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Bayesian Analysis and Design for Joint Modeling of Two Binary Responses With Misclassification

Author

Listed:
  • James D. Stamey
  • Daniel P. Beavers
  • Michael E. Sherr

Abstract

Survey data are often subject to various types of errors such as misclassification. In this article, we consider a model where interest is simultaneously in two correlated response variables and one is potentially subject to misclassification. A motivating example of a recent study of the impact of a sexual education course for adolescents is considered. A simulation-based sample size determination scheme is applied to illustrate the impact of misclassification on power and bias for the parameters of interest.

Suggested Citation

  • James D. Stamey & Daniel P. Beavers & Michael E. Sherr, 2017. "Bayesian Analysis and Design for Joint Modeling of Two Binary Responses With Misclassification," Sociological Methods & Research, , vol. 46(4), pages 772-792, November.
    • Handle: RePEc:sae:somere:v:46:y:2017:i:4:p:772-792
    DOI: 10.1177/0049124115605340
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    References listed on IDEAS

    as
    1. Dianxu Ren & Roslyn Stone, 2007. "A Bayesian Adjustment for Covariate Misclassification with Correlated Binary Outcome Data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(9), pages 1019-1034.
    2. John M. Neuhaus, 2002. "Analysis of Clustered and Longitudinal Binary Data Subject to Response Misclassification," Biometrics, The International Biometric Society, vol. 58(3), pages 675-683, September.
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