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Activity Number: 496
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #319034 View Presentation
Title: Semiparametric Regression Analysis of Recurrent Gap Times in the Presence of Competing Risks
Author(s): Chia-Hui Huang* and Yi-Hau Chen
Companies: National Taipei University and Academia Sinica
Keywords: Competing risks ; Martingale processes ; Mixture model ; Multiple events ; Recurrent data

When a disease progression is assumed to go through several stages marked by a nonterminal, recurrent event such as relapse, or a terminal event such as death, whose occurrence terminates the progression, researchers might be concerned with the duration or gap times between successive events (stages) and would like to study the covariates effects on the gap times. In addition, how the previous event or gap times affect the current gap time may be also of interest. We propose a unifying framework for joint regression analysis of gap times between successive events. The proposed mixture modeling framework consists of a logistic regression for predicting the path of transition (to a nonterminal or terminal event) at each stage, and proportional hazards models for predicting the gap times for transition to the nonterminal and terminal events at each stage, and both the two components of models are conditional on the past event history and stage-specific covariates. As special cases, when the number of stages is fixed at one or two, the proposed framework can be applied to analysis of conventional competing risks or semicompeting risks data.

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

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