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Confound modelling in UK Biobank brain imaging.

Fidel Alfaro-Almagro1, Paul McCarthy1, Soroosh Afyouni2

  • 1Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK.

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Summary
This summary is machine-generated.

Controlling for confounding variables is crucial in large cohort studies like UK Biobank to prevent spurious correlations. This study identifies potential confounds and their impact on data analysis, offering guidance for future research.

Keywords:
Big data imagingConfoundsData modellingEpidemiological studiesImage analysisMachine learningMulti-modal data integrationStatistica l modelling

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Area of Science:

  • Biomedical Research
  • Data Science
  • Statistical Genetics

Background:

  • Large cohort studies, such as UK Biobank, offer immense potential for research due to large sample sizes.
  • High statistical power in these studies increases sensitivity to confounding effects, necessitating careful consideration.
  • Unexplained variance and spurious correlations are significant challenges in analyzing large-scale datasets.

Purpose of the Study:

  • To identify and describe potential confounding variables in large cohort studies.
  • To assess the impact of these confounds on data and potential spurious correlations.
  • To provide guidance for researchers on managing confounding effects in data analysis.

Main Methods:

  • Systematic identification of potential confounding factors, including non-linear effects and interactions.
  • Development of methods to estimate the influence of each confound on the dataset.
  • Quantitative assessment of the extent to which confounds affect data and introduce spurious correlations.

Main Results:

  • Detailed descriptions of various potential confounds relevant to large cohort studies.
  • Estimation of the impact of identified confounds on data variance and observed associations.
  • Demonstration of how unaddressed confounds can lead to misleading conclusions and spurious correlations.

Conclusions:

  • Effective management of confounding variables is essential for robust findings in large cohort studies.
  • Researchers must carefully consider and address potential confounds, including complex interactions and non-linear effects.
  • This work provides a framework for understanding and mitigating confounding in large-scale biomedical research using resources like UK Biobank.