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Abstract Details
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Activity Number:
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32
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Type:
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Contributed
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Date/Time:
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Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #305458 |
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Title:
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Variable Selection for Proportional Hazards Cure Model
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Author(s):
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Xiang Liu*+ and Yingwei Peng and Dongsheng Tu and Hua Liang
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Companies:
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Abbott Laboratories and Queen's University and Queen's University and University of Rochester
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Address:
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2007 Greystem Circle, Gurnee, IL, 60031, United States
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Keywords:
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Cox proportional hazards models ;
EM algorithm ;
generalized linear models ;
penalized likelihood ;
SCAD
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Abstract:
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Survival data with a sizable cure fraction are often encountered in cancer researches and the semiparametric proportional hazards (PH) cure model is a common approach to analyze such data. However, data from a breast cancer clinical study call for the need of a variable selection approach to identify important variables/covariates in predicting a patient's cure status and risk of breast cancer recurrence. In this talk, we present a novel variable selection approach by considering penalized likelihood for the PH cure model. The method is implemented by combining the penalized likelihood methods for logistic regression models and the Cox PH models in the EM algorithm. Simulation studies and the application to a breast cancer clinical study are used to demonstrate the performance of our proposed method.
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