A1352
Title: Penalized deep partially linear Cox models
Authors: Yi Li - University of Michigan (United States) [presenting]
Abstract: A novel penalized deep partially linear Cox model (Penalized DPLC) is proposed, which incorporates the SCAD penalty to select important texture features and employs a deep neural network to estimate the nonparametric component of the model. The convergence and asymptotic properties of the estimator are proven and are compared to other methods through extensive simulation studies, evaluating its performance in risk prediction and feature selection. The proposed method is applied to the NLST study dataset to uncover the effects of key clinical and imaging risk factors on patients' survival. Findings provide valuable insights into the relationship between these factors and survival outcomes.
