A1233
Title: Variable selection in functional linear Cox model
Authors: Rahul Ghosal - University of South Carolina (United States) [presenting]
Abstract: Modern biomedical studies frequently collect complex, high-dimensional physiological signals using wearables and sensors along with time-to-event outcomes, making efficient variable selection methods crucial for interpretation and improving the accuracy of survival models. A novel variable selection method is proposed for a functional linear Cox model with multiple functional and scalar covariates measured at baseline. A spline-based semiparametric estimation approach is utilized for the functional coefficients and a group minimax concave type penalty (MCP), which effectively integrates smoothness and sparsity into the estimation of functional coefficients. An efficient group descent algorithm is used for optimization, and an automated procedure is provided to select optimal values of the smoothing and sparsity parameters. Through simulation studies, the method's ability to perform accurate variable selection and estimation is demonstrated. The method is applied to 2003-06 cohort of the National Health and Nutrition Examination Survey (NHANES) data, identifying the key temporally varying distributional patterns of physical activity and demographic predictors related to all-cause mortality. The analysis sheds light on the intricate association between daily distributional patterns of physical activity and all-cause mortality among older US adults.
