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Activity Number:
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468
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Type:
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Contributed
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Date/Time:
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Wednesday, August 5, 2009 : 10:30 AM to 12:20 PM
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Sponsor:
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SSC
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| Abstract - #303418 |
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Title:
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Semiparametric Inference for a Time-Dependent Extended Hazard Model
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Author(s):
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Yi-Kuan Tseng*+ and Ken-Ning Hsu
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Companies:
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National Central University and National Central University
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Address:
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Graduate Institute of Statistics , Jongli, International, 32001, Taiwan
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Keywords:
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Extended hazard model ; Time-dependent covariates ; Cox model ; AFT model ; HAART
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Abstract:
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We propose a natural extension of Cox and AFT model, termed "extended hazard (EH) model" for the analysis of survival data with time-dependent covariates. The EH model refers to a general class of semiparametric regression models which includes both the Cox model and the accelerated failure time (AFT) model as its subclasses. A class of estimating equation using counting process and martingale theory is developed for estimating the regression parameters of the proposed model. The resulting estimators are shown to be consistent and asymptotically normal under appropriate regularity conditions. Since the EH model includes both the Cox model and the AFT model as its special cases such that we can perform tests by this nested structure to test the Cox and the AFT hypotheses. The proposed approach is applied to analyze the well-known Standard heart transplant data and Taiwan AIDS data.
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