Abstract Details
Activity Number:
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36
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Type:
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Contributed
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Date/Time:
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Sunday, August 9, 2015 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract #316279
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Title:
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Efficient Likelihood-Based Estimation Approach for an Accelerated Failure Time Model with Case-Cohort and Nested Case-Control Sampling
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Author(s):
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Suhyun Kang* and Wenbin Lu
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Companies:
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North Carolina State University and North Carolina State University
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Keywords:
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Accelerated failure time model ;
Case cohort sampling ;
Nested case-control sampling ;
EM algorithm ;
Profile likelihood ;
Kernel smoothing
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Abstract:
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Case cohort (CC) and Nested case-control (NCC) sampling have been widely used as a cost-effective alternative to the full-cohort design. We propose an efficient and unified likelihood-based estimation approach for CC and NCC data in the Accelerated Failure Time model using the EM algorithm with a kernel smoothing technique. It is shown that the proposed estimators for the regression coefficients are consistent and asymptotically normal. The asymptotic variance of the estimators can be consistently derived using an EM-aided numerical differentiation method. Simulation studies are conducted to evaluate the finite-sample performance of the estimators, and it is applied to the Wilms' Tumor data set.
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Authors who are presenting talks have a * after their name.
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