Abstract Details
Activity Number:
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7
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Type:
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Invited
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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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Section on Statistics in Epidemiology
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Abstract #314533
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Title:
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Thresholding Regression with Covariate Subject to Random Censoring
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Author(s):
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Jing Qian* and Folefac Atem and Rebecca Betensky
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Companies:
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University of Massachusetts Amherst and Harvard University and Harvard University
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Keywords:
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Censored predictor ;
Kaplan-Meier estimator ;
Survival analysis
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Abstract:
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Censored covariates arise frequently in biomarker assessment in epidemiological studies and in family history studies of disease. While there is a large literature on regression models when the outcome variable is subject to censoring, there is a more limited literature on the treatment of censored covariates, especially for type II censoring. We develop threshold regression approaches for linear regression models with covariate subject to random censoring. Compared with existing methods, the proposed methods are simple but effective as they avoid complicated modeling in dealing with censored covariate values. We study the asymptotic properties of the resultant estimators. In addition to estimating the regression coefficient of the censored covariate, the threshold regression methods can also be used to test whether the effect of the censored covariate is significant. We discuss the choice of optimal threshold which yields the most powerful test. The finite sample performance of the proposed methods are assessed through simulation studies. We also apply the method to a motivation example.
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Authors who are presenting talks have a * after their name.
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