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Are power calculations useful? A multicentre neuroimaging study

Research output: Contribution to journalArticle

John Suckling, Julian Henty, Christine Ecker, Sean C Deoni, Michael V Lombardo, Simon Baron-Cohen, Peter Jezzard, Anna Barnes, Bhismadev Chakrabarti, Cinly Ooi, Meng-Chuan Lai, Steven C Williams, Declan G M Murphy, Edward Bullmore, for the MRC AIMS Consortium

Original languageEnglish
Pages (from-to)3569-3577
Number of pages9
JournalHuman Brain Mapping
Volume35
Issue number8
DOIs
Publication statusPublished - Aug 2014

King's Authors

Abstract

There are now many reports of imaging experiments with small cohorts of typical participants that precede large-scale, often multicentre studies of psychiatric and neurological disorders. Data from these calibration experiments are sufficient to make estimates of statistical power and predictions of sample size and minimum observable effect sizes. In this technical note, we suggest how previously reported voxel-based power calculations can support decision making in the design, execution and analysis of cross-sectional multicentre imaging studies. The choice of MRI acquisition sequence, distribution of recruitment across acquisition centres, and changes to the registration method applied during data analysis are considered as examples. The consequences of modification are explored in quantitative terms by assessing the impact on sample size for a fixed effect size and detectable effect size for a fixed sample size. The calibration experiment dataset used for illustration was a precursor to the now complete Medical Research Council Autism Imaging Multicentre Study (MRC-AIMS). Validation of the voxel-based power calculations is made by comparing the predicted values from the calibration experiment with those observed in MRC-AIMS. The effect of non-linear mappings during image registration to a standard stereotactic space on the prediction is explored with reference to the amount of local deformation. In summary, power calculations offer a validated, quantitative means of making informed choices on important factors that influence the outcome of studies that consume significant resources.

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