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

Qihuang Zhang, Grace Y. Yi
2019 Figshare  
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 et al. [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
more » ... tes 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 R package, augSIMEX, for public use. Simulation studies are conducted to illustrate the use of the algorithm. This package is available at CRAN.
doi:10.6084/m9.figshare.8152493.v1 fatcat:rwpsdz44lfbvlnb3jwshnohcyy