Bayesian sample size determination for binary regression with a misclassified covariate and no gold standard
Covariate misclassification is a common problem in epidemiology, genetics, and other biomedical areas. Because this form of misclassification is known to bias estimators, accounting for it at the design stage is of high importance. In this paper, we extend on previous work applied to response misclassification by developing a Bayesian approach to sample size determination for a covariate misclassification model with no gold standard. Our procedure considers both conditionally independent tests and tests in which dependence exists between classifiers. We specifically consider a Bayesian power criterion for the sample size determination scheme, and we demonstrate the improvement in model power for our dual classifier approach compared to a naïve single classifier approach.
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Volume (Year): 56 (2012)
Issue (Month): 8 ()
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- Hironori Fujisawa & Shizue Izumi, 2000. "Inference about Misclassification Probabilities from Repeated Binary Responses," Biometrics, The International Biometric Society, vol. 56(3), pages 706-711, 09.
- 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.
- Satoshi Morita & Peter F. Thall & Peter Müller, 2008. "Determining the Effective Sample Size of a Parametric Prior," Biometrics, The International Biometric Society, vol. 64(2), pages 595-602, 06.
- van Wieringen, Wessel N., 2005. "On identifiability of certain latent class models," Statistics & Probability Letters, Elsevier, vol. 75(3), pages 211-218, December.
- Nandini Dendukuri & Elham Rahme & Patrick Bélisle & Lawrence Joseph, 2004. "Bayesian Sample Size Determination for Prevalence and Diagnostic Test Studies in the Absence of a Gold Standard Test," Biometrics, The International Biometric Society, vol. 60(2), pages 388-397, 06.
- Nandini Dendukuri & Lawrence Joseph, 2001. "Bayesian Approaches to Modeling the Conditional Dependence Between Multiple Diagnostic Tests," Biometrics, The International Biometric Society, vol. 57(1), pages 158-167, 03.
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