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APPLICATION OF THE EMPIRICAL LIKELIHOOD METHOD IN PROPORTIONAL HAZARDS MODEL
- 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. |
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Name(s): |
HE, BIN, Author Ren, Jian-Jian, Committee Chair University of Central Florida, Degree Grantor |
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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. |
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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. |
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Persistent Link to This Record: | http://purl.flvc.org/ucf/fd/CFE0001099 | |
Restrictions on Access: | campus 2007-01-31 | |
Host Institution: | UCF |