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

Autor:	Martijn van Hasselt, Christopher R. Bollinger, Jeremy W. Bray
Rok vydání:	2021
Předmět:	Economics and Econometrics Variable (computer science) Bayes estimator Dummy variable Bayesian probability Statistics Posterior probability Prior probability Range (statistics) Trend estimation Social Sciences (miscellaneous) Mathematics
Zdroj:	Journal of Applied Econometrics. 37:351-367
ISSN:	1099-1255 0883-7252
DOI:	10.1002/jae.2879
Popis:	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.
Databáze:	OpenAIRE
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