TY - JOUR
T1 - Nonparametric Adjustment for Measurement Error in Time-to-Event Data
T2 - Application to Risk Prediction Models
AU - Braun, Danielle
AU - Gorfine, Malka
AU - Katki, Hormuzd A.
AU - Ziogas, Argyrios
AU - Parmigiani, Giovanni
N1 - Publisher Copyright:
© 2018 American Statistical Association.
PY - 2018/1/2
Y1 - 2018/1/2
N2 - Mismeasured time-to-event data used as a predictor in risk prediction models will lead to inaccurate predictions. This arises in the context of self-reported family history, a time-to-event predictor often measured with error, used in Mendelian risk prediction models. Using validation data, we propose a method to adjust for this type of error. We estimate the measurement error process using a nonparametric smoothed Kaplan–Meier estimator, and use Monte Carlo integration to implement the adjustment. We apply our method to simulated data in the context of both Mendelian and multivariate survival prediction models. Simulations are evaluated using measures of mean squared error of prediction (MSEP), area under the response operating characteristics curve (ROC-AUC), and the ratio of observed to expected number of events. These results show that our method mitigates the effects of measurement error mainly by improving calibration and total accuracy. We illustrate our method in the context of Mendelian risk prediction models focusing on misreporting of breast cancer, fitting the measurement error model on data from the University of California at Irvine, and applying our method to counselees from the Cancer Genetics Network. We show that our method improves overall calibration, especially in low risk deciles. Supplementary materials for this article are available online.
AB - Mismeasured time-to-event data used as a predictor in risk prediction models will lead to inaccurate predictions. This arises in the context of self-reported family history, a time-to-event predictor often measured with error, used in Mendelian risk prediction models. Using validation data, we propose a method to adjust for this type of error. We estimate the measurement error process using a nonparametric smoothed Kaplan–Meier estimator, and use Monte Carlo integration to implement the adjustment. We apply our method to simulated data in the context of both Mendelian and multivariate survival prediction models. Simulations are evaluated using measures of mean squared error of prediction (MSEP), area under the response operating characteristics curve (ROC-AUC), and the ratio of observed to expected number of events. These results show that our method mitigates the effects of measurement error mainly by improving calibration and total accuracy. We illustrate our method in the context of Mendelian risk prediction models focusing on misreporting of breast cancer, fitting the measurement error model on data from the University of California at Irvine, and applying our method to counselees from the Cancer Genetics Network. We show that our method improves overall calibration, especially in low risk deciles. Supplementary materials for this article are available online.
KW - Carrier status prediction
KW - Family history
KW - Mismeasured covariates
KW - Smoothed Kaplan–Meier estimator
KW - Survival analysis
UR - http://www.scopus.com/inward/record.url?scp=85047332195&partnerID=8YFLogxK
U2 - 10.1080/01621459.2017.1311261
DO - 10.1080/01621459.2017.1311261
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AN - SCOPUS:85047332195
SN - 0162-1459
VL - 113
SP - 14
EP - 25
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
IS - 521
ER -