JSM 2005 Online Program

Abstract #304697

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

To View the Program:
You may choose to view all activities of the program or just parts of it at any one time. All activities are arranged by date and time.

The views expressed here are those of the individual authors
and not necessarily those of the ASA or its board, officers, or staff.

The Program has labeled the meeting rooms with "letters" preceding the name of the room, designating in which facility the room is located:

Minneapolis Convention Center = “MCC” Hilton Minneapolis Hotel = “H” Hyatt Regency Minneapolis = “HY”

Back to main JSM 2005 Program page

Legend:

= Applied Session,

= Theme Session,

= Presenter

Activity Number:	225
Type:	Contributed
Date/Time:	Tuesday, August 9, 2005 : 8:30 AM to 10:20 AM
Sponsor:	Biometrics Section
Abstract - #304697
Title:	Bayesian Analysis of Recurrent Event Data with Informative Censoring
Author(s):	Nibedita Bandyopadhyay*+ and Ananda Sen
Companies:	GlaxoSmithKline and University of Michigan
Address:	One Franklin Plaza, Philadelphia, PA, 19101, United States
Keywords:	Counting process ; Frailty ; Gibbs sampling ; Joint model ; Survival analysis
Abstract:	Recurrent-event setting, where the subjects experience multiple occurrences of the event of interest, is encountered commonly in many biomedical applications. In analyzing recurrent event data, noninformative censoring is employed often for the implementation of statistical methods. However, when a terminating event such as death serves as part of the censoring mechanism, validity of the censoring assumption may be violated because recurrence could be a powerful risk factor for death. We consider joint modeling of recurrent event process and terminating event under a Bayesian framework in which a shared frailty is used to model the association between the intensity of the recurrent event process and the hazard of the terminating event. Comparisons are made to other existing models for recurrent event data. Our proposed model is implemented to data from a well known cancer study.

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2005 program