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Faster family-wise error control for neuroimaging with a parametric bootstrap
Simon N Vandekar1, Theodore D Satterthwaite2, Adon Rosen2
1Department of Biostatistics, Epidemiology, and Informatics, 423 Guardian Dr., University of Pennsylvania, Philadelphia PA, USA.
Biostatistics (Oxford, England)
|October 24, 2017
Summary
Neuroimaging studies need reliable statistical methods. A new parametric bootstrap procedure offers faster, accurate control of family-wise error rates (FWER) for multiple testing, improving brain imaging analysis.
Area of Science:
- Neuroimaging
- Statistical analysis
- Brain imaging
Background:
- Neuroimaging studies perform numerous statistical tests, necessitating robust control of family-wise error rates (FWER).
- Current FWER controlling procedures in neuroimaging often fail to maintain nominal levels, leading to unreliable results.
- Permutation testing reliably controls FWER but is computationally intensive for large datasets.
Purpose of the Study:
- To develop a computationally efficient parametric bootstrap procedure for controlling FWER in neuroimaging.
- To provide a reliable alternative to existing methods for multiple testing in brain imaging analyses.
- To enable faster and more accurate statistical inference in large-scale neuroimaging studies.
Main Methods:
- Developed a parametric bootstrap joint testing procedure operating directly on test statistics.
- Validated the procedure's FWER control through simulations in finite samples.
- Applied the method to region- and voxel-wise analyses for sex differences in cerebral blood flow development.
Main Results:
- The parametric bootstrap procedure reliably controls the family-wise error rate at the nominal level.
- This method significantly reduces computation time compared to permutation-based approaches.
- Simulations confirm the procedure's validity across various sample sizes relevant to neuroimaging.
Conclusions:
- The parametric bootstrap joint testing procedure offers a computationally efficient and statistically reliable solution for multiple testing in neuroimaging.
- This advancement facilitates more robust analyses of complex brain data, particularly in large cohorts.
- The method is suitable for investigating neurodevelopmental trajectories and other complex research questions in brain imaging.
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