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A Bayesian Approach to Account for Misclassification and Overdispersion in Count Data

Author

Listed:
  • Wenqi Wu

    (Department of Statistical Science, Baylor University, One Bear Place #97140, Waco, TX, 76706, USA)

  • James Stamey

    (Department of Statistical Science, Baylor University, One Bear Place #97140, Waco, TX, 76706, USA)

  • David Kahle

    (Department of Statistical Science, Baylor University, One Bear Place #97140, Waco, TX, 76706, USA)

Abstract

Count data are subject to considerable sources of what is often referred to as non-sampling error. Errors such as misclassification, measurement error and unmeasured confounding can lead to substantially biased estimators. It is strongly recommended that epidemiologists not only acknowledge these sorts of errors in data, but incorporate sensitivity analyses into part of the total data analysis. We extend previous work on Poisson regression models that allow for misclassification by thoroughly discussing the basis for the models and allowing for extra-Poisson variability in the form of random effects. Via simulation we show the improvements in inference that are brought about by accounting for both the misclassification and the overdispersion.

Suggested Citation

  • Wenqi Wu & James Stamey & David Kahle, 2015. "A Bayesian Approach to Account for Misclassification and Overdispersion in Count Data," IJERPH, MDPI, vol. 12(9), pages 1-14, August.
    • Handle: RePEc:gam:jijerp:v:12:y:2015:i:9:p:10648-10661:d:54931
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    References listed on IDEAS

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    1. Stuart Batterman & Janet Burke & Vlad Isakov & Toby Lewis & Bhramar Mukherjee & Thomas Robins, 2014. "A Comparison of Exposure Metrics for Traffic-Related Air Pollutants: Application to Epidemiology Studies in Detroit, Michigan," IJERPH, MDPI, vol. 11(9), pages 1-25, September.
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    3. Paulino, Carlos Daniel & Silva, Giovani & Alberto Achcar, Jorge, 2005. "Bayesian analysis of correlated misclassified binary data," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1120-1131, June.
    4. Helmut Küchenhoff & Samuel M. Mwalili & Emmanuel Lesaffre, 2006. "A General Method for Dealing with Misclassification in Regression: The Misclassification SIMEX," Biometrics, The International Biometric Society, vol. 62(1), pages 85-96, March.
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