Abstract Details
Activity Number:
|
292
|
Type:
|
Contributed
|
Date/Time:
|
Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Biometrics Section
|
Abstract #312170
|
|
Title:
|
Extending Fine and Gray Model: New Approach for Competing Risks Analysis
|
Author(s):
|
Anna Bellach*+ and Jason Fine and Ludger Rüschendorf and Michael Kosorok
|
Companies:
|
University of Copenhagen and University of North Carolina at Chapel Hill and Albert Ludwigs University and University of North Carolina at Chapel Hill
|
Keywords:
|
competing risks ;
Fine and Gray model ;
semiparametric transformation models ;
pseudo likelihood function ;
NPMLE ;
time varying covariates
|
Abstract:
|
Fine and Gray have established a version of the Cox's model for the hazard rate of the subdistribution in a competing risks setting. As the proportional hazards assumption does not hold in general, it is of interest also to consider other regression models for competing risks settings. We introduce a new pseudo likelihood function that can be used to derive estimators for a general class of models. For the class of semiparametric transformation models proposed by Zeng and Lin we prove the consistency and asymptotic normality of the estimators. In simulation studies and real data examples our method works well. The Fine and Gray model is a special case of our general approach.
|
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.