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Background: Measurement errors in terms of quantification or classificationfrequently occur in epidemiologic data and can strongly impact inference.Measurement errors may occur when ascertaining, recording or extracting data.Although the effects of measurement errors can be severe and are welldescribed, simple straight forward general analytic solutions are not readilyavailable for statistical analysis and measurement error is frequently notacknowledged or accounted for. Generally, to account for measurement errorrequires some data where we can observe the variables once with and oncewithout error, to establish the relationship between the two. Methods: Here wedescribe a general method accounting for measurement error in outcome and/orpredictor variables for the parametric regression setting when there is avalidation subsample where variables are measured once with and once withouterror. The method does not describe and thus does not depend on the particularrelation between the variables measured with and without error, and isgenerally robust to the type of measurement error, for example nondifferential,differential or Berkson errors. Results: Simulation studies show how the methodreduces bias compared to models based upon variables measured with error aloneand reduces variances compared to models based upon the variables measuredwithout error in the validation subsample alone. Conclusion: The proposedestimator has favorable properties in terms of bias and variance, is easilyderived empirically, and is robust to different types of measurement error.This method should be a valuable tool in the analysis of data with measurementerror.

A general, simple, robust method to account for measurement error when analyzing data with an internal validation subsample