@inproceedings{f8535dc3ed0a4351a77cb738b8d67554,
title = "Estimation of a piecewise exponential model by Bayesian P-splines techniques for prognostic assessment and prediction",
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.",
keywords = "Bayesian P-splines, Hazard Smoothing, Piecewise Exponential Model, Survival analysis",
author = "Giuseppe Marano and Patrizia Boracchi and Biganzoli, {Elia M.}",
year = "2015",
doi = "10.1007/978-3-319-24462-4_16",
language = "English",
isbn = "9783319244617",
volume = "8623",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "183--198",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
note = "11th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2014 ; Conference date: 26-06-2014 Through 28-06-2014",
}