Abstract Details
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Activity Number:
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176
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Type:
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Contributed
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Date/Time:
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Monday, August 5, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #307626 |
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Title:
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An Alternative Sample Size Method for Training Survival Risk Predictors in High Dimensions
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Author(s):
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Kevin Dobbin*+ and Xiao Song
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Companies:
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University of Georgia and University of Georgia
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Keywords:
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High dimensional data ;
Survival analysis ;
Cox regression ;
Prediction
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Abstract:
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We previously developed a sample size method for training risk predictors in high dimensions. The method required a pilot dataset. Yet, in many cases, no appropriate pilot dataset is available. Motivated by this problem, we adapt the method to the setting where no pilot dataset is available. In this case, parametric assumptions must fill the place where the pilot data stood. However, leveraging the simplification provided by our errors-in-variables regression modeling approach, we develop fast and simple computational algorithms and an R script for the calculation.
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