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dc.contributor.authorKhudyakov, Polyna
dc.contributor.authorGorfine, Malka
dc.contributor.authorZucker, David
dc.contributor.authorSpiegelman, Donna
dc.date.accessioned2019-09-21T16:12:12Z
dc.date.issued2015
dc.identifier.citationKhudyakov, Polyna, Malka Gorfine, David Zucker, and Donna Spiegelman. 2015. “The Impact of Covariate Measurement Error on Risk Prediction.” Statistics in Medicine 34 (15): 2353–67. https://doi.org/10.1002/sim.6498.
dc.identifier.issn0277-6715
dc.identifier.issn1097-0258
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:41384765*
dc.description.abstractIn the development of risk prediction models, predictors are often measured with error. In this paper, we investigate the impact of covariate measurement error on risk prediction. We compare the prediction performance using a costly variable measured without error, along with error-free covariates, to that of a model based on an inexpensive surrogate along with the error-free covariates. We consider continuous error-prone covariates with homoscedastic and heteroscedastic errors, and also a discrete misclassified covariate. Prediction performance is evaluated by the area under the receiver operating characteristic curve (AUC), the Brier score (BS), and the ratio of the observed to the expected number of events (calibration). In an extensive numerical study, we show that (i) the prediction model with the error-prone covariate is very well calibrated, even when it is mis-specified; (ii) using the error-prone covariate instead of the true covariate can reduce the AUC and increase the BS dramatically; (iii) adding an auxiliary variable, which is correlated with the error-prone covariate but conditionally independent of the outcome given all covariates in the true model, can improve the AUC and BS substantially. We conclude that reducing measurement error in covariates will improve the ensuing risk prediction, unless the association between the error-free and error-prone covariates is very high. Finally, we demonstrate how a validation study can be used to assess the effect of mismeasured covariates on risk prediction. These concepts are illustrated in a breast cancer risk prediction model developed in the Nurses' Health Study.
dc.language.isoen_US
dc.publisherWiley
dash.licenseOAP
dc.titleThe Impact of Covariate Measurement Error on Risk Prediction
dc.typeJournal Article
dc.description.versionAccepted Manuscript
dc.relation.journalStatistics in Medicine
dash.depositing.authorSpiegelman, Donna::37eeac21962b33e4e46e7aedde542849::600
dc.date.available2019-09-21T16:12:12Z
dash.workflow.comments1Science Serial ID 100618
dc.identifier.doi10.1002/sim.6498
dash.source.volume34;15
dash.source.page2353


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