassification is a common source of bias and reduced efficiency in the analysis of discrete data. Several methods have been proposed to adjust for misclassification using formation on errors rates (i) gathered by resampling the study population, (ii) gathered by sampling a separate population, or (iii) assumed a priori. We present unified methods for incorporating these types of information into analyses based on log-linear models and maximum likelihood estimation. General variance expressions are developed. Examples from epidemiologic studies are used to demonstrate the proposed methodology.

Original languageEnglish (US)
Pages (from-to)1001-1012
Number of pages12
JournalBiometrics
Volume43
Issue number4
DOIs
StatePublished - Jan 1 1987
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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