A general approach to analyzing epidemiology data that contain misclassification errors

M. A. Espeland, Siu Hui

Research output: Contribution to journalArticle

71 Citations (Scopus)

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
StatePublished - 1987
Externally publishedYes

Fingerprint

Misclassification Error
Epidemiology
Misclassification
Maximum likelihood estimation
epidemiology
Sampling
Log-linear Models
Discrete Data
Resampling
Maximum Likelihood Estimation
Population
Error Rate
Epidemiologic Studies
Linear Models
epidemiological studies
Methodology
data analysis
linear models
methodology
Demonstrate

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Applied Mathematics
  • Statistics and Probability
  • Public Health, Environmental and Occupational Health

Cite this

A general approach to analyzing epidemiology data that contain misclassification errors. / Espeland, M. A.; Hui, Siu.

In: Biometrics, Vol. 43, No. 4, 1987, p. 1001-1012.

Research output: Contribution to journalArticle

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