Measurement Error

I am interested in the application of multiple imputation for correcting for measurement error using calibration samples (Guo, Harel & Little 2010, Guo & Little 2011, Guo, Little & McConnell 2011). Classical calibration approaches have poor statistical properties, and multiple imputation provides a convenient way of providing users with good inferences without the need for complex statistical methods. Li, Taylor & Little (2011) presents a Bayesian shrinkage approach to the related issue of surrogate markers in clinical trials

References

Guo, Y. & Little, R.J. (2011). Regression Analysis Involving Covariates with Heteroscedastic Measurement Error. Statistics in Medicine, 30, 18, 2278–2294. DOI: 10.1002/sim.4261

Guo, Y., Little, R.J. and McConnell, D.S. (2011). On Using Summary Statistics from an External Calibration Sample to Correct for Covariate Measurement Error. To appear in Epidemiology.

Guo, Y., Harel, O. & Little, R.J. (2010). How Well Quantified is the Limit of Quantification? Epidemiology, 21, 4, S10-S16. DOI: 10.1097/EDE.0b013e3181d60e56

Li, Y., Taylor, J.M.G. & Little, R.J. (2011). A Shrinkage Approach for Estimating a Treatment Effect Using Surrogate Marker Data in Clinical Trials. Biometrics DOI: 10.1111/j.1541-0420.2011.01608.x

(revised December 8, 2011)