  Measurement error in a covariate or the outcome of regression models is
common, but is often ignored, even though measurement error can lead to
substantial bias in the estimated covariate-outcome association. While several
texts on measurement error correction methods are available, these methods
remain seldomly applied. To improve the use of measurement error correction
methodology, we developed mecor, an R package that implements measurement error
correction methods for regression models with continuous outcomes. Measurement
error correction requires information about the measurement error model and its
parameters. This information can be obtained from four types of studies, used
to estimate the parameters of the measurement error model: an internal
validation study, a replicates study, a calibration study and an external
validation study. In the package mecor, regression calibration methods and a
maximum likelihood method are implemented to correct for measurement error in a
continuous covariate in regression analyses. Additionally, methods of moments
methods are implemented to correct for measurement error in the continuous
outcome in regression analyses. Variance estimation of the corrected estimators
is provided in closed form and using the bootstrap.
