Nonparametric Adjustment for Measurement Error in Time-to-Event Data

• Activity Number: 128
• Type: Contributed
• Date/Time: Monday, August 5, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Danielle Braun*+ and Malka Gorfine and Hormuzd Katki and Giovanni Parmigiani
• Companies: Harvard University and Technion - Israel Institute of Technology and National Cancer Institute and Dana-Farber Cancer Institute
• Keywords: Risk prediction ; Smoothed Kaplan-Meier ; Measurement error ; Survival data

Abstract

Measurement error in time to event data used as a predictor will lead to inaccurate predictions. This arises in the context of self-reported family history, a time to event covariate often measured with error, used in Mendelian risk prediction models. Using a validation data set, we propose a method to adjust for measurement error in this type of covariate. We estimate the measurement error process using a non- parametric 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 risk prediction models and multivariate survival, as well as illustrate our method using a data application for Mendelian risk prediction models. Results from 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 our adjusted method corrects for measurement error by improving both MSEP and calibration.