A Bayesian approach to account for misclassification in prevalence and trend estimation

Martijn van Hasselt, Christopher R. Bollinger, Jeremy W. Bray

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


In this paper, we present a Bayesian approach to estimate the mean of a binary variable and changes in the mean over time, when the variable is subject to misclassification error. These parameters are partially identified, and we derive identified sets under various assumptions about the misclassification rates. We apply our method to estimating the prevalence and trend of prescription opioid misuse, using data from the 2002–2014 National Survey on Drug Use and Health. Using a range of priors, the posterior distribution provides evidence that among middle-aged White men, the prevalence of opioid misuse increased multiple times between 2002 and 2012.

Original languageEnglish
Pages (from-to)351-367
Number of pages17
JournalJournal of Applied Econometrics
Issue number2
StatePublished - Mar 2022

Bibliographical note

Funding Information:
We thank John Pepper and Gary Koop for helpful comments on an earlier version of this paper.

Publisher Copyright:
© 2021 John Wiley & Sons, Ltd.


  • Bayesian estimation
  • Misclassication
  • partial identication

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Economics and Econometrics


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