Competing risks data analysis with high-dimensional covariates: an application in bladder cancer.

ABSTRACT: Analysis of microarray data is associated with the methodological problems of high dimension and small sample size. Various methods have been used for variable selection in high-dimension and small sample size cases with a single survival endpoint. However, little effort has been directed toward addressing competing risks where there is more than one failure risks. This study compared three typical variable selection techniques including Lasso, elastic net, and likelihood-based boosting for high-dimensional time-to-event data with competing risks. The performance of these methods was evaluated via a simulation study by analyzing a real dataset related to bladder cancer patients using time-dependent receiver operator characteristic (ROC) curve and bootstrap .632+ prediction error curves. Th

SUBMITTER: Tapak L 

PROVIDER: S-EPMC4563215 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature