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arXiv · 1905.05865

Nonlinear Semi-Parametric Models for Survival Analysis

Chirag Nagpal·Rohan Sangave·Amit Chahar·Parth Shah·Artur Dubrawski·Bhiksha Raj
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Abstract

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.

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Chirag Nagpal, Rohan Sangave, Amit Chahar, Parth Shah, Artur Dubrawski, Bhiksha Raj. 2019-05-14. Nonlinear Semi-Parametric Models for Survival Analysis. https://arxiv.org/abs/1905.05865

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