Gutenberg Open Science: A systematic review and evaluation of statistical methods for group variable selection

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-10166

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DC Field	Value	Language
dc.contributor.author	Buch, Gregor	-
dc.contributor.author	Schulz, Andreas	-
dc.contributor.author	Schmidtmann, Irene	-
dc.contributor.author	Strauch, Konstantin	-
dc.contributor.author	Wild, Philipp S.	-
dc.date.accessioned	2024-03-07T13:25:22Z	-
dc.date.available	2024-03-07T13:25:22Z	-
dc.date.issued	2022	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/10184	-
dc.description.abstract	This review condenses the knowledge on variable selection methods implemented in R and appropriate for datasets with grouped features. The focus is on regularized regressions identified through a systematic review of the literature, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A total of 14 methods are discussed, most of which use penalty terms to perform group variable selection. Depending on how the methods account for the group structure, they can be classified into knowledge and data-driven approaches. The first encompass group-level and bi-level selection methods, while two-step approaches and collinearity-tolerant methods constitute the second category. The identified methods are briefly explained and their performance compared in a simulation study. This comparison demonstrated that group-level selection methods, such as the group minimax concave penalty, are superior to other methods in selecting relevant variable groups but are inferior in identifying important individual variables in scenarios where not all variables in the groups are predictive. This can be better achieved by bi-level selection methods such as group bridge. Two-step and collinearity-tolerant approaches such as elastic net and ordered homogeneity pursuit least absolute shrinkage and selection operator are inferior to knowledge-driven methods but provide results without requiring prior knowledge. Possible applications in proteomics are considered, leading to suggestions on which method to use depending on existing prior knowledge and research question.	en_GB
dc.language.iso	eng	de
dc.rights	CC BY-NC-ND	*
dc.rights.uri	https://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject.ddc	610 Medizin	de_DE
dc.subject.ddc	610 Medical sciences	en_GB
dc.title	A systematic review and evaluation of statistical methods for group variable selection	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-10166	-
jgu.type.contenttype	Scientific article	de
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 04 Medizin	de
jgu.organisation.number	2700	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Statistics in medicine	de
jgu.journal.volume	42	de
jgu.journal.issue	3	de
jgu.pages.start	331	de
jgu.pages.end	352	de
jgu.publisher.year	2022	-
jgu.publisher.name	Wiley	de
jgu.publisher.place	Chichester	de
jgu.publisher.issn	1097-0258	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	610	de
jgu.publisher.doi	10.1002/sim.9620	de
jgu.organisation.ror	https://ror.org/023b0x485	-
jgu.subject.dfg	Lebenswissenschaften	de
Appears in collections:	DFG-491381577-H

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