Performance of bias-correction methods for exposure measurement error using repeated measurements with and without missing data
Article first published online: 25 JUN 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 31, Issue 28, pages 3467–3480, 10 December 2012
How to Cite
Batistatou, E. and McNamee, R. (2012), Performance of bias-correction methods for exposure measurement error using repeated measurements with and without missing data. Statist. Med., 31: 3467–3480. doi: 10.1002/sim.5422
- Issue published online: 23 NOV 2012
- Article first published online: 25 JUN 2012
- Manuscript Accepted: 24 MAR 2012
- Manuscript Received: 2 NOV 2010
- exposure measurement error;
- regression calibration;
- grouping approach;
- multiple imputation
It is known that measurement error leads to bias in assessing exposure effects, which can however, be corrected if independent replicates are available. For expensive replicates, two-stage (2S) studies that produce data ‘missing by design’, may be preferred over a single-stage (1S) study, because in the second stage, measurement of replicates is restricted to a sample of first-stage subjects. Motivated by an occupational study on the acute effect of carbon black exposure on respiratory morbidity, we compare the performance of several bias-correction methods for both designs in a simulation study: an instrumental variable method (EVROS IV) based on grouping strategies, which had been recommended especially when measurement error is large, the regression calibration and the simulation extrapolation methods. For the 2S design, either the problem of ‘missing’ data was ignored or the ‘missing’ data were imputed using multiple imputations. Both in 1S and 2S designs, in the case of small or moderate measurement error, regression calibration was shown to be the preferred approach in terms of root mean square error. For 2S designs, regression calibration as implemented by Stata software is not recommended in contrast to our implementation of this method; the ‘problematic’ implementation of regression calibration although substantially improved with use of multiple imputations. The EVROS IV method, under a good/fairly good grouping, outperforms the regression calibration approach in both design scenarios when exposure mismeasurement is severe. Both in 1S and 2S designs with moderate or large measurement error, simulation extrapolation severely failed to correct for bias. Copyright © 2012 John Wiley & Sons, Ltd.