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Parametric Bayesian analysis of case-control data with imprecise exposure measurements

Author

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  • Gustafson, Paul
  • Le, Nhu D.
  • Vallée, Marc

Abstract

Case-control data with imprecise exposure measurements can be analyzed via Bayesian fitting of a retrospective discriminant analysis model. The parameters of interest are the regression coefficients in the prospective log-odds ratio for disease. Under a standard noninformative prior, the posterior means of these parameters are infinite. Posterior medians, however, perform reasonably relative to other estimators that adjust for covariate imprecision. The Bayesian inference can be implemented with direct posterior simulation, so the analysis is not complicated by convergence and dependence issues associated with Markov chain Monte Carlo methods.

Suggested Citation

  • Gustafson, Paul & Le, Nhu D. & Vallée, Marc, 2000. "Parametric Bayesian analysis of case-control data with imprecise exposure measurements," Statistics & Probability Letters, Elsevier, vol. 47(4), pages 357-363, May.
    • Handle: RePEc:eee:stapro:v:47:y:2000:i:4:p:357-363
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    References listed on IDEAS

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    1. Peter Muller & Giovanni Parmigiani & Joellen Schildkraut & Luca Tardella, 1999. "A Bayesian Hierarchical Approach for Combining Case-Control and Prospective Studies," Biometrics, The International Biometric Society, vol. 55(3), pages 858-866, September.
    2. Raymond J. Carroll & Kathryn Roeder & Larry Wasserman, 1999. "Flexible Parametric Measurement Error Models," Biometrics, The International Biometric Society, vol. 55(1), pages 44-54, March.
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