|
Activity Number:
|
331
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Tuesday, July 31, 2007 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
WNAR
|
| Abstract - #309771 |
|
Title:
|
Robust Inference for Joint Modeling of Longitudinal Measurements and Competing Risks Failure Time Data
|
|
Author(s):
|
Ning Li*+ and Gang Li and Robert Elashoff
|
|
Companies:
|
University of California, Los Angeles and University of California, Los Angeles and University of California, Los Angeles
|
|
Address:
|
3281 S Sepulveda Blvd, Los Angeles, CA, 90034,
|
|
Keywords:
|
Cause-specific hazard ; Competing risks ; EM algorithm ; Joint modeling ; Longitudinal data ; Robust inference
|
|
Abstract:
|
We propose a robust joint model for analysis of longitudinal measurements and competing risks failure time data in the presence of substantial outlying longitudinal observations during follow-up. Our model consists of a linear mixed effects sub-model for the longitudinal outcome and a proportional cause-specific hazards frailty sub-model for the competing risks data, linked together by some latent random effects. We study the maximum likelihood estimates of the parameters by an EM algorithm and estimate their standard errors using a profile likelihood method. The robustness of normality assumption for the random effects in the linear mixed sub-model is also investigated.
|
