Estimation of a piecewise exponential model by Bayesian P-splines techniques for prognostic assessment and prediction

Giuseppe Marano, Patrizia Boracchi, Elia M. Biganzoli

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

Abstract

Methods for fitting survival regression models with a penalized smoothed hazard function have been recently discussed, even though they could be cumbersome. A simpler alternative which does not require specific software packages could be fitting a penalized piecewise exponential model. In this work the implementation of such strategy in Win- BUGS is illustrated, and preliminary results are reported concerning the application of Bayesian P-splines techniques. The technique is applied to a pre-specified model in which the number and positions of knots were fixed on the basis of clinical knowledge, thus defining a non-standard smoothing problem.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages183-198
Number of pages16
Volume8623
ISBN (Print)9783319244617
DOIs
Publication statusPublished - 2015
Event11th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2014 - Cambridge, United Kingdom
Duration: Jun 26 2014Jun 28 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8623
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other11th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2014
CountryUnited Kingdom
CityCambridge
Period6/26/146/28/14

Keywords

  • Bayesian P-splines
  • Hazard Smoothing
  • Piecewise Exponential Model
  • Survival analysis

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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