Measurement Error Correction in Linear Models with a Continuous Outcome

Covariate measurement error correction is implemented by means of regression calibration by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331), efficient regression calibration by Spiegelman D, Carroll RJ & Kipnis V (2001) and maximum likelihood estimation by Bartlett JW, Stavola DBL & Frost C (2009) . Outcome measurement error correction is implemented by means of the method of moments by Buonaccorsi JP (2010, ISBN:1420066560) and efficient method of moments by Keogh RH, Carroll RJ, Tooze JA, Kirkpatrick SI & Freedman LS (2014) . Standard error estimation of the corrected estimators is implemented by means of the Delta method by Rosner B, Spiegelman D & Willett WC (1990) and Rosner B, Spiegelman D & Willett WC (1992) , the Fieller method described by Buonaccorsi JP (2010, ISBN:1420066560), and the Bootstrap by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331).

The mecor Package

This package for R implements measurement error correction methods for measurement error in a continuous covariate or outcome in a linear model with a continuous outcome.

Installation

The package can be installed via

devtools::install_github("LindaNab/mecor", build_vignettes = TRUE)

Quick demo

library(mecor)
# load the internal covariate validation study
data("vat", package = "mecor")
head(vat)
# correct the biased exposure-outcome association
mecor(ir_ln ~ MeasError(substitute = wc, reference = vat) + age + sex + tbf, data = vat, method = "standard")

More examples

Browse the vignettes of the package for more information.

browseVignettes(package = "mecor")

References

Key reference

• Nab L, van Smeden M, Keogh RH, Groenwold RHH. mecor: an R package for measurement error correction in linear models with a continuous outcome. 2021:208:106238. doi:10.1016/j.cmpb.2021.106238

References to methods implemented in the package

• Bartlett JW, Stavola DBL, Frost C. Linear mixed models for replication data to efficiently allow for covariate measurement error. Statistics in Medicine. 2009:28(25):3158–3178. doi:10.1002/sim.3713

• Buonaccorsi JP. Measurement error: Models, methods, and applications. 2010. Chapman & Hall/CRC, Boca Raton.

• Carroll RJ, Ruppert D, Stefanski LA, Crainiceanu CM. Measurement error in non-linear models: A modern perspective. 2006, 2nd edition. Chapman & Hall/CRC, Boca Raton.

• Keogh RH, Carroll RJ, Tooze JA, Kirkpatrick SI, Freedman LS. Statistical issues related to dietary intake as the response variable in intervention trials. Statistics in Medicine. 2016:35(25):4493–4508. doi:10.1002/sim.7011

• Keogh RH, White IR. A toolkit for measurement error correction, with a focus on nutritional epidemiology. Statistics in Medicine 2014:33(12):2137–2155. doi:10.1002/sim.6095

• Nab L, Groenwold RHH, Welsing PMJ, van Smeden M. Measurement error in continuous endpoints in randomised trials: Problems and solutions. Statistics in Medicine. 2019:38(27):5182-5196. doi:10.1002/sim.8359

• Rosner B, Spiegelman D, Willett WC. Correction of logistic regression relative risk estimates and confidence intervals for measurement error: The case of multiple covariates measured with error. 1990:132(4):734-745. doi:10.1093/oxfordjournals.aje.a115715

• Rosner B, Spiegelman D, Willett WC. Correction of logistic regression relative risk estimates and confidence intervals for random within-person measurement error. American Journal of Epidemiology. 1992:136(11):1400-1413. doi:10.1093/oxfordjournals.aje.a116453

• Spiegelman D, Carroll RJ, Kipnis V. Efficient regression calibration for logistic regression in main study/internal validation study designs with an imperfect reference instrument. Statistics in Medicine. 2001:20(1):139-160. doi:10.1002/1097-0258(20010115)20:1<139::AID-SIM644>3.0.CO;2-K