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Abstract Details
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Activity Number:
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506
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #302690 |
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Title:
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Quantile Regression for Dependently Censored Data
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Author(s):
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Shuang Ji*+ and Limin Peng
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Companies:
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Emory University and Emory University
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Address:
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Department of Biostatistics and Bioinformatics, Atlanta, GA, 30322, US
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Keywords:
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quantile regression ;
survival analysis ;
dependent censoring
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Abstract:
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Quantile regression is known for its flexibility in accommodating varying covariate effects and has attracted growing interests in survival analysis. Considerable research effort has been devoted to study quantile regression under independent censoring assumptions. However, dependent censoring often occurs in practice and ignoring the association between censoring and the event time of interest may lead to substantial biases. In this work, we develop a quantile regression method that appropriately adjusts for dependent censoring when making inferences on covariate effects on marginal quantiles of the event time outcome. We propose valid estimation and inference procedures, along with an efficient and stable algorithm. We establish the uniform consistency and weak convergence of the resulting estimators. The finite-sample performance of our approach is assessed by extensive simulation studies. We illustrate the practical utility of our method via an application to a stroke clinical trial.
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