BDNNSurv: Bayesian Deep Neural Networks for Survival Analysis Using Pseudo Values

Feng, Dai; Zhao, Lili

doi:10.6339/21-JDS1018

Journal of Data Science

BDNNSurv: Bayesian Deep Neural Networks for Survival Analysis Using Pseudo Values

Volume 19, Issue 4 (2021), pp. 542–554

Dai Feng Lili Zhao

https://doi.org/10.6339/21-JDS1018

Pub. online: 13 August 2021 Type: Statistical Data Science

Received
2 March 2021

Accepted
15 July 2021

Published
13 August 2021

Abstract

There has been increasing interest in modeling survival data using deep learning methods in medical research. In this paper, we proposed a Bayesian hierarchical deep neural networks model for modeling and prediction of survival data. Compared with previously studied methods, the new proposal can provide not only point estimate of survival probability but also quantification of the corresponding uncertainty, which can be of crucial importance in predictive modeling and subsequent decision making. The favorable statistical properties of point and uncertainty estimates were demonstrated by simulation studies and real data analysis. The Python code implementing the proposed approach was provided.

Supplementary material

Supplementary Material

The code used for simulation, including R code to simulate data and summarize results, python code to generate initial values for BDNNSurv, python code to run BDNNSurv, and R code to run BART and ANOVA DDP.

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Keywords

automatic differentiation variational inference Bayesian deep learning neural networks pseudo probability survival outcome

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