Integration of multiple genomic data sources in a Bayesian Cox model for variable selection and prediction
type: Item
instance of: scholarly article
title: Integration of multiple genomic data sources in a Bayesian Cox model for variable selection and prediction
zbMATH Open document ID: 1395.62061
DOI: 10.1155/2017/7340565
author: Tabea Treppmann
author: Katja Ickstadt
author: Manuela Zucknick
published in: Computational \& Mathematical Methods in Medicine
publication date: 2018-08-27T00:00:00Z
review text: Summary: Bayesian variable selection becomes more and more important in statistical analyses, in particular when performing variable selection in high dimensions. For survival time models and in the presence of genomic data, the state of the art is still quite unexploited. One of the more recent approaches suggests a Bayesian semiparametric proportional hazards model for right censored time-to-event data. We extend this model to directly include variable selection, based on a stochastic search procedure within a Markov chain Monte Carlo sampler for inference. This equips us with an intuitive and flexible approach and provides a way for integrating additional data sources and further extensions. We make use of the possibility of implementing parallel tempering to help improve the mixing of the Markov chains. In our examples, we use this Bayesian approach to integrate copy number variation data into a gene-expression-based survival prediction model. This is achieved by formulating an informed prior based on copy number variation. We perform a simulation study to investigate the model's behavior and prediction performance in different situations before applying it to a dataset of glioblastoma patients and evaluating the biological relevance of the findings.
Mathematics Subject Classification ID: 62P10
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 62N01
Mathematics Subject Classification ID: 92D10
zbMATH DE Number: 6925416
describes a project that uses: R
describes a project that uses: CODA
describes a project that uses: rms
describes a project that uses: BayesDA
describes a project that uses: Rcpp
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/7340565
OpenAlex ID: W2741347789
Wikidata QID: Q38615065
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DBLP publication ID: journals/cmmm/TreppmannIZ17
description: scientific article; zbMATH DE number 6925416