A comparison of regression calibration, moment reconstruction and imputation for adjusting for covariate measurement error in regression
- 4 August 2008
- journal article
- research article
- Published by Wiley in Statistics in Medicine
- Vol. 27 (25), 5195-5216
- https://doi.org/10.1002/sim.3361
Abstract
Regression calibration (RC) is a popular method for estimating regression coefficients when one or more continuous explanatory variables, X, are measured with an error. In this method, the mismeasured covariate, W, is substituted by the expectation E(X|W), based on the assumption that the error in the measurement of X is non‐differential. Using simulations, we compare three versions of RC with two other ‘substitution’ methods, moment reconstruction (MR) and imputation (IM), neither of which rely on the non‐differential error assumption. We investigate studies that have an internal calibration sub‐study. For RC, we consider (i) the usual version of RC, (ii) RC applied only to the ‘marker’ information in the calibration study, and (iii) an ‘efficient’ version (ERC) in which the estimators (i) and (ii) are combined. Our results show that ERC is preferable when there is non‐differential measurement error. Under this condition, there are cases where ERC is less efficient than MR or IM, but they rarely occur in epidemiology. We show that the efficiency gain of usual RC and ERC over the other methods can sometimes be dramatic. The usual version of RC carries similar efficiency gains to ERC over MR and IM, but becomes unstable as measurement error becomes large, leading to bias and poor precision. When differential measurement error does pertain, then MR and IM have considerably less bias than RC, but can have much larger variance. We demonstrate our findings with an analysis of dietary fat intake and mortality in a large cohort study. Copyright © 2008 John Wiley & Sons, Ltd.Keywords
This publication has 15 references indexed in Scilit:
- Performance of a food-frequency questionnaire in the US NIH–AARP (National Institutes of Health–American Association of Retired Persons) Diet and Health StudyPublic Health Nutrition, 2008
- Multiple-imputation for measurement-error correctionInternational Journal of Epidemiology, 2006
- Adjusting for covariate errors with nonparametric assessment of the true covariate distributionBiometrika, 2004
- A New Method for Dealing with Measurement Error in Explanatory Variables of Regression ModelsBiometrics, 2004
- Structure of Dietary Measurement Error: Results of the OPEN Biomarker StudyAmerican Journal of Epidemiology, 2003
- Design and Serendipity in Establishing a Large Cohort with Wide Dietary Intake DistributionsAmerican Journal of Epidemiology, 2001
- Modeling Earnings Measurement Error: A Multiple Imputation ApproachThe Review of Economics and Statistics, 1996
- Regression With Missing X's: A ReviewJournal of the American Statistical Association, 1992
- Approximate Quasi-likelihood Estimation in Models with Surrogate PredictorsJournal of the American Statistical Association, 1990
- Improvements of the naive approach to estimation in nonlinear errors-in-variables regression modelsPublished by American Mathematical Society (AMS) ,1990