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Activity Number: 33 - Cutting-Edge Statistical Methods for Modeling Disease Progression Processes
Type: Contributed
Date/Time: Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
Sponsor: International Chinese Statistical Association
Abstract #322795
Title: Recurrent Event Data with Measurement Error and Informative Censoring
Author(s): Yu-Jen Cheng* and Hsiang Yu and Ching-Yun Wang
Companies: National Tsing Hua University and National Tsing Hua University and Fred Hutchinson Cancer Research Center
Keywords: recurrent event ; measurement error ; informative censoring

Recurrent event data arise frequently in many longitudinal follow-up studies. Hence, evaluating covariate effects on the rates of occurrence of such events is commonly of interest. Examples include repeated hospitalizations, recurrent infections in HIV, and tumor recurrences. In this article, we consider semiparametric regression methods for the occurrence rate function of recurrent events when the covariates may be measured with errors. In contrast to the existing works, in our case the conventional assumption of independent censoring is violated since the recurrent event process is interrupted by some correlated events, which is called informative drop-out. Further, some covariates may be measured with errors. To accommodate both informative censoring and measurement error, the occurrence of recurrent events is modelled through an unspecified frailty distribution and accompanied with a classical measurement error model. The finite sample performances of proposed methods are examined via simulations and applied to a real data.

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

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