A Bayesian neural network for competing risks models with covariates

C. T C Arsene, P. J G Lisboa, P. Boracchi, E. Biganzoli, M. S H Aung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationIET Conference Publications
Pages27
Number of pages1
Edition520
DOIs
Publication statusPublished - 2006
EventIET 3rd International Conference MEDSIP 2006: Advances in Medical, Signal and Information Processing - Glasgow, United Kingdom
Duration: Jul 17 2006Jul 19 2006

Other

OtherIET 3rd International Conference MEDSIP 2006: Advances in Medical, Signal and Information Processing
CountryUnited Kingdom
CityGlasgow
Period7/17/067/19/06

Keywords

  • Bayesian neural network
  • Competing risks
  • PLANN-CRARD
  • Survival analysis

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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    Arsene, C. T. C., Lisboa, P. J. G., Boracchi, P., Biganzoli, E., & Aung, M. S. H. (2006). A Bayesian neural network for competing risks models with covariates. In IET Conference Publications (520 ed., pp. 27) https://doi.org/10.1049/cp:20060386