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Activity Number: 593
Type: Topic Contributed
Date/Time: Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #309037
Title: Threshold-Dependent Proportional Hazards Model for Current Status Data with Biomarker Subject to Measurement Error
Author(s): Noorie Hyun*+ and Donglin Zeng and David Couper and James Pankow
Companies: The University of North Carolina and The University of North Carolina and The University of North Carolina and University of Minnesota
Keywords: Cox proportional hazard model ; Current status data ; Biomarker ; Threshold ; Measurement Error
Abstract:

In many medical studies, the time to a disease event is determined by the value of some biomarker crossing a specified threshold. Current status data arise when the biomarker value at a visit time is above or below the corresponding threshold. However, assuming a fixed threshold for all subjects may not be appropriate for some biomarkers. Medical researchers have showed that thresholds can vary across populations or from person to person. Furthermore, a biomarker is usually subject to measurement error. In the presence of these two challenging issues, existing methods for analyzing current status data are no longer applicable. In this paper, we propose a semiparametric method based on the Cox regression model depending on threshold values to account for measurement error in the biomarker. We estimate the model parameters using the nonparametric maximum likelihood approach and implement computation via the EM algorithm. We show consistency and semiparametric efficiency of the regression parameter estimator and estimate asymptotic variance. The method is illustrated through an application to data from a diabetes study.


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