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Cox proportional hazards model is one of the most popular models inbiomedical data analysis. There have been continuing efforts to improve theflexibility of such models for complex signal detection, for example, viaadditive functions. Nevertheless, the task to extend Cox additive models toaccommodate high-dimensional data is nontrivial. When estimating additivefunctions, commonly used group sparse regularization may introduce excesssmoothing shrinkage on additive functions, damaging predictive performance.Moreover, an "all-in-all-out" approach makes functional selection challengingto answer if nonlinear effects exist. We develop an additive Cox PH model toaddress these challenges in high-dimensional data analysis. Notably, we imposea novel spike-and-slab LASSO prior that motivates the bi-level functionalselection on additive functions. A scalable and deterministic algorithm,EM-Coordinate Descent, is designed for scalable model fitting. We compare thepredictive and computational performance against state-of-the-art models insimulation studies and metabolomics data analysis. The proposed model isbroadly applicable to various fields of research, e.g. genomics and populationhealth, via the freely available R package BHAM(https://boyiguo1.github.io/BHAM/).

A scalable and flexible Cox proportional hazards model for high-dimensional survival prediction and functional selection