Accounting for grouped predictor variables or pathways in high-dimensional penalized Cox regression models.

ABSTRACT: BACKGROUND:The standard lasso penalty and its extensions are commonly used to develop a regularized regression model while selecting candidate predictor variables on a time-to-event outcome in high-dimensional data. However, these selection methods focus on a homogeneous set of variables and do not take into account the case of predictors belonging to functional groups; typically, genomic data can be grouped according to biological pathways or to different types of collected data. Another challenge is that the standard lasso penalisation is known to have a high false discovery rate. RESULTS:We evaluated different penalizations in a Cox model to select grouped variables in order to further penalize variables that, in addition to having a low effect, belong to a group with a low overall effe

SUBMITTER: Belhechmi S 

PROVIDER: S-EPMC7331150 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature