A0691
Title: Joint variable screening in accelerated failure time models
Authors: Jinfeng Xu - University of Hong Kong (Hong Kong) [presenting]
Abstract: Variable screening has gained increasing popularity in high-dimensional survival analysis. Most existing methods for variable screening with survival data suffer from that variable importance is assessed based on marginal models that relate the time-to-event outcome to each variable separately, implying that the relevance of one variable is examined when other variables are excluded. These methods will preclude variables that only manifest their influence jointly and may retain irrelevant variables that are correlated with relevant ones. To circumvent these difficulties, we propose a new approach to evaluating joint variable importance in censored accelerated failure time models. We establish the sure screening properties of the proposed approach and demonstrate its effectiveness through simulation studies and a real data application. A novel stability selection-based procedure is also proposed for tuning.
