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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 #318665 View Presentation
Title: Semiparametric Models of Bivariate Times-to-Event Data with a Semi-Competing Risk
Author(s): Ran Liao* and Sujuan Gao
Companies: Indiana University and Indiana University
Keywords: Semiparametric ; survival analysis ; Semicompeting risk ; frailty model ; nonparametric ; Bivariate Times to Event Data

Survival analysis of time to events data often encounters the situations of correlated multiple events including the same type of event observed from siblings or multiple events experienced by the same individual. In addition, survival analysis in biomedical research can be further complicated by semi-competing risk when individuals at risk of a particular disease die from other causes. In this poster, we propose a frailty model based approach for bivariate survival outcomes with a semi-competing risk. Two estimation approaches are proposed and compared. The first is a two-stage semiparametric approach where the cumulative baseline hazard was estimated by a nonparametric method first and plugged in the likelihood function. Parameter estimation was then achieved by maximizing the pseudo-likelihood functions. In the second approach, we propose to use a pseudo partial likelihood approach for parameter estimation and inference similar to the concept in the Cox's partial likelihood. Simulation studies are conducted to compare the performances of these two approaches. The proposed model will be applied to data from a longitudinal study of an elderly population.

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

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