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Bayesian sample size determination for binary regression with a misclassified covariate and no gold standard

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Author Info

  • Beavers, Daniel P.
  • Stamey, James D.

Abstract

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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File URL: http://www.sciencedirect.com/science/article/pii/S0167947312000965
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Bibliographic Info

Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 56 (2012)
Issue (Month): 8 ()
Pages: 2574-2582

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Handle: RePEc:eee:csdana:v:56:y:2012:i:8:p:2574-2582

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Web page: http://www.elsevier.com/locate/csda

Related research

Keywords: Logistic regression; Misclassification; Bias; Sample size; Covariate;

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