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 (RC), arguably
the most commonly adopted approach. We then argue why the Bayesian approach has
a number of statistical advantages compared to RC, 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 RC 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
