Nonlinear Semi-Parametric Models for Survival Analysis
This work addresses survival analysis for researchers and practitioners by offering an incremental improvement in interpretability over existing deep learning methods.
The paper tackles the problem of survival analysis by arguing against deep non-linear models and proposing more interpretable semi-parametric models based on mixtures of experts, which perform equally well or better in some cases.
Semi-parametric survival analysis methods like the Cox Proportional Hazards (CPH) regression (Cox, 1972) are a popular approach for survival analysis. These methods involve fitting of the log-proportional hazard as a function of the covariates and are convenient as they do not require estimation of the baseline hazard rate. Recent approaches have involved learning non-linear representations of the input covariates and demonstrate improved performance. In this paper we argue against such deep parameterizations for survival analysis and experimentally demonstrate that more interpretable semi-parametric models inspired from mixtures of experts perform equally well or in some cases better than such overly parameterized deep models.