Bayesian correction for covariate measurement error: A frequentist evaluation and comparison with regression calibration

Jonathan W Bartlett, Ruth H Keogh

Research output: Contribution to journalArticlepeer-review

21 Citations (SciVal)


Bayesian approaches for handling covariate measurement error are well established and yet arguably are still relatively little used by researchers. For some this is likely due to unfamiliarity or disagreement with the Bayesian inferential paradigm. For others a contributory factor is the inability of standard statistical packages to perform such Bayesian analyses. In this paper, we first give an overview of the Bayesian approach to handling covariate measurement error, and contrast it with regression calibration, arguably the most commonly adopted approach. We then argue why the Bayesian approach has a number of statistical advantages compared to regression calibration and demonstrate that implementing the Bayesian approach is usually quite feasible for the analyst. Next, we describe the closely related maximum likelihood and multiple imputation approaches and explain why we believe the Bayesian approach to generally be preferable. We then empirically compare the frequentist properties of regression calibration and the Bayesian approach through simulation studies. The flexibility of the Bayesian approach to handle both measurement error and missing data is then illustrated through an analysis of data from the Third National Health and Nutrition Examination Survey.

Original languageEnglish
Article number27(6)
Pages (from-to)1695-1708
Number of pages14
JournalStatistical Methods in Medical Research
Issue number6
Early online date28 Sept 2016
Publication statusPublished - 1 Jun 2018


  • Bayesian inference
  • Measurement error
  • multiple imputation
  • regression calibration

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management


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