A Bayesian Neural Network for Competing Risks Models with Covariates

This paper presents a Bayesian Neural Network for the analysis of Competing Risk (CR) data model. Based on a previously developed non-linear model namely Partial Logistic Artificial Neural Network (PLANN) with Automatic Relevance Determination (ARD), this paper proposes an extension for the flexible joint estimation of cause-specific hazards depending on both discrete and continuous covariates (PLANN-CR-ARD) and for censored data. The Bayesia analysis uses Gaussian priors for the neural network parameters and the likelihood function based on the competing risk data is identified as the cross-entropy function. The PLANN-CR-ARD model is illustrated with analyses of an Intra-Ocular Melanoma dataset and comparison with the non-parametric Nelson-Allen estimates of the cause-specific cumulative hazards functions.