You are here

APPLICATION OF THE EMPIRICAL LIKELIHOOD METHOD IN PROPORTIONAL HAZARDS MODEL

Download pdf | Full Screen View

Date Issued:
2006
Abstract/Description:
In survival analysis, proportional hazards model is the most commonly used and the Cox model is the most popular. These models are developed to facilitate statistical analysis frequently encountered in medical research or reliability studies. In analyzing real data sets, checking the validity of the model assumptions is a key component. However, the presence of complicated types of censoring such as double censoring and partly interval-censoring in survival data makes model assessment difficult, and the existing tests for goodness-of-fit do not have direct extension to these complicated types of censored data. In this work, we use empirical likelihood (Owen, 1988) approach to construct goodness-of-fit test and provide estimates for the Cox model with various types of censored data.Specifically, the problems under consideration are the two-sample Cox model and stratified Cox model with right censored data, doubly censored data and partly interval-censored data. Related computational issues are discussed, and some simulation results are presented. The procedures developed in the work are applied to several real data sets with some discussion.
Title: APPLICATION OF THE EMPIRICAL LIKELIHOOD METHOD IN PROPORTIONAL HAZARDS MODEL.
20 views
15 downloads
Name(s): HE, BIN, Author
Ren, Jian-Jian, Committee Chair
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2006
Publisher: University of Central Florida
Language(s): English
Abstract/Description: In survival analysis, proportional hazards model is the most commonly used and the Cox model is the most popular. These models are developed to facilitate statistical analysis frequently encountered in medical research or reliability studies. In analyzing real data sets, checking the validity of the model assumptions is a key component. However, the presence of complicated types of censoring such as double censoring and partly interval-censoring in survival data makes model assessment difficult, and the existing tests for goodness-of-fit do not have direct extension to these complicated types of censored data. In this work, we use empirical likelihood (Owen, 1988) approach to construct goodness-of-fit test and provide estimates for the Cox model with various types of censored data.Specifically, the problems under consideration are the two-sample Cox model and stratified Cox model with right censored data, doubly censored data and partly interval-censored data. Related computational issues are discussed, and some simulation results are presented. The procedures developed in the work are applied to several real data sets with some discussion.
Identifier: CFE0001099 (IID), ucf:46780 (fedora)
Note(s): 2006-05-01
Ph.D.
Sciences, Department of Mathematics
Doctorate
This record was generated from author submitted information.
Subject(s): Bootstrap
confidence interval
Cox model
doubly censored data
empirical likelihood function
goodness-of-fit test
maximum likelihood
partly interval-censored data
proportional hazards model
right censored data
survival analysis.
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0001099
Restrictions on Access: campus 2007-01-31
Host Institution: UCF

In Collections