The impact of covariate measurement error on risk prediction

Polyna Khudyakov, Malka Gorfine, David Zucker, Donna Spiegelman

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

In 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.

Original languageEnglish
Pages (from-to)2353-2367
Number of pages15
JournalStatistics in Medicine
Volume34
Issue number15
DOIs
StatePublished - 10 Jul 2015
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2015 John Wiley & Sons, Ltd.

Funding

FundersFunder number
National Cancer InstituteP01CA053996

    Keywords

    • Brier score
    • Logistic regression
    • Measurement error
    • Probit regression
    • ROC-AUC
    • Risk prediction

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