A correction method is proposed for models including the generalized linear model when the covariate is measured with error. The method requires a separate validation data set that consists of the surrogateWand the true covariateXor an unbiased estimateX⊃ofX.We do not require the classical additive measurement error model in which the surrogate is unbiased for the true covariates. We first obtain an estimate ofE(X|W) by using nonparametric kernel regression ofXorX⊃onWbased on the validation data. Then we perform a standard analysis with the unknownXreplaced by the estimate ofE(X|W). The asymptotic distribution of the resulting regression parameter estimator is obtained. Generalizations to include components ofXmeasured without error are also discussed.