A simulation study of measurement error correction methods in logistic regression

Measurement error models in logistic regression have received considerable theoretical interest over the past 10-15 years. In this paper, we present t he results of a simulation study that compares four estimation methods: the so-called regression calibration method, probit maximum likelihood as an a pproximation to the logistic maximum likelihood, the exact maximum likeliho od method based on a logistic model, and the naive estimator, which is the result of simply ignoring the fact that some of the explanatory variables a re measured with error. We have compared the behavior of these methods in a simple, additive measurement error model. We show that, in this situation, the regression calibration method is a very good alternative to more mathe matically sophisticated methods.