Bayesian misclassification and propensity score methods for clustered observational studies

Qi Zhou, Yoo-Mi Chin (), James Stamey and Joon Jin Song

Journal of Applied Statistics, 2018, vol. 45, issue 9, 1547-1560

Abstract: Bayesian propensity score regression analysis with misclassified binary responses is proposed to analyse clustered observational data. This approach utilizes multilevel models and corrects for misclassification in the responses. Using the deviance information criterion (DIC), the performance of the approach is compared with approaches without correcting for misclassification, multilevel structure specification, or both in the study of the impact of female employment on the likelihood of physical violence. The smallest DIC confirms that our proposed model best fits the data. We conclude that female employment has an insignificant impact on the likelihood of physical spousal violence towards women. In addition, a simulation study confirms that the proposed approach performed best in terms of bias and coverage rate. Ignoring misclassification in response or multilevel structure of data would yield biased estimation of the exposure effect.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:45:y:2018:i:9:p:1547-1560

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DOI: 10.1080/02664763.2017.1380786

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