Abstract Details
Activity Number:
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596
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Type:
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Contributed
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Date/Time:
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Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract - #309820 |
Title:
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Inference for Survival Prediction in the High-Dimensional Setting
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Author(s):
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Jennifer Sinnott*+ and Tianxi Cai
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Companies:
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Harvard University and Harvard University
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Keywords:
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high dimensional ;
survival function ;
prediction error ;
resampling ;
shrinkage ;
prostate cancer
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Abstract:
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For a risk prediction model to provide clinical utility, it is crucial that it deliver both a prediction for a new patient's risk and an honest assessment of the error inherent in that prediction. When the number of predictors is small, classical methods can capture prediction error; however, when the predictors are high dimensional, estimation of the prediction error can be challenging. In this high dimensional setting, we investigate inference on the survival function estimated using a model, such as the Cox model, under shrinkage. A shrinkage method can be chosen to give nice theoretical properties including asymptotic normality and asymptotically perfect variable selection; nevertheless, in finite samples, the estimated conditional survival distribution can be difficult to approximate using either asymptotic results, which can underestimate the variability, or the standard bootstrap, which may yield overly-conservative standard error estimates. We propose an adaptation of perturbation resampling designed to improve estimation of the error in survival prediction. We demonstrate our method in a study relating a large panel of tissue biomarkers to prostate cancer progression.
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Authors who are presenting talks have a * after their name.
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