A1073
Title: Functional time transformation model with applications to digital health
Authors: Rahul Ghosal - University of South Carolina (United States) [presenting]
Abstract: The advent of wearable and sensor technologies now leads to functional predictors that are intrinsically infinite-dimensional. While the existing approaches for functional data and survival outcomes lean on the well-established Cox model, the proportional hazard (PH) assumption might not always be suitable in real-world applications. Motivated by physiological signals encountered in digital medicine, a more general and flexible functional time-transformation model is developed for estimating the conditional survival function with both functional and scalar covariates. A partially functional regression model is used to directly model the survival time on the covariates through an unknown monotone transformation and a known error distribution. Bernstein polynomials are used to model the monotone transformation function and the smooth functional coefficients. A sieve method of maximum likelihood is employed for estimation. Numerical simulations illustrate a satisfactory performance of the proposed method in estimation and inference. The application of the proposed model is demonstrated through two case studies involving wearable data.
