  Background: Measurement errors in terms of quantification or classification
frequently 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 well
described, simple straight forward general analytic solutions are not readily
available for statistical analysis and measurement error is frequently not
acknowledged or accounted for. Generally, to account for measurement error
requires some data where we can observe the variables once with and once
without error, to establish the relationship between the two. Methods: Here we
describe a general method accounting for measurement error in outcome and/or
predictor variables for the parametric regression setting when there is a
validation subsample where variables are measured once with and once without
error. The method does not describe and thus does not depend on the particular
relation between the variables measured with and without error, and is
generally robust to the type of measurement error, for example nondifferential,
differential or Berkson errors. Results: Simulation studies show how the method
reduces bias compared to models based upon variables measured with error alone
and reduces variances compared to models based upon the variables measured
without error in the validation subsample alone. Conclusion: The proposed
estimator has favorable properties in terms of bias and variance, is easily
derived empirically, and is robust to different types of measurement error.
This method should be a valuable tool in the analysis of data with measurement
error.
