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

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

Abstract

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.

Suggested Citation

  • Martijn van Hasselt & Christopher R. Bollinger & Jeremy W. Bray, 2022. "A Bayesian approach to account for misclassification in prevalence and trend estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 351-367, March.
    • Handle: RePEc:wly:japmet:v:37:y:2022:i:2:p:351-367
    DOI: 10.1002/jae.2879
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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior

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