Sample size considerations of prediction-validation methods in high-dimensional data for survival outcomes.
Published
Journal Article
A variety of prediction methods are used to relate high-dimensional genome data with a clinical outcome using a prediction model. Once a prediction model is developed from a data set, it should be validated using a resampling method or an independent data set. Although the existing prediction methods have been intensively evaluated by many investigators, there has not been a comprehensive study investigating the performance of the validation methods, especially with a survival clinical outcome. Understanding the properties of the various validation methods can allow researchers to perform more powerful validations while controlling for type I error. In addition, sample size calculation strategy based on these validation methods is lacking. We conduct extensive simulations to examine the statistical properties of these validation strategies. In both simulations and a real data example, we have found that 10-fold cross-validation with permutation gave the best power while controlling type I error close to the nominal level. Based on this, we have also developed a sample size calculation method that will be used to design a validation study with a user-chosen combination of prediction. Microarray and genome-wide association studies data are used as illustrations. The power calculation method in this presentation can be used for the design of any biomedical studies involving high-dimensional data and survival outcomes.
Full Text
Duke Authors
Cited Authors
- Pang, H; Jung, S-H
Published Date
- April 2013
Published In
Volume / Issue
- 37 / 3
Start / End Page
- 276 - 282
PubMed ID
- 23471879
Pubmed Central ID
- 23471879
Electronic International Standard Serial Number (EISSN)
- 1098-2272
Digital Object Identifier (DOI)
- 10.1002/gepi.21721
Language
- eng
Conference Location
- United States