Confound modelling in UK Biobank brain imaging
Dealing with confounds is an essential step in large cohort studies to address problems such as unexplained variance and spurious correlations. UK Biobank is a powerful resource for studying associations between imaging and non-imaging measures such as lifestyle factors and health outcomes, in part...
| Main Authors: | , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Elsevier
2020
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| _version_ | 1826263897893502976 |
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| author | Alfaro-Almagro, F McCarthy, P Afyouni, S Andersson, JLR Bastiani, M Miller, KL Nichols, TE Smith, SM |
| author_facet | Alfaro-Almagro, F McCarthy, P Afyouni, S Andersson, JLR Bastiani, M Miller, KL Nichols, TE Smith, SM |
| author_sort | Alfaro-Almagro, F |
| collection | OXFORD |
| description | Dealing with confounds is an essential step in large cohort studies to address problems such as unexplained variance and spurious correlations. UK Biobank is a powerful resource for studying associations between imaging and non-imaging measures such as lifestyle factors and health outcomes, in part because of the large subject numbers. However, the resulting high statistical power also raises the sensitivity to confound effects, which therefore have to be carefully considered. In this work we describe a set of possible confounds (including non-linear effects and interactions that researchers may wish to consider for their studies using such data). We include descriptions of how we can estimate the confounds, and study the extent to which each of these confounds affects the data, and the spurious correlations that may arise if they are not controlled. Finally, we discuss several issues that future studies should consider when dealing with confounds. |
| first_indexed | 2024-03-06T19:59:12Z |
| format | Journal article |
| id | oxford-uuid:26afca73-a9f8-4869-a8e8-f516a4fabfd7 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T19:59:12Z |
| publishDate | 2020 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:26afca73-a9f8-4869-a8e8-f516a4fabfd72022-03-26T12:02:26ZConfound modelling in UK Biobank brain imagingJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:26afca73-a9f8-4869-a8e8-f516a4fabfd7EnglishSymplectic ElementsElsevier 2020Alfaro-Almagro, FMcCarthy, PAfyouni, SAndersson, JLRBastiani, MMiller, KLNichols, TESmith, SMDealing with confounds is an essential step in large cohort studies to address problems such as unexplained variance and spurious correlations. UK Biobank is a powerful resource for studying associations between imaging and non-imaging measures such as lifestyle factors and health outcomes, in part because of the large subject numbers. However, the resulting high statistical power also raises the sensitivity to confound effects, which therefore have to be carefully considered. In this work we describe a set of possible confounds (including non-linear effects and interactions that researchers may wish to consider for their studies using such data). We include descriptions of how we can estimate the confounds, and study the extent to which each of these confounds affects the data, and the spurious correlations that may arise if they are not controlled. Finally, we discuss several issues that future studies should consider when dealing with confounds. |
| spellingShingle | Alfaro-Almagro, F McCarthy, P Afyouni, S Andersson, JLR Bastiani, M Miller, KL Nichols, TE Smith, SM Confound modelling in UK Biobank brain imaging |
| title | Confound modelling in UK Biobank brain imaging |
| title_full | Confound modelling in UK Biobank brain imaging |
| title_fullStr | Confound modelling in UK Biobank brain imaging |
| title_full_unstemmed | Confound modelling in UK Biobank brain imaging |
| title_short | Confound modelling in UK Biobank brain imaging |
| title_sort | confound modelling in uk biobank brain imaging |
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