Gutenberg Open Science: A weighting approach for judging the effect of patient strata on high-dimensional risk prediction signatures

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-8062

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DC Field	Value	Language
dc.contributor.author	Weyer, Veronika	-
dc.contributor.author	Binder, Harald	-
dc.date.accessioned	2022-10-17T08:03:18Z	-
dc.date.available	2022-10-17T08:03:18Z	-
dc.date.issued	2015
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/8077	-
dc.description.abstract	BACKGROUND: High-dimensional molecular measurements, e.g. gene expression data, can be linked to clinical time-to-event endpoints by Cox regression models and regularized estimation approaches, such as componentwise boosting, and can incorporate a large number of covariates as well as provide variable selection. If there is heterogeneity due to known patient subgroups, a stratified Cox model allows for separate baseline hazards in each subgroup. Variable selection will still depend on the relative stratum sizes in the data, which might be a convenience sample and not representative for future applications. Such effects need to be systematically investigated and could even help to more reliably identify components of risk prediction signatures. RESULTS: Correspondingly, we propose a weighted regression approach based on componentwise likelihood-based boosting which is implemented in the R package CoxBoost (https://github.com/binderh/CoxBoost). This approach focuses on building a risk prediction signature for a specific stratum by down-weighting the observations from the other strata using a range of weights. Stability of selection for specific covariates as a function of the weights is investigated by resampling inclusion frequencies, and two types of corresponding visualizations are suggested. This is illustrated for two applications with methylation and gene expression measurements from cancer patients. CONCLUSION: The proposed approach is meant to point out components of risk prediction signatures that are specific to the stratum of interest and components that are also important to other strata. Performance is mostly improved by incorporating down-weighted information from the other strata. This suggests more general usefulness for risk prediction signature development in data with heterogeneity due to known subgroups.	en_GB
dc.description.sponsorship	DFG, Open Access-Publizieren Universität Mainz / Universitätsmedizin	de
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	610 Medizin	de_DE
dc.subject.ddc	610 Medical sciences	en_GB
dc.title	A weighting approach for judging the effect of patient strata on high-dimensional risk prediction signatures	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-8062	-
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	BMC bioinformatics	de
jgu.journal.volume	16	de
jgu.pages.alternative	Art. 294	de
jgu.publisher.year	2015	-
jgu.publisher.name	BioMed Central	de
jgu.publisher.place	London	de
jgu.publisher.uri	http://dx.doi.org/10.1186/s12859-015-0716-8	de
jgu.publisher.issn	1471-2105	de
jgu.organisation.place	Mainz	-
jgu.identifier.pmid	26374641
jgu.subject.ddccode	610	de
opus.date.modified	2017-05-11T09:37:15Z
opus.subject.dfgcode	00-000
opus.organisation.string	FB 04: Medizin: Institut für Med. Biometrie, Epidemologie und Informatik	de_DE
opus.identifier.opusid	53341
opus.importsource	pubmed
opus.institute.number	0424
opus.metadataonly	false
opus.type.contenttype	Keine	de_DE
opus.type.contenttype	None	en_EN
opus.affiliated	Binder, Harald
jgu.publisher.doi	10.1186/s12859-015-0716-8	de
jgu.organisation.ror	https://ror.org/023b0x485	-
Appears in collections:	DFG-OA-Publizieren (2012 - 2017)

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