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Abstract Details
Activity Number:
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60
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Type:
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Topic Contributed
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Date/Time:
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Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract - #304185 |
Title:
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Quantile Regression for Left-Truncated Semi-Competing Risks Data
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Author(s):
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Ruosha Li*+ and Limin Peng
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Companies:
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University of Pittsburgh and Emory University
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Address:
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730 S Negley Ave Apt 31, Pittsburgh, PA, 15232, United States
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Keywords:
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cumulative incidence ;
left truncation ;
quantile regression ;
semicompeting risks
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Abstract:
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Semicompeting risks is often encountered in biomedical studies where a terminating event censors a nonterminating event but not vice versa. In practice, left truncation on the terminating event may arise and can greatly complicate the regression analysis on the nonterminating event. In this work, we propose a quantile regression method for left-truncated semicompeting risks data, which provides meaningful interpretations as well as the flexibility to accommodate varying covariate effects. We develop estimation and inference procedures that can be easily implemented by existing statistical software. Asymptotic properties of the resulting estimators are established including uniform consistency and weak convergence. The finite-sample performance of the proposed method is evaluated via simulation studies. An application to a registry dataset provides an illustration of our proposals.
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