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Activity Number: 346
Type: Topic Contributed
Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract #317379 View Presentation
Title: A Pairwise-Likelihood Augmented Estimator for Cox Model Under Left-Truncation
Author(s): Fan Wu* and Sehee Kim and Jing Qin and Peter Kotanko and Rajiv Saran and Yi Li
Companies: 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
Keywords: Semiparaemtric Methods ; Length-biased sampling ; Composite-Likelihood ; Empirical Process ; U-process ; Self-consistency

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.

Authors who are presenting talks have a * after their name.

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