IDEAS home Printed from https://ideas.repec.org/p/ris/uncgec/2019_013.html

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

Author

Listed:
  • Martijn van Hasselt

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

  • Christopher Bollinger

    (University of Kentucky)

  • Jeremy Bray

    (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

  • Martijn van Hasselt & Christopher Bollinger & Jeremy Bray, 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
    as

    Download full text from publisher

    File URL: https://bryan.uncg.edu/19-13-a-bayesian-approach-to-account-for-misclassification-in-prevalence-and-trend-estimation/
    File Function: Full text
    Download Restriction: no
    ---><---

    Other versions of this item:

    More about this item

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ris:uncgec:2019_013. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Albert Link (email available below). General contact details of provider: https://edirc.repec.org/data/edncgus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.