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A Bayesian Adjustment for Covariate Misclassification with Correlated Binary Outcome Data

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  • Dianxu Ren
  • Roslyn Stone

Abstract

Estimated associations between an outcome variable and misclassified covariates tend to be biased when the methods of estimation that ignore the classification error are applied. Available methods to account for misclassification often require the use of a validation sample (i.e. a gold standard). In practice, however, such a gold standard may be unavailable or impractical. We propose a Bayesian approach to adjust for misclassification in a binary covariate in the random effect logistic model when a gold standard is not available. This Markov Chain Monte Carlo (MCMC) approach uses two imperfect measures of a dichotomous exposure under the assumptions of conditional independence and non-differential misclassification. A simulated numerical example and a real clinical example are given to illustrate the proposed approach. Our results suggest that the estimated log odds of inpatient care and the corresponding standard deviation are much larger in our proposed method compared with the models ignoring misclassification. Ignoring misclassification produces downwardly biased estimates and underestimate uncertainty.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:japsta:v:34:y:2007:i:9:p:1019-1034
    DOI: 10.1080/02664760701591895
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    References listed on IDEAS

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    1. J. G. Booth & J. P. Hobert, 1999. "Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 265-285.
    2. Hyejin KO & Marie Davidian, 2000. "Correcting for Measurement Error in Individual-Level Covariates in Nonlinear Mixed Effects Models," Biometrics, The International Biometric Society, vol. 56(2), pages 368-375, June.
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    1. Beavers, Daniel P. & Stamey, James D., 2012. "Bayesian sample size determination for binary regression with a misclassified covariate and no gold standard," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2574-2582.
    2. 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.

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