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A Bayesian Approach to Account for Misclassification in Prevalence and Trend Estimation

Author

Listed:
  • van Hasselt, Martijn

    (University of North Carolina at Greensboro, Department of Economics)

  • Bollinger, Christopher

    (University of Kentucky)

  • Bray, Jeremy

    (University of North Carolina at Greensboro, Department of Economics)

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 the prevalence of opioid misuse increases multiple times between 2002 and 2012.

Suggested Citation

  • van Hasselt, Martijn & Bollinger, Christopher & Bray, Jeremy, 2019. "A Bayesian Approach to Account for Misclassification in Prevalence and Trend Estimation," UNCG Economics Working Papers 19-13, University of North Carolina at Greensboro, Department of Economics.
    • Handle: RePEc:ris:uncgec:2019_013
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    More about this item

    Keywords

    Misclassification; partial identification; Bayesian estimation;
    All these keywords.

    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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