Unbiased Prediction and Feature Selection in High-Dimensional Survival Regression.

ABSTRACT: With widespread availability of omics profiling techniques, the analysis and interpretation of high-dimensional omics data, for example, for biomarkers, is becoming an increasingly important part of clinical medicine because such datasets constitute a promising resource for predicting survival outcomes. However, early experience has shown that biomarkers often generalize poorly. Thus, it is crucial that models are not overfitted and give accurate results with new data. In addition, reliable detection of multivariate biomarkers with high predictive power (feature selection) is of particular interest in clinical settings. We present an approach that addresses both aspects in high-dimensional survival models. Within a nested cross-validation (CV), we fit a survival model, evaluate a dataset i

SUBMITTER: Laimighofer M 

PROVIDER: S-EPMC4827277 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature