This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 223
Type: Topic Contributed
Date/Time: Monday, August 2, 2010 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #306473
Title: Fitting High-Dimensional Joint Models for Longitudinal and Time-to-Event Data Using Laplace Approximations
Author(s): Dimitris Rizopoulos*+
Companies: Erasmus University Medical Center
Address: PO Box 2040, Rotterdam, International, 3000 CA, Netherlands
Keywords: longitudinal Data ; survival data ; multiple outcomes ; Laplace approximation ; joint modeling
Abstract:

In longitudinal studies measurements are often collected for different types of outcomes for each subject. These may include several longitudinally measured responses (such as blood values relevant to the medical condition under study) and the time at which an event of particular interest occurs (e.g., death or development of a disease). In these settings answering the scientific questions of interest usually requires a joint modeling approach of the longitudinal outcomes with the time-to-event. In the joint modeling framework random effects are typically used to explain the interrelationships between the longitudinal and event time processes. However, estimation in the presence of random effects involves intractable integrals requiring therefore numerical integration. We propose a new computational approach for fitting such models based on the fully exponential Laplace approximation.


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