001     1034776
005     20250203103400.0
024 7 _ |2 doi
|a 10.1101/2024.02.02.24302198
037 _ _ |a FZJ-2024-07530
100 1 _ |0 P:(DE-Juel1)187351
|a Komeyer, Vera
|b 0
|e Corresponding author
245 _ _ |a Confounder control in biomedicine necessitates conceptual considerations beyond statistical evaluations
260 _ _ |c 2024
336 7 _ |0 PUB:(DE-HGF)25
|2 PUB:(DE-HGF)
|a Preprint
|b preprint
|m preprint
|s 1736237966_17716
336 7 _ |2 ORCID
|a WORKING_PAPER
336 7 _ |0 28
|2 EndNote
|a Electronic Article
336 7 _ |2 DRIVER
|a preprint
336 7 _ |2 BibTeX
|a ARTICLE
336 7 _ |2 DataCite
|a Output Types/Working Paper
520 _ _ |a Machine learning (ML) models hold promise in precision medicine by enabling personalized predictions basedon high-dimensional biomedical data. Yet, transitioning models from prototyping to clinical applications poseschallenges, with confounders being a significant hurdle by undermining the reliability, generalizability, andinterpretability of ML models. Using hand grip strength (HGS) prediction from neuroimaging data from theUK Biobank as a case study, we demonstrate that confounder adjustment can have a greater impact on modelperformance than changes in features or algorithms. An ubiquitous and necessary approach to confounding isby statistical means. However, a pure statistical viewpoint overlooks the biomedical relevance of candidateconfounders, i.e. their biological link and conceptual similarity to actual variables of interest. Problematically,this can lead to biomedically not-meaningful confounder-adjustment, which limits the usefulness of resultingmodels, both in terms of biological insights and clinical applicability. To address this, we propose a two-dimensional framework, the Confound Continuum, that combines both statistical association and biomedicalrelevance, i.e. conceptual similarity, of a candidate confounder. The evaluation of conceptual similarityassesses on a continuum how much two variables overlap in their biological meaning, ranging from negligiblelinks to expressing the same underlying biology. It thereby acknowledges the gradual nature of the biologicallink between candidate confounders and a predictive task. Our framework aims to create awareness for theimperative need to complement statistical confounder considerations with biomedical, conceptual domainknowledge (without going into causal considerations) and thereby offers a means to arrive at meaningful andinformed confounder decisions. The position of a candidate confoudner in the two-dimensional grid of theConfound Continuum can support informed and context-specific confounder decisions and thereby not onlyenhance biomedical validity of predictions but also support translation of predictive models into clinicalpractice.
536 _ _ |0 G:(DE-HGF)POF4-5254
|a 5254 - Neuroscientific Data Analytics and AI (POF4-525)
|c POF4-525
|f POF IV
|x 0
536 _ _ |0 G:(GEPRIS)431549029
|a DFG project G:(GEPRIS)431549029 - SFB 1451: Schlüsselmechanismen normaler und krankheitsbedingt gestörter motorischer Kontrolle (431549029)
|c 431549029
|x 1
588 _ _ |a Dataset connected to DataCite
700 1 _ |0 P:(DE-Juel1)131678
|a Eickhoff, Simon B.
|b 1
700 1 _ |0 P:(DE-Juel1)161406
|a Grefkes, Christian
|b 2
700 1 _ |0 P:(DE-Juel1)172843
|a Patil, Kaustubh R.
|b 3
700 1 _ |0 P:(DE-Juel1)185083
|a Raimondo, Federico
|b 4
|e Corresponding author
|u fzj
773 _ _ |a 10.1101/2024.02.02.24302198
|t medrxiv
|y 2024
856 4 _ |u https://www.medrxiv.org/content/10.1101/2024.02.02.24302198v2.full.pdf+html
909 C O |o oai:juser.fz-juelich.de:1034776
|p VDB
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)187351
|a Forschungszentrum Jülich
|b 0
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)131678
|a Forschungszentrum Jülich
|b 1
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)161406
|a Forschungszentrum Jülich
|b 2
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)172843
|a Forschungszentrum Jülich
|b 3
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)185083
|a Forschungszentrum Jülich
|b 4
|k FZJ
913 1 _ |0 G:(DE-HGF)POF4-525
|1 G:(DE-HGF)POF4-520
|2 G:(DE-HGF)POF4-500
|3 G:(DE-HGF)POF4
|4 G:(DE-HGF)POF
|9 G:(DE-HGF)POF4-5254
|a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|v Decoding Brain Organization and Dysfunction
|x 0
914 1 _ |y 2024
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)INM-7-20090406
|k INM-7
|l Gehirn & Verhalten
|x 0
920 1 _ |0 I:(DE-Juel1)INM-3-20090406
|k INM-3
|l Kognitive Neurowissenschaften
|x 1
980 _ _ |a preprint
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)INM-7-20090406
980 _ _ |a I:(DE-Juel1)INM-3-20090406
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21