Abstract Details
Activity Number:
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548
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Type:
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Contributed
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Date/Time:
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Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract - #307783 |
Title:
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Adaptive Model Selection Between Cox Model and Aalen Model
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Author(s):
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Yu-Mei Chang*+
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Companies:
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Tunghai University
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Keywords:
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data perturbation ;
generalized degrees of freedom ;
Kullback-Leibler loss ;
right-censored survival data
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Abstract:
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In medical studies, Cox proportional hazards model (Cox, 1972) is the most commonly used method to analyze the survivor function of patients for right-censored data. However, the proportional hazards assumption is usually violated in practice. Therefore, the Aalen's additive model (Aalen, 1989) is an alternative choice under consideration. In this model, the covariates act in an additive manner on an unknown baseline hazard rate. The unknown risk coefficients in this model are allowed to be functions of time so that the effect of a covariate may vary over time. However, the two models generally perform differently under different circumstances, so that neither the Cox model nor the Aalen model is superior in all cases. How to select between them has not been explored in the literature. Therefore, we proposed a data-driven method for making a selection based on a concept of generalized degrees of freedom, resulting in an approximately unbiased estimator of the Kullback-Leibler loss via a data perturbation technique. The effectiveness of the proposed method is justified by a simulation study and also is applied to a real data set.
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Authors who are presenting talks have a * after their name.
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