Authors:	Weyer, Veronika Binder, Harald
Title:	A weighting approach for judging the effect of patient strata on high-dimensional risk prediction signatures
Online publication date:	17-Oct-2022
Year of first publication:	2015
Language:	english
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.
DDC:	610 Medizin 610 Medical sciences
Institution:	Johannes Gutenberg-Universität Mainz
Department:	FB 04 Medizin
Place:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-8062
Version:	Published version
Publication type:	Zeitschriftenaufsatz
License:	CC BY
Information on rights of use:	https://creativecommons.org/licenses/by/4.0/
Journal:	BMC bioinformatics 16
Pages or article number:	Art. 294
Publisher:	BioMed Central
Publisher place:	London
Issue date:	2015
ISSN:	1471-2105
Publisher URL:	http://dx.doi.org/10.1186/s12859-015-0716-8
Publisher DOI:	10.1186/s12859-015-0716-8
Appears in collections:	DFG-OA-Publizieren (2012 - 2017)