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

TL;DR

The paper introduces 'mecor', an R package that implements various measurement error correction methods for regression models with continuous outcomes, aiming to reduce bias caused by measurement errors.

Contribution

It provides a comprehensive R package that applies measurement error correction techniques for both covariates and outcomes in regression models, facilitating broader application.

Findings

Implemented regression calibration and maximum likelihood methods.

Included methods of moments for outcome measurement error correction.

Provided variance estimation via closed form and bootstrap.

Abstract

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.