JSM 2004 Online Program

Abstract #300381

This is the preliminary program for the 2004 Joint Statistical Meetings in Toronto, Canada. 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, 2004); 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.

Back to main JSM 2004 Program page

Activity Number:	102
Type:	Topic Contributed
Date/Time:	Monday, August 9, 2004 : 10:30 AM to 12:20 PM
Sponsor:	ENAR
Abstract - #300381
Title:	Latent Class Pattern Mixture Models for Longitudinal Data with Dropout
Author(s):	Jason Roy*+
Companies:	University of Rochester
Address:	, Rochester, NY, 14642,
Keywords:	incomplete data ; latent variable
Abstract:	Pattern mixture models are a popular approach for analyzing incomplete longitudinal data. The typical approach is to stratify the response model on dropout time. However, this approach may lead to sparse cells. An alternative is to assume the existence of a small number of dropout classes. Dropout classes are unobserved, but assumed to be related to dropout time. The response model is then stratified over classes. We propose a general model that is suitable for both continuous and discrete response. We consider both maximum likelihood and Bayesian methods for inference.

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 2004 program