Covariate measurement imprecision or errors arise frequently in many areas. It is well known that ignoring such errors can substantially degrade the quality of inference or even yield erroneous results. Although in practice both covariates subject to measurement error and covariates subject to misclassification can occur, research attention in the literature has mainly focused on addressing either one of these problems separately. To fill this gap, we develop estimation and inference methods that accommodate both characteristics simultaneously. Specifically, we consider measurement error and misclassification in generalized linear models under the scenario that an external validation study is available, and systematically develop a number of effective functional and structural methods. Our methods can be applied to different situations to meet various objectives.
Yi, G. Y., Ma, Y., Spiegelman, D., & Carroll, R. J. (2015). Functional and Structural Methods With Mixed Measurement Error and Misclassification in Covariates. Journal of the American Statistical Association, 110(510), 681–696. doi:10.1080/01621459.2014.922777
The authors thank the review team for their helpful comments. Yi's research was supported by a grant from the Natural Sciences and Engineering Research Council of Canada. Ma's research was supported by grants from the National Science Foundation (DMS-1000354, DMS-1206693) and the National Institute of Neurological Disorder and Stroke (R01-073671). Spiegelman's research was supported by grants from NIH/NIEHS (R01 ES 09411) and NIH/NCI (R01-CA050597). Carroll's research was supported by grants from the National Cancer Institute (R37-CA057030), the National Institute of Neurological Disorder and Stroke (R01-073671) and by Award Number KUS-CI-016-04, made by King Abdullah University of Science and Technology (KAUST).