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@ARTICLE{Komeyer:1034776,
      author       = {Komeyer, Vera and Eickhoff, Simon B. and Grefkes, Christian
                      and Patil, Kaustubh R. and Raimondo, Federico},
      title        = {{C}onfounder control in biomedicine necessitates conceptual
                      considerations beyond statistical evaluations},
      journal      = {medrxiv},
      reportid     = {FZJ-2024-07530},
      year         = {2024},
      abstract     = {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.},
      cin          = {INM-7 / INM-3},
      cid          = {I:(DE-Juel1)INM-7-20090406 / I:(DE-Juel1)INM-3-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      DFG project G:(GEPRIS)431549029 - SFB 1451:
                      Schlüsselmechanismen normaler und krankheitsbedingt
                      gestörter motorischer Kontrolle (431549029)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(GEPRIS)431549029},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.1101/2024.02.02.24302198},
      url          = {https://juser.fz-juelich.de/record/1034776},
}