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Activity Number:
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83
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Type:
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Invited
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Date/Time:
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Monday, July 30, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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| Abstract - #307831 |
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Title:
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Semiparametric Accelerated Failure Time Model with Missing Data
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Author(s):
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Menggang Yu*+
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Companies:
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Indiana University Purdue University Indianapolis
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Address:
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Department of Medicine/Biostatistics, School of Medicine, Indianapolis, IN, 46202,
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Keywords:
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Accelerated Failure Time Model ; Two-Phase Sampling ; Missing Data ; Survival Analysis ; Semiparametric Method
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Abstract:
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Semiparametric accelerated failure time model (SAFT) is a popular alternative to the Cox proportional hazard model. In SAFT, log-transformed survival time is directly modeled as a sum of linear combination of covariates and an unspecified error term. Theoretical and computational properties of SAFT have been studied in the last decade when all covariates are fully observed. In this talk, we consider the case when some of the covariates may be subject to missing. We will pay special attention to a two-phase sampling scheme when some covariates are only observed in the 2nd phase of a study depending on the first phase data. Several estimation methods will be discussed and compared. Computational aspects will also be addressed.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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