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Measurement error in a covariate or the outcome of regression models iscommon, but is often ignored, even though measurement error can lead tosubstantial bias in the estimated covariate-outcome association. While severaltexts on measurement error correction methods are available, these methodsremain seldomly applied. To improve the use of measurement error correctionmethodology, we developed mecor, an R package that implements measurement errorcorrection methods for regression models with continuous outcomes. Measurementerror correction requires information about the measurement error model and itsparameters. This information can be obtained from four types of studies, usedto estimate the parameters of the measurement error model: an internalvalidation study, a replicates study, a calibration study and an externalvalidation study. In the package mecor, regression calibration methods and amaximum likelihood method are implemented to correct for measurement error in acontinuous covariate in regression analyses. Additionally, methods of momentsmethods are implemented to correct for measurement error in the continuousoutcome in regression analyses. Variance estimation of the corrected estimatorsis provided in closed form and using the bootstrap.

mecor: An R package for measurement error correction in linear regression models with a continuous outcome