JSM 2005 Online Program

Abstract #302845

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:	143
Type:	Contributed
Date/Time:	Monday, August 8, 2005 : 10:30 AM to 12:20 PM
Sponsor:	Section on Statistical Computing
Abstract - #302845
Title:	Estimating Correlation Coefficient between Two Variables with Repeated Observations Using Mixed Effects Model
Author(s):	Anuradha Roy*+
Companies:	The University of Texas at San Antonio
Address:	6900 N Loop 1604 West, San Antonio, TX, 78249, United States
Keywords:	Autoregressive Covariance Structure ; Compound Symmetry ; Maximum Likelihood Estimates ; Mixed Effects Model ; Proc Mixed
Abstract:	Under the assumption of multivariate normality, we estimate the correlation coefficient between two variables with repeated observations on each variable using Mixed Effects Model. The solution to this problem was studied by many. Bland and Altman (1995) considered the problem in many ad hoc ways. Lam, Webb, and O'Donnell (1999) solved the problem by considering different correlation structure on the repeated measures by assuming the repeated measures are linked over time; but, their method needs specialized software. Hamlett, Ryan, Serrano-Trespalacios, and Wolfinger (2003) generalized their model and used Proc Mixed of SAS. We also assume the repeated measures are linked over time and generalize all the previous models. We study how the correlation coefficient between the variables gets affected by incorrect assumption of the correlation structure on the repeated measures by using Proc Mixed of SAS. Our model also will work when some of the repeated measures are missing at random.

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