The correction of risk estimates for measurement error

The methods available for the correction of risk estimates for measurement errors are reviewed. The assumptions and design implications of each of the following six methods are noted: linear imputation, absolute limits, maximum likelihood, latent class, discriminant analysis and Gibbs sampling.

All methods, with the exception of the absolute limits approach, require either repeated determinations on the same subjects with use of the methods that are prone to error or a validation study, in which the measurement is performed for a number of persons with use of both the error-prone method and a more accurate method regarded as a "gold standard".

The maximum likelihood, latent class and absolute limits methods are most suitable for purely discrete risk factors. The linear imputation methods and the closely related discrimination analysis method are suitable for continuous risk factors which, together with the errors of measurement, are usually assumed to be normally distributed.

The Gibbs sampling approach is, in principle, useful for both discrete and continuous risk factors and measurement errors, although its use does mandate that the user specify models and dependencies that may be very complex. Also, the Bayesian approach implicit in the use of Gibbs sampling is difficult to apply to the design of the case-control study.