Nonlinear Semi-Parametric Models for Survival Analysis
Machine Learning
2019-05-16 v1 Machine Learning
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.
Keywords
Cite
@article{arxiv.1905.05865,
title = {Nonlinear Semi-Parametric Models for Survival Analysis},
author = {Chirag Nagpal and Rohan Sangave and Amit Chahar and Parth Shah and Artur Dubrawski and Bhiksha Raj},
journal= {arXiv preprint arXiv:1905.05865},
year = {2019}
}