|Title||Sample size considerations of prediction-validation methods in high-dimensional data for survival outcomes.|
|Publication Type||Journal Article|
|Year of Publication||2013|
|Authors||Pang, Herbert, and Sin-Ho Jung|
|Date Published||2013 Apr|
|Keywords||Adenocarcinoma, Adenocarcinoma of Lung, Computer Simulation, Genome-Wide Association Study, Human Genome Project, Humans, Lung Neoplasms, Microarray Analysis, Mortality, Multiple Myeloma, Proportional Hazards Models, Research Design, Sample Size, Validation Studies as Topic|
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.
|Alternate Journal||Genet Epidemiol|
|Original Publication||Sample size considerations of prediction-validation methods in high-dimensional data for survival outcomes.|
|PubMed Central ID||PMC3763900|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
P01CA142538 / CA / NCI NIH HHS / United States
Sample size considerations of prediction-validation methods in high-dimensional data for survival outcomes.