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Activity Number: 592 - New Developments in Experiment Design and Statistical Modeling
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 2:00 PM to 3:50 PM
Sponsor: International Chinese Statistical Association
Abstract #329635 Presentation
Title: Methods for Multivariate Recurrent Event Data with Measurement Error and Informative Censoring
Author(s): Yu-Jen Cheng* and Ching-Yun Wang and Hsiang Yu
Companies: National Tsing Hua University and Fred Hutchinson Cancer Research Center and National Tsing Hua University
Keywords: informative censoring; measurement error; recurrent event data
Abstract:

In multivariate recurrent event data regression, observation of recurrent events is usually terminated by other events that are associated with the recurrent event processes, resulting in informative censoring. Additionally, some covariates could be measured with errors. In some applications, an instrumental variable is observed in a subsample, namely a calibration sample, which can be applied for bias correction. In this article, a shared frailty model is adopted to characterize the informative censoring and dependence among different types of recurrent events. To adjust for measurement errors, a nonparametric correction method using the calibration sample only is proposed. In the second approach, the information from the whole cohort is incorporated by the generalized method of moments. The proposed methods do not require the Poisson-type assumption for the multivariate recurrent event process and the distributional assumption for the frailty. Moreover, we do not need to impose any distributional assumption on the underlying covariates and measurement error. Both methods perform well, but the second approach improves efficiency. The proposed methods are applied to a real data.


Authors who are presenting talks have a * after their name.

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