Neural Frailty Machine: Beyond proportional hazard assumption in neural survival regressions
This work addresses the problem of survival analysis in domains like healthcare by extending neural models beyond proportional hazards, though it is incremental as it builds on existing neural and frailty concepts.
The authors tackled the limitation of proportional hazard assumptions in neural survival models by introducing the neural frailty machine (NFM), which incorporates multiplicative frailty to capture unobserved heterogeneity and leverages neural networks for nonlinear covariate dependence, resulting in improved predictive performance over state-of-the-art models on 6 benchmark datasets.
We present neural frailty machine (NFM), a powerful and flexible neural modeling framework for survival regressions. The NFM framework utilizes the classical idea of multiplicative frailty in survival analysis to capture unobserved heterogeneity among individuals, at the same time being able to leverage the strong approximation power of neural architectures for handling nonlinear covariate dependence. Two concrete models are derived under the framework that extends neural proportional hazard models and nonparametric hazard regression models. Both models allow efficient training under the likelihood objective. Theoretically, for both proposed models, we establish statistical guarantees of neural function approximation with respect to nonparametric components via characterizing their rate of convergence. Empirically, we provide synthetic experiments that verify our theoretical statements. We also conduct experimental evaluations over $6$ benchmark datasets of different scales, showing that the proposed NFM models outperform state-of-the-art survival models in terms of predictive performance. Our code is publicly availabel at https://github.com/Rorschach1989/nfm