title: Sample size considerations of prediction-validation methods in high-dimensional data for survival outcomes.
abstract: 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.
authors.affiliation: Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, USA.
authors.forename: Herbert
authors.initials: H
authors.lastname: Pang
authors.forename: Sin-Ho
authors.initials: SH
authors.lastname: Jung
mesh_terms: D000230:Adenocarcinoma / Q000235:genetics / Q000401:mortality; D000077192:Adenocarcinoma of Lung; D003198:Computer Simulation; D055106:Genome-Wide Association Study; D016045:Human Genome Project; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics / Q000401:mortality; D046228:Microarray Analysis / Q000379:methods / Q000706:statistics & numerical data; D009026:Mortality; D009101:Multiple Myeloma / Q000235:genetics / Q000401:mortality; D016016:Proportional Hazards Models; D012107:Research Design; D018401:Sample Size; D054928:Validation Studies as Topic