Gaussian Processes for Survival Analysis

TL;DR

This paper presents a flexible Bayesian survival analysis model using Gaussian processes that accommodates various censoring types and outperforms existing methods in accuracy and computational efficiency.

Contribution

It introduces a semi-parametric Bayesian framework with Gaussian processes for survival analysis, handling multiple censoring types without restrictive assumptions.

Findings

Outperforms Cox, ANOVA-DDP, and random survival forests in experiments.

Handles left, right, and interval censoring effectively.

Uses a scalable approximation scheme for faster inference.

Abstract

We introduce a semi-parametric Bayesian model for survival analysis. The model is centred on a parametric baseline hazard, and uses a Gaussian process to model variations away from it nonparametrically, as well as dependence on covariates. As opposed to many other methods in survival analysis, our framework does not impose unnecessary constraints in the hazard rate or in the survival function. Furthermore, our model handles left, right and interval censoring mechanisms common in survival analysis. We propose a MCMC algorithm to perform inference and an approximation scheme based on random Fourier features to make computations faster. We report experimental results on synthetic and real data, showing that our model performs better than competing models such as Cox proportional hazards, ANOVA-DDP and random survival forests.