A0528
Title: Penalized deep partially linear cox models with application to CT scans of lung cancer patients
Authors: Yuming Sun - College of William and Mary (United States) [presenting]
Yi Li - University of Michigan (United States)
Jian Kang - University of Michigan (United States)
Abstract: Partially linear Cox models have gained popularity for survival analysis by dissecting the hazard function into parametric and nonparametric components, allowing for the effective incorporation of both well-established risk factors (such as age and clinical variables) and emerging risk factors (e.g., image features) within a unified framework. However, when the dimension of parametric components exceeds the sample size, the task of model fitting becomes formidable, while nonparametric modeling grapples with the curse of dimensionality. A novel penalized deep partially linear Cox model (penalized DPLC) is proposed, which incorporates the smoothly clipped absolute deviation (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 compared to other methods through extensive simulation studies, evaluating its performance in risk prediction and feature selection. The proposed method is applied to the National Lung Screening Trial 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.
