Bayesian Inference of Odds Ratios in Misclassified Binary Data with a Validation Substudy |
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Authors: | Dewi Rahardja Yan D Zhao Hao Helen Zhang |
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Institution: | 1. Department of Clinical Sciences and Simmons Cancer Center , UT Southwestern Medical Center , Dallas, Texas, USA rahardja@gmail.com;3. Department of Clinical Sciences and Simmons Cancer Center , UT Southwestern Medical Center , Dallas, Texas, USA;4. Department of Statistics , North Carolina State University , Raleigh, North Carolina, USA |
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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. |
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Keywords: | Bayesian inference Binary data Credible interval Misclassification Odds ratio |
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