The performance of methods for correcting measurement error in case-control studies

It is generally agreed that adjustment for measurement error (when feasible) can substantially increase the validity of epidemiologic analyses. Although a broad variety of methods for measurement error correction has been developed, application in practice is rare. One reason may be that little is known about the robustness of these methods against violations of their restrictive assumptions.

We carried out a simulation study to assess the performance of two error correction methods (a regression calibration method and a semiparametric approach) as compared with standard analyses without measurement error correction in case-control studies with internal validation data. Performance was assessed over a wide range of model parameters including varying degrees of violations of assumptions.

In nearly all the settings assessed, the semiparametric estimate performed better than all alternatives under investigation. The regression calibration method is sensitive to violations of the assumptions of nondifferential error and small error variance.

The semiparametric method is a very robust method to correct for measurement error in case-control studies, but lack of functional software hinders widespread use. If the assumptions for the regression calibration method are fulfilled, application of this method, originally developed for cohort studies, in case-control studies may be a useful alternative that is easy to implement.