Survival Analysis: A Neural-Bayesian Approach

Abstract

In this article we show that traditional Cox survival analysis can be improved upon when written in terms of a multi-layered perceptron and analyzed in the context of the Bayesian evidence framework. The obtained posterior distribution of network parameters is approximated both by Hybrid Markov Chain Monte Carlo sampling and by variational methods. We discuss the merits of both approaches. We argue that the neural-Bayesian approach circumvents the shortcomings of the original Cox analysis, and therefore yields better predictive results. As a bonus, we apply the Bayesian posterior (the probability distribution of the network parameters given the data) to estimate p-values on the inputs.