Adjusting for measurement error in explanatory variables

In epidemiology, measurement error or within-individual variation in exposures and confounders leads to attenuated effect estimates and inadequate control of confounding. Adjustment for measurement error is possible if its magnitude may be estimated from supplementary information, typically replicate measurements or partial observation of an error-free value. Methods currently available in Stata, including eivreg and ivreg, are of limited usefulness in epidemiology, and regression calibration is more commonly used. I will describe a new program, regcal, which implements regression calibration, and incorporates a modification for replicated discrete variables when the assumptions underlying regression calibration do not hold. This is illustrated using observational data on the association between cholesterol levels and green tea consumption.