JSM 2015 Preliminary Program

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Activity Number: 407
Type: Topic Contributed
Date/Time: Tuesday, August 11, 2015 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #316785 View Presentation
Title: Regression Analysis of Bivariate Failure Time Data
Author(s): Shanshan Zhao* and Ross Prentice
Companies: NIEHS and Fred Hutchinson Cancer Research Center
Keywords: bivariate survival data ; Cox model ; cross ration ; regression model
Abstract:

Although Cox proportional hazards model has been widely used for univariate failure time data analysis, a corresponding mature methodology for the regression analysis of multivariate failure time data has been slow to develop. In practice with rare diseases, we may not be able to observe the disease of interest in all study participants. However, we might observe other events during the study. If the observed events are related to the main study outcome, we may be able to use these information to fill in information about the main study outcome on those censored participants. For example, a study to predict time to stroke can benefit from information on time to coronary heart disease, given these two outcomes are related. In addition, we might be interested in understanding how time to different diseases are related to each other, and how disease onset times relate within families. Here we consider a modeling approach that combines a generalized version of Cox models for single failure hazard functions, with a corresponding regression model for the cross ratio function, toward establishing a unified approach to the regression analysis of bivariate failure time data.


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