Journal
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
Volume 110, Issue 510, Pages 681-696Publisher
AMER STATISTICAL ASSOC
DOI: 10.1080/01621459.2014.922777
Keywords
External validation study; Functional measurement error modeling; Generalized linear models; Likelihood method; Regression calibration; Semiparametric regression; Simulation extrapolation algorithm; Structural measurement error modeling
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Funding
- Natural Sciences and Engineering Research Council of Canada
- National Science Foundation [DMS-1000354, DMS-1206693]
- National Institute of Neurological Disorder and Stroke [R01-073671]
- NIH/NIEHS [R01 ES 09411]
- NIH/NCI [R01-CA050597]
- National Cancer Institute [R37-CA057030]
- King Abdullah University of Science and Technology (KAUST) [KUS-CI-016-04]
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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.
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