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Selecting predictive biomarkers from genomic data

Frommlet, Florian ; Szulc, Piotr ; König, Franz and Bogdan, Malgorzata LU (2022) In PLoS ONE 17(6 6).
Abstract

Recently there have been tremendous efforts to develop statistical procedures which allow to determine subgroups of patients for which certain treatments are effective. This article focuses on the selection of prognostic and predictive genetic biomarkers based on a relatively large number of candidate Single Nucleotide Polymorphisms (SNPs). We consider models which include prognostic markers as main effects and predictive markers as interaction effects with treatment. We compare different high-dimensional selection approaches including adaptive lasso, a Bayesian adaptive version of the Sorted L-One Penalized Estimator (SLOBE) and a modified version of the Bayesian Information Criterion (mBIC2). These are compared with classical multiple... (More)

Recently there have been tremendous efforts to develop statistical procedures which allow to determine subgroups of patients for which certain treatments are effective. This article focuses on the selection of prognostic and predictive genetic biomarkers based on a relatively large number of candidate Single Nucleotide Polymorphisms (SNPs). We consider models which include prognostic markers as main effects and predictive markers as interaction effects with treatment. We compare different high-dimensional selection approaches including adaptive lasso, a Bayesian adaptive version of the Sorted L-One Penalized Estimator (SLOBE) and a modified version of the Bayesian Information Criterion (mBIC2). These are compared with classical multiple testing procedures for individual markers. Having identified predictive markers we consider several different approaches how to specify subgroups susceptible to treatment. Our main conclusion is that selection based on mBIC2 and SLOBE has similar predictive performance as the adaptive lasso while including substantially fewer biomarkers.

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author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
PLoS ONE
volume
17
issue
6 6
article number
e0269369
publisher
Public Library of Science (PLoS)
external identifiers
  • scopus:85132082144
  • pmid:35709188
ISSN
1932-6203
DOI
10.1371/journal.pone.0269369
language
English
LU publication?
yes
id
a226ec25-5fd0-4fe7-b1a3-24c83366ae44
date added to LUP
2022-09-06 12:32:26
date last changed
2024-04-04 11:34:45
@article{a226ec25-5fd0-4fe7-b1a3-24c83366ae44,
  abstract     = {{<p>Recently there have been tremendous efforts to develop statistical procedures which allow to determine subgroups of patients for which certain treatments are effective. This article focuses on the selection of prognostic and predictive genetic biomarkers based on a relatively large number of candidate Single Nucleotide Polymorphisms (SNPs). We consider models which include prognostic markers as main effects and predictive markers as interaction effects with treatment. We compare different high-dimensional selection approaches including adaptive lasso, a Bayesian adaptive version of the Sorted L-One Penalized Estimator (SLOBE) and a modified version of the Bayesian Information Criterion (mBIC2). These are compared with classical multiple testing procedures for individual markers. Having identified predictive markers we consider several different approaches how to specify subgroups susceptible to treatment. Our main conclusion is that selection based on mBIC2 and SLOBE has similar predictive performance as the adaptive lasso while including substantially fewer biomarkers.</p>}},
  author       = {{Frommlet, Florian and Szulc, Piotr and König, Franz and Bogdan, Malgorzata}},
  issn         = {{1932-6203}},
  language     = {{eng}},
  number       = {{6 6}},
  publisher    = {{Public Library of Science (PLoS)}},
  series       = {{PLoS ONE}},
  title        = {{Selecting predictive biomarkers from genomic data}},
  url          = {{http://dx.doi.org/10.1371/journal.pone.0269369}},
  doi          = {{10.1371/journal.pone.0269369}},
  volume       = {{17}},
  year         = {{2022}},
}