Abstract Details
Activity Number:
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346
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
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Sponsor:
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ENAR
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Abstract #317379
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View Presentation
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Title:
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A Pairwise-Likelihood Augmented Estimator for Cox Model Under Left-Truncation
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Author(s):
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Fan Wu* and Sehee Kim and Jing Qin and Peter Kotanko and Rajiv Saran and Yi Li
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Companies:
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and University of Michigan and National Institute of Allergy and Infectious Diseases and Renal Research Institute and Kidney Epidemiology and Cost Center and University of Michigan
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Keywords:
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Semiparaemtric Methods ;
Length-biased sampling ;
Composite-Likelihood ;
Empirical Process ;
U-process ;
Self-consistency
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Abstract:
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Survival data collected from prevalent cohorts are subject to left-truncation. Conventional conditional approaches disregard the information in the marginal likelihood of truncation time thus can be inefficient. On the other hand, the stationary assumption under length-biased sampling (LBS) methods can lead to biased estimation when it is violated. In this paper, we propose a semiparametric estimation method by augmenting the Cox partial likelihood with a pairwise likelihood. We eliminate the unspecified truncation distribution in the marginal likelihood, yet retain the information about regression coefficients and the baseline hazard. Self-consistency of the estimator guarantees a fast algorithm to solve for the regression coefficients and the baseline hazard simultaneously. The proposed estimator is shown to be consistent and asymptotically normal with a consistent sandwich-type variance estimator. Simulations indicate a substantial efficiency gain in both the regression coefficients and the cumulative hazard over Cox estimators, and that the gain is comparable to LBS methods when the stationary assumption holds. A data analysis illustrates the application of the methods.
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Authors who are presenting talks have a * after their name.
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