Bayesian Inference of Odds Ratios in Misclassified Binary Data with a Validation Substudy

Abstract

We propose a fully Bayesian model with a non-informative prior for analyzing misclassified binary data with a validation substudy. In addition, we derive a closed-form algorithm for drawing all parameters from the posterior distribution and making statistical inference on odds ratios. Our algorithm draws each parameter from a beta distribution, avoids the specification of initial values, and does not have convergence issues. We apply the algorithm to a data set and compare the results with those obtained by other methods. Finally, the performance of our algorithm is assessed using simulation studies.

Keywords:

• Bayesian inference
• Binary data
• Credible interval
• Misclassification
• Odds ratio

Mathematics Subject Classification:

• 62-07
• 62H12
• 62C10

Acknowledgment

The authors would like to acknowledge the assistance of the Biostatistics Shared Resource at the Harold C. Simmons Cancer Center, which is supported in part by an NCI Cancer Center Support Grant, 1P30 CA142543-01. In addition, the authors would also like to thank the Editor and two anonymous referees for their constructive comments, which helped to improve the presentation of this article.

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