JSM 2005 Online Program

Abstract #304197

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:	390
Type:	Topic Contributed
Date/Time:	Wednesday, August 10, 2005 : 10:30 AM to 12:20 PM
Sponsor:	Biometrics Section
Abstract - #304197
Title:	Simulation Study for Longitudinal Data with Nonignorable Missing Data
Author(s):	Rong Liu*+ and V. Ramakrishnan
Companies:	Virginia Commonwealth University and Virginia Commonwealth University
Address:	Sanger Hall B1-066, Richmond, VA, 23298, United States
Keywords:	multivariate truncated normal ; treatment related dropout
Abstract:	In longitudinal clinical trials, the datasets often are incomplete. In some cases, the patients drop out due to treatment-related reasons, which leads the distribution of the observed data to resemble a truncated normal distribution. In these situations, the common practice of imputing the missing data using the last observation carried forward or the regression prediction method are inadequate. As an alternative, Ramakrishnan and Wang (2004) proposed a method for analysis under a truncated multivariate normal distribution. An EM algorithm was applied to simplify the estimation of truncated normal likelihood and to utilize standard software (such as SAS) for the analysis. In this talk, results from a simulation study comparing different methods are presented. Also, an extension of this method to multivariate outcomes will be discussed.

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