JSM 2005 Online Program

Abstract #302697

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.

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Activity Number:	213
Type:	Invited
Date/Time:	Tuesday, August 9, 2005 : 8:30 AM to 10:20 AM
Sponsor:	WNAR
Abstract - #302697
Title:	Informative Priors, Sensitivity Analysis, and the Role of Bayesian Inference for Handling Dropout
Author(s):	Joseph Hogan*+ and Joo Yeon Lee
Companies:	Brown University and Brown University
Address:	Center for Statistical Sciences, Comm Health Dept, Providence, RI, 02912, USA
Keywords:	pattern mixture models ; selection models ; informative dropout ; non-ignorable missing data ; smoking cessation ; longitudinal binary responses
Abstract:	It is well known that full-data distributions cannot be identified from incomplete data without the benefit of unverifiable assumptions such as a parametric distribution for the full data or assumptions about the missing data mechanism. This talk will focus on the formulation of full-data models that separate the observed and missing data distributions and on the role played by informative priors for drawing inference about the full data. We argue that mixture models are frequently more well suited than selection models to meaningful sensitivity analysis and incorporation of informative prior information about missing data. Data from longitudinal studies in smoking cessation, where outcome-related dropout is a significant problem, will be used to illustrate.

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