JSM 2005 Online Program

Abstract #303495

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:	455
Type:	Contributed
Date/Time:	Wednesday, August 10, 2005 : 2:00 PM to 3:50 PM
Sponsor:	Section on Statistics in Epidemiology
Abstract - #303495
Title:	The Multilevel Hierarchical Random Effect Models: What Do We Gain by Accounting for More Levels?
Author(s):	An-Lin Cheng*+ and Haiqun Lin
Companies:	Yale University and Yale University
Address:	60 College Street, New Haven, CT, 06520, United States
Keywords:	Hierarchical random effect models ; cluster data ; longitudinal
Abstract:	Hierarchical random effect models have been commonly applied when analyzing the cluster data resulting from various experimental settings. In one setting, we might have longitudinal measurements for a subject along with treatment and geographical information. Should we account both the subject and geographical effects as random effect, or is a simpler model preferred? In this paper, we present the simulation study concerning the performance of different models by comparing coverage rates of the true parameters and bias and standard error for the estimates of the treatment effects. Several unbalanced scenarios with respect to measurements also are considered. The discussion is extended to categorical responses. We also illustrate the results using a real dataset obtained from the ACCESS (Access to Community Care and Effective Services and Supports).

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