Variable Selection in Functional Linear Cox Model (FLCM) with Multiple Functional and Scalar Covariates

Music City Center 

As biomedical studies increasingly gather complex, high-dimensional physiological data, effective variable selection methods are essential to manage this complexity and enhance accuracy in survival models. We propose a flexible penalized variable selection method for a functional Cox model with multiple functional and scalar covariates, utilizing the group minimax concave penalty (MCP) which automatically integrates smoothness into the estimation of functional coefficients. Additionally, we introduce a novel framework for selecting smoothing parameters within the Extended Bayesian Information Criteria (EBIC), distinguished by a new method for calculating degrees of freedom. Through a simulation study, we demonstrate the method's ability to perform accurate variable selection and parameter estimation. The method is applied to National Health and Nutrition Examination Survey (NHANES) data, identifying the key temporally varying distributional features of physical activity and demographic predictors related to all-cause mortality. This analysis sheds light on the intricate relationship between physical activity and all-cause mortality among older US adults.

Functional data analysis

Survival analysis

Variable selection

NHANES 

Biometrics Section