Abstract Details
|
Activity Number:
|
559
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Wednesday, August 6, 2014 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
ENAR
|
|
Abstract #311006
|
|
|
Title:
|
Regression Analysis of Longitudinal Data with Informative Observation Times
|
|
Author(s):
|
Yang Li*+ and HaiYing Wang and Tony Sun and Xin He
|
|
Companies:
|
University of North Carolina at Charlotte and University of New Hampshire and University of Missouri and University of Maryland
|
|
Keywords:
|
informative observation times ;
joint analysis approach ;
longitudinal data analysis ;
shared frailty models
|
|
Abstract:
|
Longitudinal data usually occur in medical follow-up studies in which a certain response process is observed at discrete times. Most existing approaches on longitudinal data analysis assumed that the observation times are independent of the response process either completely or given some covariates. We present a joint analysis approach in which the possible correlation is characterized by time-varying shared frailty models. Estimating equations are developed for the parameter estimation and a simulation study is conducted to assess the finite sample performance of the approach. The asymptotic properties of the proposed estimates are also given and the method is applied to an illustrative example.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2014 program
|
2014 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Professional Development program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.