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Bayesian sensitivity analysis to unmeasured confounding for misclassified data

Author

Listed:
  • Qi Zhou

    (Xi’an Jiaotong University)

  • Yoo-Mi Chin

    (Baylor University)

  • James D. Stamey

    (Baylor University)

  • Joon Jin Song

    (Baylor University)

Abstract

Bayesian sensitivity analysis of unmeasured confounding is proposed for observational data with misclassified outcome. The approach simultaneously corrects bias from error in the outcome and examines possible change in the exposure effect estimation assuming the presence of a binary unmeasured confounder. We assess the influence of unmeasured confounding on the exposure effect estimation through two sensitivity parameters that characterize the associations of the unmeasured confounder with the exposure status and with the outcome variable. The proposed approach is illustrated in the study of the effect of female employment status on the likelihood of domestic violence. An extensive simulation study is conducted to confirm the efficacy of the proposed approach. The simulation results indicate accounting for misclassification in outcome and unmeasured confounding significantly reduce the bias in exposure effect estimation and improve the coverage probability of credible intervals.

Suggested Citation

  • Qi Zhou & Yoo-Mi Chin & James D. Stamey & Joon Jin Song, 2020. "Bayesian sensitivity analysis to unmeasured confounding for misclassified data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(4), pages 577-596, December.
    • Handle: RePEc:spr:alstar:v:104:y:2020:i:4:d:10.1007_s10182-019-00357-1
    DOI: 10.1007/s10182-019-00357-1
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    References listed on IDEAS

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    1. Sander Greenland, 2005. "Multiple‐bias modelling for analysis of observational data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(2), pages 267-306, March.
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    3. Greenland S., 2003. "The Impact of Prior Distributions for Uncontrolled Confounding and Response Bias: A Case Study of the Relation of Wire Codes and Magnetic Fields to Childhood Leukemia," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 47-54, January.
    4. Carlos Daniel Paulino & Paulo Soares & John Neuhaus, 2003. "Binomial Regression with Misclassification," Biometrics, The International Biometric Society, vol. 59(3), pages 670-675, September.
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