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

Abstract Details

Activity Number: 251
Type: Contributed
Date/Time: Monday, August 2, 2010 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #307403
Title: Marginal Method for Multilevel Incomplete Binary Data That Are Missing at Random
Author(s): Baojiang Chen*+ and Xiao-Hua Zhou
Companies: University of Washington and University of Washington
Address: 4311 11th Ave NE #300, Seattle, WA, 98105,
Keywords: Estimating equation ; latent variable ; missing at random ; multi-level
Abstract:

Incomplete multi-level data arise common in many clinical trials and observational studies. Multi-level variations arise in this type of data and appropriate data analysis should take these variations into account. A random effect model can allow the variations by assuming random effects in each level, but the estimation is often intensive. Marginal methods such as the IPWGEE can involve simple estimation computation, but it is hard to specify the working correlation matrix for multi-level data. In this paper, we introduce a latent variable method to deal with incomplete multi-level data, which fills the gaps between the random effect models and marginal models. Latent variable models are built in both the response and missing data processes to incorporate the variations arise in each level. Simulation studies demonstrate that this method performs well in various situations.


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 2010 program




2010 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.