Abstract Details
Activity Number:
|
176
|
Type:
|
Contributed
|
Date/Time:
|
Monday, August 5, 2013 : 10:30 AM to 12:20 PM
|
Sponsor:
|
Biometrics Section
|
Abstract - #307626 |
Title:
|
An Alternative Sample Size Method for Training Survival Risk Predictors in High Dimensions
|
Author(s):
|
Kevin Dobbin*+ and Xiao Song
|
Companies:
|
University of Georgia and University of Georgia
|
Keywords:
|
High dimensional data ;
Survival analysis ;
Cox regression ;
Prediction
|
Abstract:
|
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.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.