Survival on Image Regression with Application to Partially Functional Distributional Representation of Physical Activity
Conference
65th ISI World Statistics Congress 2025
Format: IPS Abstract - WSC 2025
Keywords: functional data analysis, survival analysis
Session: IPS 872 - Functional Data Analysis Approaches on Wearable Device Data
Monday 6 October 10:50 a.m. - 12:30 p.m. (Europe/Amsterdam)
Abstract
Technological advancements in wearables and medical imaging leads to high-dimensional
physiological signals in the forms of images. We develop a novel survival on image regression model with application to partially functional distributional predictors. The existing approaches for functional data and survival outcomes have been primarily developed for uni-dimensional functional predictors. Recent developments in distributional data analysis enables us to model temporally varying distributional representation of physical activity (PA) as a partially functional predictor and investigate its association with survival using a semiparametric Cox model. We use tensor product splines to model the smooth bivariate functional coefficients. A penalized partial likelihood is employed for estimation. Numerical analysis through simulations illustrates a satisfactory finite sample performance of the proposed method in estimation. The application of the proposed method is demonstrated in understanding the association between temporally varying distributional representation of physical activity and all-cause mortality based on the National Health and Nutrition Examination Survey (NHANES) 2011-2014. The results provide important insights for developing time-of-day and intensity specific PA interventions. software implementation of the proposed method is provided in R.