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.

A Bayesian neural network for competing risks models with covariates / C. Arsene, P. Lisboa, P. Boracchi, E. Biganzoli, M. Aung - In: IET 3rd International Conference MEDSIP 2006. Advances in Medical, Signal and Information Processing[s.l] : Curran Associates, Inc., 2006. - ISBN 0 86341 658 6. - pp. 27-27

A Bayesian neural network for competing risks models with covariates

P. Boracchi;E. Biganzoli
Penultimo
;
2006

Abstract

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.
Bayesian neural network; Competing risks; PLANN-CRARD; Survival analysis
Settore MED/01 - Statistica Medica
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/188903
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