Mecor: An R package for measurement error correction in linear regression models with a continuous outcome. (September 2021)

• Record Type: Journal Article
• Authors: Nab, Linda; van Smeden, Maarten; Keogh, Ruth H.; Groenwold, Rolf H.H.
• Is Part Of: Computer methods and programs in biomedicine. Volume 208(2021)
• Publication Date: 2021-09
• Subjects: Measurement error correction -- Regression calibration -- Method of moments -- Maximum likelihood -- R; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106238
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The R package mecor accommodates measurement error correction in linear regression models with a continuous outcome. mecor implements measurement error correction by means of regression calibration, maximum likelihood estimation and method of moments. mecor implements measurement error correction methods for four different validation data structures: internal, replicates, calibration and external data. When no additional validation data is available, a framework for conducting sensitivity analyses is provided. 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 a continuous outcome. 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.