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 Bayesian 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-Alien estimates of the cause-specific cumulative hazards functions.