The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Online Program Home
Abstract Details
|
Activity Number:
|
158
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Monday, July 30, 2012 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Biometrics Section
|
| Abstract - #304969 |
|
Title:
|
Comparative Modeling of Correlated Data with Time-Dependent Covariates
|
|
Author(s):
|
Jeffrey R Wilson*+
|
|
Companies:
|
Arizona State University
|
|
Address:
|
CPCOM495D, Tempe, AZ, 85287-9801,
|
|
Keywords:
|
Longitudinal data ;
Correlated Response ;
Generalized Method of Moments ;
GEE
|
|
Abstract:
|
Analyzing longitudinal data calls for modeling both the correlation inherent from the repeated measures of the responses as well as the correlation realized on account of the feedback created between the responses at a particular time and the predictors at other times. A generalized method of moments (GMM) for estimating the coefficients in longitudinal data with correct classification of time-dependent covariates provides substantial gains in efficiency over generalized estimating equations (GEE) with the independent working correlation. We provide a comparative study of binary GMM models with different methods of classification of time-dependent covariates as well as looking at the GEE approach. We fit the GMM model using SAS IML while classifying using SAS and methods presented by Lai and Small but modified for binary data.
|
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 2012 program
|
2012 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.