R package for analysis of data with mixed measurement error and misclassification in covariates: augSIMEX

Measurement error and misclassification arise commonly in various data collection processes. It is well-known that ignoring these features in the data analysis usually leads to biased inference. With the generalized linear model setting, Yi etal. [Functional and structural methods with mixed measurement error and misclassification in covariates. J Am Stat Assoc. 2015;110:681-696] developed inference methods to adjust for the effects of measurement error in continuous covariates and misclassification in discrete covariates simultaneously for the scenario where validation data are available. The augmented simulation-extrapolation (SIMEX) approach they developed generalizes the usual SIMEX method which is only applicable to handle continuous error-prone covariates. To implement this method, we develop an package, augSIMEX, for public use. Simulation studies are conducted to illustrate the use of the algorithm. This package is available at CRAN.