An Improved Misclassification Simulation Extrapolation (MC-SIMEX) Algorithm.

Misclassification Simulation-Extrapolation (MC-SIMEX) is an established method to correct for misclassification in binary covariates in a model. It involves the use of a simulation component which simulates pseudo-datasets with added degree of misclassification in the binary covariate and an extrapolation component which models the covariate's regression coefficients obtained at each level of misclassification using a quadratic function. This quadratic function is then used to extrapolate the covariate's regression coefficients to a point of "no error" in the classification of the binary covariate under question. However, extrapolation functions are not usually known accurately beforehand and are therefore only approximated versions. In this article, we propose an innovative method that uses the exact (not approximated) extrapolation function through the use of a derived relationship between the naïve regression coefficient estimates and the true coefficients in generalized linear models. Simulation studies are conducted to study and compare the numerical properties of the resulting estimator to the original MC-SIMEX estimator. Real data analysis using colon cancer data from the MSKCC cancer registry is also provided.